https://cstwiki.wtb.tue.nl/api.php?action=feedcontributions&user=20182838&feedformat=atomControl Systems Technology Group - User contributions [en]2024-03-29T01:49:39ZUser contributionsMediaWiki 1.39.5https://cstwiki.wtb.tue.nl/index.php?title=PRE2020_4_Group4&diff=116850PRE2020 4 Group42021-05-27T07:17:21Z<p>20182838: /* Analysis Survey 1 */</p>
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<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Coco, The Computer Companion </font><br />
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==Group Description==<br />
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===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
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===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
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== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
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==Problem Statement and Objectives==<br />
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===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
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Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
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Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
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=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
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==USE: User, Society and Enterprise==<br />
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<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
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=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
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Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
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Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
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=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
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=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
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==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
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Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref> have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
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Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
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Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
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===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
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''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
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=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
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''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
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===Survey===<br />
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To find out more about the group of people that will be most interested in having a virtual companion, a survey will be conducted. This survey is meant to specify the target group and to get to know their preferences regarding the functionality of the virtual agent. The survey consists of 21 questions about Coco divided into the topics demographics, general computer work, productivity, tasks of a VA, privacy and interest. The link to the survey can be found [https://docs.google.com/forms/d/e/1FAIpQLSeAPqsZHCz95NWTv7W3PtrVc4D9m3TxM11IHSQ1SNEzvw_jdg/viewform?usp=sf_link here]. And the analysis of the survey can be found [[#Analysis Survey 1|here]].<br />
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===Planning===<br />
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[[File:0LAUK0 Planning Group 4 (Q4).PNG|1100 px|alt=Planning Group 4]]<br />
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==Research==<br />
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===State of the Art===<br />
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'''''Productivity agents'''''<br />
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As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
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Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
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With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
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'''''Companion agents'''''<br />
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When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. <ref name="Ta"/> investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements.<br />
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'''''Physical health agents'''''<br />
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Cambo, Avrahami, & Lee <ref name = "Cambo2017">Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021</ref> investigated the application “BreakSense” and concluded that the technology should let the user decide for themselves when to take a break. They discovered that in this way, the physical activity became part of their daily routine.<br />
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'''''Computer assistants'''''<br />
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Although these agents focus on specific tasks, there also exist personal computer assistants that are developed to help, for instance children, more generally with their daily activities. The study by Kessens et al. <ref name = "Kessens2009">Kessens, J. M., Neerincx, M. A., Looije, R., Kroes, M., & Bloothooft, G. (2009). Facial and vocal emotion expression of a personal computer assistant to engage, educate and motivate children. Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009. https://doi.org/10.1109/ACII.2009.5349582</ref> investigated such a computer assistant, namely the Philips iCat. This animated virtual robot can show varying emotional expressions and fulfilled the roles of both companion, educator and motivator.<br />
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===Related Literature===<br />
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'''''General'''''<br />
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A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]].<br />
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'''''Design of the Virtual Agent'''''<br />
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In week 3 a separate literature study has been conducted, specifically focused on the design aspect of the virtual agent. As a result of this study, it is planned to form a scientifically grounded recommendation for the design of the virtual agent, based on the direction of all papers taken together.<br />
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An overview of the collected data regarding the design and appearance of the agent can be found [[Related Literature Group 4, design|here]].<br />
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===Motivation for virtual agent===<br />
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This section contains the motivation for the decision to develop a virtual agent instead of a physical robot. Several important aspects of Coco will be discussed to reach this, including its emotional intelligence, embodiment and costs. '''Disclaimer: this is still a concept and will thus be extended later'''<br />
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'''''Emotional intelligence and communication'''''<br />
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One of the qualities of Coco should be that it is able to provide social support (objective 3). Of course, the question arises whether artificial agents can actually ‘understand’ feelings, or whether they are simply manipulating symbols. A famous thought experiment, called the Chinese Room, covers this topic <ref name = "Searle1980">Searle, J. R. (1980). Minds, Brains and Programs. Behavioral and Brain Sciences, 417-424. https://doi.org/10.1017/S0140525X00005756</ref>. Regardless of this, we would like Coco to show emotional intelligent behavior (whether it then actually has intelligence or not is a topic that is left for discussion). <br />
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Research has shown that artificial agents that show such intelligent behavior come across as more reliable than robots that lack this property (Fan, Scheutz, Lohani, Mccoy, & Stokes)<ref name = "Fan2017">Fan, L., Scheutz, M., Lohani, M., Mccoy, M., & Stokes, C. (2017). Do We Need Emotionally Intelligent Artificial Agents? First Results of Human Perceptions of Emotional Intelligence in Humans Compared to Robots. Springer International Publishing AG 2017, 129–141. https://doi.org/10.1007/978-3-319-67401-8_15</ref>. Moreover, robots that do not show intelligent behavior can be experienced as confusing or unpredictable to humans. As soon as behavior is misinterpreted by the users, emotional harm could be caused (Fan et al.)<ref name="Fan2017"/>. And emotional harm should of course be prevented from happening. <br />
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Another advantage of a virtual agent is the fact that it can process both spoken and written language. This option allows easier communication between Coco and its users, as no confusions can arise due to accents or bad pronunciations. Even though a physical robot can also process spoken language, it cannot process written language if not connected to a laptop or computer. Additionally, language processing software on a laptop can be easily updated. <br />
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A virtual agent with intelligent behavior would hence be preferred over a robot without intelligent behavior. <br />
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'''''Physical versus simulated embodiment'''''<br />
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Tanaka, Nakanishi & Ishiguro have found in an experiment that the physical embodiment of a robot enhanced the social telepresence, which is the feeling of face-to-face interaction while a person is not physically present (e.g. using video calls). However, they have also found that this social telepresence decreases the smoothness of speech of the participant(Tanaka, Nakanishi, & Ishiguro) <ref name = "Tanaka2014">Tanaka, K., Nakanishi, H., & Ishiguro, H. (2014). Comparing video, avatar, and robot mediated communication: Pros and cons of embodiment. Communications in Computer and Information Science, 96–110. https://doi.org/10.1007/978-3-662-44651-5_9</ref>.<br />
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According to Milne et al., the most important advantages of virtual agents are that they can be accessed at any time and that they do not need to be repaired (Milne, Luerssen, Lewis, Leibbrandt, & Powers) <ref name = "Milne2010">Milne, M., Luerssen, M. H., Lewis, T. W., Leibbrandt, R. E., Powers, D. M. W. (2010). Development of a virtual agent based social tutor for children with autism spectrum disorders.Proceedings of the International Joint Conference on Neural Networks, 1–9. https://doi.org/10.1109/IJCNN.2010.5596584</ref>.<br />
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The article of Lee, Jung, Kim & Kim investigates one of the most fundamental questions about social robots, namely whether physical embodiment adds value for good social interaction compared to social robots without embodiment. For instance, manufacturing of embodied robots is very expensive, and many technical difficulties can arise because of the many embedded sensors and motors. However, they did find that physically embodied agents may facilitate better social interaction because the physical robot can provide more affordance for social interaction. (affordance means that the properties of the robot show how it should be used). <br />
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Lee et al. also found that if an embodied robot had an “anthropomorphic-physical embodiment”, which means that human characteristics are assigned to it, expectations of the robot were very high. When it then did not have the ability to react to touch-input, the high expectations of the people dropped and they became frustrated and disappointed in the robot. This might be a general negative effect of physical embodiment (especially for lonely people that might want to use the robot as a real companion). <ref name = "Lee2006">Lee, K. M., Jung, Y., Kim, J., & Kim, S. R. (2006). Are physically embodied social agents better than disembodied social agents?: The effects of physical embodiment, tactile interaction, and people's loneliness in human–robot interaction. International Journal of Human Computer Studies, 962-973. https://doi.org/10.1016/j.ijhcs.2006.05.002</ref>.<br />
<br />
Wang & Rau have researched the effect on users’ responses of different types of social robots. They distinguished on two factors; embodiment and substrates. It has been found that people prefer that the embodiment matches the substrate. This means that they would prefer if a physical robot has the best effect in a physical substrate, but a virtual robot the best in a virtual substrate. This means that our design idea matches their research findings and that a virtual context (i.e. a computer/laptop) will best comply with a virtual embodiment <ref name = "Wang2019">Wang, B., & Rau, P. L. P. (2019). Influence of Embodiment and Substrate of Social Robots on Users’ Decision-Making and Attitude. International Journal of Social Robotics, 411-421. https://doi.org/10.1007/s12369-018-0510-7</ref>. A different paper agrees with Wang & Rau and also recommends that a virtual agent works best in a 2D environment, while a physical robot works best in a 3D environment <ref name = "Shinozawa2005">Shinozawa, K., Naya, F., Yamato, J., Kogure, K. (2005). Differences in effect of robot and screen agent recommendations on human decision-making. International Journal of Human Computer Studies, 267-279. https://doi.org/10.1007/s12369-018-0510-7</ref>.<br />
<br />
<br />
'''''Costs'''''<br />
<br />
A paper on design of social robots from Puehn et al. compares a low-cost social robot ‘Philos’ to commercial social robots. Their design idea Philos has a commercial value of $3,000 and the associated software is free. But the more widely known, higher quality robots, like the animal robot Paro and humanoid robot Nao, cost way more than that, namely $6,000 and $15,000 respectively<ref name = "Puehn2014">Puehn, C. G., Liu, T., Feng, Y., Hornfeck, K., & Lee, K. (2014). Design of a low-cost social robot: Towards personalized human-robot interaction. Lecture Notes in Computer Science, 704-713. https://doi.org/10.1007/978-3-319-07446-7_67</ref>.<br />
<br />
Also, Avramova et al. mentions that physical robots are expensive in their development and maintenance, whereas software can easily be updated. <ref name = "Avramova2017">Avramova, V., Yang, F., Li, C., Peters, C., & Skantze, G. (2017). A virtual poster presenter using mixed reality. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-67401-8_3</ref>.<br />
<br />
* https://www.webfx.com/internet-marketing/ai-pricing.html is an interesting website to see how much it will cost to develop AI software. Comparing e.g. chatbots, large data analysis systems and virtual assistants.<br />
<br />
<br />
'''''Functionalities'''''<br />
<br />
In order to improve its user's concentration and motivation (objective 1), Coco would benefit from permission to control apps or websites. Imagine, for instance, that someone has his/her social media open when trying to make exercises. Incoming messages could cause a lot of distraction, which could be prevented by (temporarily) blocking this website. Related to this, Coco could monitor your activity on the computer. In this way, it could provide summaries and recommendations on how you should invest your time. <br />
<br />
Coco could also be used to make an efficient and motivating planning for its users. Being able to link Coco to the user's schedule would be necessary for this task. <br />
<br />
Hence, some of the desired functionalities of Coco are thought to be implemented easier in a virtual agent than in a physical robot. <br />
<br />
<br />
'''''Conclusion'''''<br />
<br />
Although physical robots could be a better choice for certain applications, it seems that in the end for Coco, the costs and benefits of a virtual agent outweigh the benefits of a physical robot. One large advance is the fact that a virtual agent would make the technology better accessible to a large public. Although not everyone has the money to buy an advanced robot, many would be able to invest in a license to use the software. In this way, the objectives of Coco, can be achieved the best by developing a virtual agent.<br />
<br />
===Analysis Survey 1===<br />
<br />
The Stata code for the analysis of survey 1 can be found in Appendix A.<br />
<br />
<br />
'''''Interest'''''<br />
<br />
The first part of the analysis was quite exploratory: a general question was asked about whether people were interested in having a virtual agent as described in the survey. The research question for this first part was formulated in the following way: ''what are the differences in interests and preferences for people with different demographical characteristics?''<br />
<br />
When it comes to interest in the VA, almost half of the respondents expressed their doubts. They did not immediately reject the idea, but thought their choice depended on for example the situation and the functionality of the agent. When looking at age, people that were not sure were distributed very evenly. A Wilcoxon rank-sum test however shows a significant difference in age for people who say they would be interested and people who say they are not (p=0.001). It seems younger people are more open to the idea of having a virtual agent.<br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Scatter_plot_of_age_by_interest.png|400px|thumb|left|Figure X: Scatter plot of interest by age]]<br />
[[File:Bar_chart_interest_age.png|400px|thumb|left|Figure X: Bar chart of interest by age]]<br />
</div><br />
<br />
<br />
Looking at gender, both males and females are interested for around 25% of the time. Even though the figure below suggests that females more often consider the idea of having a virtual agent, this difference is not significant (X^2, p=0.07).<br />
<br />
<div style="display:flex;flex-direction:row;"> [[File:Interest_gender.png|400px|thumb|left|Figure X: Bar chart of interest by gender]] </div><br />
<br />
<br />
From Figure 4 we can also conclude that people who use their laptop around 7-9 hours a day are most open to the idea of a virtual agent. Least interested are people who use their computer less than 2 hours a day, or more than 9 hours. For both groups respondents indicated that they do not need such a virtual agent.<br />
<br />
<div style="display:flex;flex-direction:row;"> [[File:Interest_daily_working_hours.png|400px|thumb|left|Figure X: Bar chart of interest by working hours]] </div><br />
<br />
<br />
<br />
'''''Corona impact on productivity and health'''''<br />
<br />
The second part of the analysis was about the influence of corona on productivity and health. Several research questions have been formulated:<br />
<br />
''Do people feel that their'' '''productivity, including concentration and motivation''' '', has decreased because of the corona virus?''<br />
<br />
''Do people feel that their'' '''physical health''' ''has decreased because of the corona virus?''<br />
<br />
''Do people feel that their'' '''mental health''' ''has decreased because of the corona virus?'' <br />
<br />
<br />
Considering the effect of COVID-19 on people's perception of their work situation, several observations can be made. Generally, mental health, loneliness and productivity are the three areas that have been negatively impacted by the corona crisis the most. <br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Bar_chart_breaks.png|300px|thumb|left|Figure X: Bar chart of breaks]]<br />
[[File:Bar_chart_concentration.png|300px|thumb|left|Figure X: Bar chart of concentration]]<br />
[[File:Bar_chart_loneliness.png|300px|thumb|left|Figure X: Bar chart of loneliness]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Bar_chart_mental_health.png|300px|thumb|left|Figure X: Bar chart of mental health]]<br />
[[File:Bar_chart_motivation.png|300px|thumb|left|Figure X: Bar chart of motivation]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Bar_chart_physical_health.png|300px|thumb|left|Figure X: Bar chart of physical health]]<br />
[[File:Bar_chart_productivity.png|300px|thumb|left|Figure X: Bar chart of productivity]]<br />
</div><br />
<br />
<br />
Since this questionnaire was also made to determine the user group that would need the help of a VA the most, the following matters will also be analyzed based on age. Generally, people older than 25 take less breaks since COVID-19, and younger people tend to take more breaks. Younger people generally think their concentration has decreased since COVID-19 and on the other hand, people older than 25 generally say their concentration has increased. However, both groups state their productivity has decreased. Although both groups state that their mental health, physical health and feeling of loneliness have decreased since COVID-19, the younger user group indicates a greater negative impact. <br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Bar_chart_breaks_age.png|300px|thumb|left|Figure X: Bar chart of breaks by age]]<br />
[[File:Bar_chart_concentration_age.png|300px|thumb|left|Figure X: Bar chart of concentration by age]]<br />
[[File:Bar_chart_loneliness_age.png|300px|thumb|left|Figure X: Bar chart of loneliness by age]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Bar_chart_mental_health_age.png|300px|thumb|left|Figure X: Bar chart of mental health by age]]<br />
[[File:Bar_chart_motivation_age.png|300px|thumb|left|Figure X: Bar chart of motivation by age]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Bar_chart_physical_health_age.png|300px|thumb|left|Figure X: Bar chart of physical health by age]]<br />
[[File:Bar_chart_productivity_age.png|300px|thumb|left|Figure X: Bar chart of productivity by age]]<br />
</div><br />
<br />
<br />
In Table 1 the means of the two groups are shown to clarify the above made statements. When the mean is smaller than 0, there is on average a decrease and if this impact is bigger than 0, there is on average an increase. There is a significant difference between the two groups if the p-value is smaller than 0.05. All this information suggests that it might be best to focus on people of 25 or younger for this research and the development of the computer companion. <br />
<br />
''Table 1: The means for several corona impact measurements and the differences between the different age groups.''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Aspects !! Mean <= 25 !! Mean > 25 !! p-value<br />
|-<br />
| Taking breaks || 0.19 || -0.49 || 0.0001<br />
|-<br />
| Concentration || -0.30 || 2.9 || 0.0004 <br />
|-<br />
| Feeling of loneliness || -0.40 || -0.36 || 0.7745 <br />
|-<br />
| Mental health || -0.61 || -0.28 || 0.0117 <br />
|-<br />
| Motivation || 0.37 || 0.19 || 0.1868 <br />
|-<br />
| Physical health || -0.74 || -0.09 || 0.0001 <br />
|-<br />
| Productivity || -0.47 || -0.31 || 0.2165 <br />
|-<br />
|}<br />
<br />
<br />
To summarize, considering all respondents the productivity, physical health and mental health have decreased. Generally, concentration has not been affected by COVID-19. Respondents even indicated an increase in motivation. Once the respondents were grouped by age, a clear difference in responses could be seen. The people of 25 years old and younger indicated a slight decrease in concentration, while people over 25 indicated a slight increase in concentration. Even though both groups indicated a decrease for most other cases, people of 25 years old and younger indicated a higher decrease in mental health, physical health and productivity. For both groups motivation increased, but there was a bigger increase indicated for the younger group.<br />
<br />
<br />
Regarding the open question 'Are there other activities than the ones mentioned above that you often experience to negatively impact your productivity?', around 20 people mentioned that phone calls, applications like Netflix, and message platforms like Microsoft Teams and WhatsApp disturb their productivity. Besides that, 15 participants mentioned that their family and other people in their household negatively impact productivity. Children for example ask for a lot of attention when parents are working at home, and they need to be helped with homeschooling (unfortunately, Coco will not be able to solve this problem). Besides these two main impact factors, some participants also mentioned no breaks, lack of physical contact, the mailman, spontaneous household chores, unstructured schedules, bad sleep quality, a non-stimulating workplace, ambient noise, and the monotony of the day as things that negatively impact productivity.<br />
<br />
<br />
'''''Helpful tasks for a virtual agent'''''<br />
<br />
The third part of the analysis was about the tasks of a virtual agent users would see as useful. The research question for this part is: ''Which tasks are most important for a computer companion to possess?''<br />
<br />
Sixteen different variables are used to see what users prefer as tasks for a virtual agent. For every variable, the respondents could choose five options from a Likert scale varying between ‘Not useful at all’ to ‘Very useful’. From the histograms it can be seen that ‘Help avoiding distractions by blocking applications’, ‘Help to split up daunting tasks’, ‘Encourage taking breaks’, ‘Encourage physical activity during breaks’, ‘Help increase productivity with your favorite concentration/focus application’, ‘Reflect on work at the end of the day’, ‘Help schedule tasks in your preferred scheduling application’, ‘Help you to focus on a currently scheduled task by getting you back from distractions’, ‘Providing reminders of your schedule’, ‘Make task switching smoother’ and ‘Help you to work according to your preferred time-scheduling technique’ all seem to lean to the right side, indicating that they are thought to be more useful. <br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Hist_1.png|300px|thumb|left|Figure X: Bar chart of interest in blocking applications]]<br />
[[File:Hist_2.png|300px|thumb|left|Figure X: Bar chart of interest in splitting up tasks]]<br />
[[File:Hist_3.png|300px|thumb|left|Figure X: Bar chart of interest in encouraging to take breaks]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Hist_4.png|300px|thumb|left|Figure X: Bar chart of interest in encouraging to be physically active]]<br />
[[File:Hist_5.png|300px|thumb|left|Figure X: Bar chart of interest in providing physical exercises]]<br />
[[File:Hist_6.png|300px|thumb|left|Figure X: Bar chart of interest in connecting with focus application]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Hist_7.png|300px|thumb|left|Figure X: Bar chart of interest in providing motivation]]<br />
[[File:Hist_8.png|300px|thumb|left|Figure X: Bar chart of interest in reflection at the end of the day]]<br />
[[File:Hist_9.png|300px|thumb|left|Figure X: Bar chart of interest in connecting with scheduling application]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Hist_10.png|300px|thumb|left|Figure X: Bar chart of interest in providing small talk]]<br />
[[File:Hist_11.png|300px|thumb|left|Figure X: Bar chart of interest in sending auditory reminders]]<br />
[[File:Hist_12.png|300px|thumb|left|Figure X: Bar chart of interest in helping focus on current tasks]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Hist_13.png|300px|thumb|left|Figure X: Bar chart of interest in connecting with applications to define tasks]]<br />
[[File:Hist_14.png|300px|thumb|left|Figure X: Bar chart of interest in providing reminders]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Hist_15.png|300px|thumb|left|Figure X: Bar chart of interest in making task switching smoother]]<br />
[[File:Hist_16.png|300px|thumb|left|Figure X: Bar chart of interest in helping work to scheduling technique]]<br />
</div><br />
<br />
<br />
These histograms can be supported by skewness values. A skewness value below zero means that the data is more distributed to the right, while a positive value means that the data is more distributed to the left. We try to find a negative skewness here to confirm what is mentioned above. As can be seen in table 2, all the skewness values of above topics are indeed negative, however, some are larger skewed than others and thus more often chosen to be useful. In conclusion, the most useful tasks (based on negative skewness values between –0.57 to –0.80) are, from highest to lowest; ‘Help avoiding distractions by blocking applications’, ‘Help increase productivity with your favorite concentration/focus application’, ‘Help schedule tasks in your preferred scheduling application’. All other variables have skewness values lower than -0.39 and are thus less helpful than these mentioned above.<br />
<br />
''Table 2: The skewness values for several variables of what could be helpful tasks of a VA.''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Variables !! Skewness<br />
|-<br />
| '''Help avoiding distractions by blocking applications''' || '''-0.7914'''<br />
|-<br />
| Help to split up daunting tasks || -0.3372<br />
|-<br />
| Encourage taking breaks || -0.1631<br />
|-<br />
| Encourage physical activity during breaks || -0.1469<br />
|-<br />
| '''Help increase productivity with your favorite concentration/focus application''' || '''-0.7717'''<br />
|-<br />
| Reflect on work at the end of the day || -0.3852<br />
|-<br />
| '''Help schedule tasks in your preferred scheduling application''' || '''-0.5737'''<br />
|-<br />
| Help you to focus on a currently scheduled task by getting you back from distractions || -0.2038<br />
|-<br />
| Providing reminders of your schedule || -0.2560<br />
|-<br />
| Make task switching smoother || -0.3755<br />
|-<br />
| Help you to work according to your preferred time-scheduling technique || -0.3555<br />
|-<br />
|}<br />
<br />
<br />
To check whether it was a good decision to focus on respondents under 25 years, we have made bar charts of the three most important variables ,grouped by age. The results can be found below and it can be seen that this is indeed a good choice. More younger people have interest in the three most helpful tasks and older people are doubtful or even sure that it will not be helpful. <br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Avoiddistractions_vs_age.png|300px|thumb|left|Bar chart of interest in blocking applications by age]]<br />
[[File:Focusapp_vs_age.png|300px|thumb|left|Bar chart of interest in connecting with focus application by age]]<br />
[[File:Scheduling_vs_age.png|300px|thumb|left|Bar chart of interest in connecting with scheduling application by age]]<br />
</div><br />
<br />
Furthermore, it can be checked using t-tests (or if not allowed; rank sum tests) whether there is a significant difference between age groups for the variables. We start with performing Shapiro-Wilk and Skewness/Kurtosis tests to see if the data is normally distributed. Both p-values should be larger than 0.05 here to not reject the null hypothesis (‘The data is normally distributed’). If this is the case, we may perform a t-test and otherwise we may only perform a non-parametrical test. We will only look for a significant difference in the three most helpful tasks, according to above. Table 3 shows that all variables have a corresponding p-value smaller than 0.05 and thus there is a significance difference between the two age groups and those helpful tasks.<br />
<br />
''Table 3: The corresponding significance values for the three main helpful tasks variables.''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Variables !! Significance value<br />
|-<br />
| Help avoiding distractions by blocking applications || 0.0287<br />
|-<br />
| Help increase productivity with your favorite concentration/focus application || 0.0023<br />
|-<br />
| Help schedule tasks in your preferred scheduling application || 0.0001<br />
|-<br />
|}<br />
<br />
<br />
Regarding the open question 'Are there any tasks which aren't mentioned in the list above that you would like a virtual agent to be able to do?', around 20 people have given useful suggestions for extra features of Coco or have confirmed that already existing features will be helpful. Quite a lot of people mentioned that Coco should be connected to streaming services for music or meditations. Furthermore, some people mentioned that they would like it if Coco were connected to their coffee machine or alarm.<br />
It is also mentioned a lot that Coco should suggest a planning for the day based on tasks and order their email on priority. Moreover, it has also been mentioned a few times that Coco should be able to block certain applications and signal to colleagues whether they are busy or not, some people add that this should happen automatically. These are ideas we already had, and it is thus great to hear that the respondents think the same.<br />
In the survey's additional comments, two people gave additional suggestions for functionality. The first mentioned that it would be useful if you can decide to turn of Coco for a while, especially during the weekend this might be useful. Another person worried that Coco should not make them lose their concentration: “The last thing I want is another application that sends me unsolicited and annoying notifications.”<br />
<br />
In conclusion, our research question ''Which tasks are most important for a computer companion to possess?'' will be answered with the three best options. Coco should be able to help avoid distractions by blocking applications, help increase productivity with users' favorite concentration/focus application, and finally help schedule tasks in users' preferred scheduling application.<br />
<br />
<br />
'''''Appearance'''''<br />
<br />
In the survey, participants had to indicate their preferences for the appearance of Coco. There were three options (human, robot, animal) which they could rank from 1 (most favorable) to 3 (least favorable). The frequency of option 1, 2, or 3 was then encoded for each appearance into three variables (“human_appearance", “robot_appearance", and “animal_appearance"). These results are shown in table 4. <br />
<br />
''Table 4: Percentages of ranking human, robot, and animal appearance.''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! !! Human !! Robot !! Animal<br />
|-<br />
| '''Ranking 1''' || 35.71% || 34.52% || 23.21% <br />
|-<br />
| '''Ranking 2''' || 27.98% || 27.38% || 32.74% <br />
|-<br />
| '''Ranking 3''' || 29.17% || 29.17% || 34.52% <br />
|-<br />
|}<br />
<br />
As becomes clear from table 4, people like a human or a robot almost exactly the same. An animal, on the other hand, is somewhat less favorable, as it is chosen less often as first option and more often as second or third option. It would be useful to focus more on the preferences between humans and robots in the second survey. <br />
<br />
To check for any different preferences between younger (< 25 years) and older (>= 25 years) participants, the histograms shown in Figure X, Figure X, and Figure X are analyzed. Looking at these plots, some differences seem to exist. To check whether these differences are significant, a Chi-square test has been performed for each variable “human_appearance", “robot_appearance", and “animal_appearance". The alpha-value has been set at its default-value of 0.05 and the p-values are 0.000 (human), 0.096 (robot), and 0.012 (animal). Hence, indeed a significant difference is found in the preferences for a human or animal appearance between younger and older people. Older people think a human appearance is most favorable, while younger people think the opposite. Also, older people prefer an animal appearance less than younger people. However, the differences in case of a robot appear to be insignificant. <br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Bar_chart_breaks.png|300px|thumb|left|Figure X: Bar chart of breaks]]<br />
[[File:Bar_chart_concentration.png|300px|thumb|left|Figure X: Bar chart of concentration]]<br />
[[File:Bar_chart_loneliness.png|300px|thumb|left|Figure X: Bar chart of loneliness]]<br />
</div><br />
<br />
Regarding the open question 'Would you rather have a different look for your computer buddy than indicated above?', most people did not have any additional comments about the appearance of Coco, however, some trends could be seen amongst the ones that did gave some input.<br />
First of all, five people mentioned that they would like the option to customize the appearance of Coco. They mention, for instance, that they would like to choose a figure on one day, and another one on the next day. One person even mentioned the difference between work and leisure. Next to this, they would like the option to adapt the figures themselves. Of course, this would require more (difficult) options in the software.<br />
Furthermore, six people mentioned that Coco did not even need an appearance. Something more simplistic, for example such as Siri (mentioned by a couple), would be sufficient in their opinion.<br />
Also, eighteen people gave additional options that could be designed. These ranged from “more natural elements” and “a Disney character” to “a self-chosen picture”.<br />
Lastly, two persons suggested to change our proposed appearances. One mentioned to make them “somewhat older", while the other would like to see some more details.<br />
<br />
<br />
'''''Distractions that negatively impact productivity'''''<br />
<br />
This section will try to answer the following research questions. The first question is, ''is there a significant difference in the extent to which the two groups (workers, age > 25 and students age <= 25) experience the negative impact on their productivity?'' If there is indeed a large difference between the two groups, it might be important to narrow down our user group. And the second research question is ''what types of distractions have the most influence on productivity?'' This question is asked so we can prioritize the functions that Coco needs to have. <br />
<br />
In the survey, we asked our responders the following; "How often do you get distracted from your work by the following activities?". These activities are the internet, lectures, news, personal mail, scheduled meetings, online shopping, social media, unplanned meetings and work mail. To see what types of distractors have the most influence on productivity, we calculated the mean response for both students and workers. The mean response was based on the different answers the respondents could give to the question, to which they could answer the following: never, less than once a month, a few times a month, a few times a week, daily, 2 or 3 times a day, more than three times a day, in which 1 means never and 7 means more than three times a day. The mean amount of distraction can be seen for both age groups per distraction factor in the '''table ....''' below. The significance values are also shown in the table because the effect of the type of distractor can differ significantly per age group. This statistically significant difference is computed with the Wilcoxon rank-sum test. <br />
<br />
''Table ...: Distraction factors''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Distraction factor !! Mean for age <= 25 !! Mean for age > 25 !! Significance value<br />
|-<br />
| ''Internet'' || 4.97 || 3.22 || p = 0.0000 <br />
|-<br />
| ''Lecture'' || 3.6 || 1.92 || p = 0.0000 <br />
|-<br />
| ''News'' || 3.71 || 3.08 || p = 0.1617 <br />
|-<br />
| ''Personal mail'' || 3.86 || 3.36 || p = 0.6041 <br />
|-<br />
| ''Scheduled meetings'' || 3.6 || 3.46 || p = 0.2918 <br />
|-<br />
| ''Shopping'' || 3.03 || 1.82 || p = 0.0000 <br />
|-<br />
| ''Social media'' || 5.4 || 2.9 || p = 0.0000 <br />
|-<br />
| ''Unplanned meetings'' || 2.8 || 3.58 || p = 0.0002 <br />
|-<br />
| ''Work mail'' || 4.06 || 4.64 || p = 0.0005 <br />
|}<br />
<br />
As you can see in '''table .....''', the mean distraction is higher for every sort of distraction type except for work-related mail. This highlights that students’ (people aged <= 25) productivity is more often affected by these distractions than the productivity of working people. To give an example, a mean of 4.97 (internet) for people aged 25 or lower means that they are distracted 'daily' on average. The differences between the two groups are significant for internet, lectures, shopping, social media, unplanned meetings and for work mail. This table also informs us about what types of distractors have the most influence on both groups. For clarity,''' table .....''' below shows the rank order of the amount of distraction per age group. <br />
<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Rank !! Students !! Workers<br />
|-<br />
| 1. || Social media || Work mail <br />
|-<br />
| 2. || Internet || Unplanned meetings<br />
|-<br />
| 3. || Work mail || Scheduled meetings<br />
|-<br />
| 4. || Personal mail|| Personal meetings<br />
|-<br />
| 5. || News || Internet<br />
|-<br />
| rowspan="2" | 6. || Lectures & Scheduled meetings || News<br />
|-<br />
| Scheduled meetings<br />
|-<br />
| 7. || Shopping || Social media <br />
|-<br />
| 8. || Unplanned meetings || Lectures<br />
|-<br />
| 9. || || Shopping<br />
|}<br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Graph_Internet_Distraction.jpg|400px|thumb|left|Frequency of distraction (internet), grouped by age]]<br />
[[File:Graph_Lectures_Distraction.jpg|400px|thumb|left|Frequency of distraction (lectures), grouped by age]]<br />
[[File:Graph_News_Distraction.jpg|400px|thumb|left|Frequency of distraction (news), grouped by age]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Graph_Personal_Mail_Distraction.jpg|400px|thumb|left|Frequency of distraction (personal mail), grouped by age]]<br />
[[File:Graph_Scheduled_Meetings_Distraction.jpg|400px|thumb|left|Frequency of distraction (scheduled meetings), grouped by age]]<br />
[[File:Graph_Shopping_Distraction.jpg|400px|thumb|left|Frequency of distraction (shopping), grouped by age]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Graph_Social_Media_Distraction.jpg|400px|thumb|left|Frequency of distraction (social media), grouped by age]]<br />
[[File:Graph_Unplanned_Meetings_Distraction.jpg|400px|thumb|left|Frequency of distraction (unplanned meetings), grouped by age]]<br />
[[File:Graph_Workmail_Distraction.jpg|400px|thumb|left|Frequency of distraction (work mail), grouped by age]]<br />
</div><br />
<br />
<br />
To summarize, there is indeed a significant difference between the two age groups and the times that they get distracted by different factors, so this again confirms that it is a good idea to split up the groups and investigate them separately. From both the graphs and the statistical analysis you can see that students are more often distracted by multiple factors than workers. For these students, the two largest distractor factors are, by far, social media and the internet. These factors distract students two or three times a day and daily (respectively).<br />
<br />
<br />
'''''Money'''''<br />
<br />
There were quite some different answers to the question what people were willing to pay for Coco. There were three main categories in which people answered the question. One was paying per certain period, for example a monthly or yearly fee. The second category of people wanted Coco to be a one-time buy, and the third category consists of other answers.<br />
<br />
<br />
''Payments per period''<br />
<br />
Around 17 people said that they would like to pay monthly or yearly, however the amount that they were willing to pay varied widely. Most of these people wanted to pay somewhere between 5 – 20 Euros per month.<br />
<br />
<br />
''One-time buy''<br />
<br />
The amount of money that people were willing to pay in a one-time buy varied widely again, between 2 Euros to 500 Euro's. However, most people responded with 10 Euro's and 50 Euro's. The average price that people were willing to pay was approximately 68 Euro's.<br />
<br />
<br />
''Other''<br />
<br />
17 people mentioned that they did not want to pay for the software and there were also some additional ideas posed by the respondents, for example, 9 people mentioned that 'work should pay for this'. Besides that, some people said that they wanted a free tryout first to see the functionalities before they want to pay for it. If results are good, they are willing to pay around 10 Euros per month or around 100 Euros per year. Moreover, a few people commented that it depends on the type of license; a one-time purchase, or pay on monthly base or yearly base? This was an unnecessary dubiety on our side.<br />
<br />
<br />
'''''General conclusion of analysis 1'''''<br />
<br />
As indicated in the second part of the analysis 'Corona impact on productivity and health', it might be best to focus on people of 25 years old or younger. Their productivity, concentration, mental health, and physical health have decreased the most since COVID-19. Coco would be best of help for the users if it could aid them during their work considering these aspects.<br />
<br />
Because almost all the frequencies of the distraction types are higher for students than for workers, it can be concluded that Coco would be more useful for students. The two things that distract students the most compared to the other distraction factors are social media, and the internet. Therefore it is of great importance that Coco will be able to prevent the students from getting distracted by at least these two factors.<br />
<br />
==References==<br />
<br />
<references/><br />
<br />
<br />
<br />
<br />
==Appendix==<br />
===Appendix A - Stata code analysis survey 1===<br />
<br />
Here the Stata code for the analysis of survey 1 can be found.<br />
<br />
[[File:Wiki_appendix_1.png|1500px]]<br />
[[File:Wiki_appendix_2.png|1500px]]<br />
[[File:Wiki_appendix_3.png|1500px]]<br />
[[File:Wiki_appendix_4.png|1500px]]<br />
[[File:Wiki_appendix_5.png|1500px]]<br />
[[File:Wiki_appendix_6.png|1500px]]<br />
[[File:Wiki_appendix_7.png|1500px]]<br />
[[File:Wiki_appendix_8.png|1500px]]<br />
[[File:Wiki_appendix_9.png|1500px]]<br />
[[File:Wiki_appendix_10.png|1500px]]<br />
[[File:Wiki_appendix_11.png|1500px]]<br />
[[File:Wiki_appendix_12.png|1500px]]<br />
[[File:Wiki_appendix_13.png|1500px]]</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=PRE2020_4_Group4&diff=116849PRE2020 4 Group42021-05-27T07:17:10Z<p>20182838: /* Analysis Survey 1 */</p>
<hr />
<div><div style="font-family: 'Calibri'; font-size: 15px; line-height: 1.5; max-width: 1100px; word-wrap: break-word; color: #333; font-weight: 400; margin-left: auto; margin-right: auto; padding: 50px; background-color: white; padding-top: 25px;"><br />
<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Coco, The Computer Companion </font><br />
<br />
==Group Description==<br />
<br />
===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
<br />
===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
<br />
== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
<br />
<br />
<br />
==Problem Statement and Objectives==<br />
<br />
===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
<br />
Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
<br />
=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
<br />
<br />
<br />
==USE: User, Society and Enterprise==<br />
<br />
<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
<br />
=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
<br />
Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
<br />
Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
<br />
=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
<br />
=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
<br />
==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
<br />
Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref> have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
<br />
Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
<br />
Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
<br />
===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
<br />
''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
<br />
<br />
=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
<br />
''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
<br />
<br />
===Survey===<br />
<br />
To find out more about the group of people that will be most interested in having a virtual companion, a survey will be conducted. This survey is meant to specify the target group and to get to know their preferences regarding the functionality of the virtual agent. The survey consists of 21 questions about Coco divided into the topics demographics, general computer work, productivity, tasks of a VA, privacy and interest. The link to the survey can be found [https://docs.google.com/forms/d/e/1FAIpQLSeAPqsZHCz95NWTv7W3PtrVc4D9m3TxM11IHSQ1SNEzvw_jdg/viewform?usp=sf_link here]. And the analysis of the survey can be found [[#Analysis Survey 1|here]].<br />
<br />
===Planning===<br />
<br />
[[File:0LAUK0 Planning Group 4 (Q4).PNG|1100 px|alt=Planning Group 4]]<br />
<br />
==Research==<br />
<br />
===State of the Art===<br />
<br />
<br />
'''''Productivity agents'''''<br />
<br />
As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
<br />
Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
<br />
With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
<br />
<br />
'''''Companion agents'''''<br />
<br />
When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. <ref name="Ta"/> investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements.<br />
<br />
<br />
'''''Physical health agents'''''<br />
<br />
Cambo, Avrahami, & Lee <ref name = "Cambo2017">Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021</ref> investigated the application “BreakSense” and concluded that the technology should let the user decide for themselves when to take a break. They discovered that in this way, the physical activity became part of their daily routine.<br />
<br />
<br />
'''''Computer assistants'''''<br />
<br />
Although these agents focus on specific tasks, there also exist personal computer assistants that are developed to help, for instance children, more generally with their daily activities. The study by Kessens et al. <ref name = "Kessens2009">Kessens, J. M., Neerincx, M. A., Looije, R., Kroes, M., & Bloothooft, G. (2009). Facial and vocal emotion expression of a personal computer assistant to engage, educate and motivate children. Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009. https://doi.org/10.1109/ACII.2009.5349582</ref> investigated such a computer assistant, namely the Philips iCat. This animated virtual robot can show varying emotional expressions and fulfilled the roles of both companion, educator and motivator.<br />
<br />
===Related Literature===<br />
<br />
'''''General'''''<br />
<br />
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]].<br />
<br />
<br />
'''''Design of the Virtual Agent'''''<br />
<br />
In week 3 a separate literature study has been conducted, specifically focused on the design aspect of the virtual agent. As a result of this study, it is planned to form a scientifically grounded recommendation for the design of the virtual agent, based on the direction of all papers taken together.<br />
<br />
An overview of the collected data regarding the design and appearance of the agent can be found [[Related Literature Group 4, design|here]].<br />
<br />
===Motivation for virtual agent===<br />
<br />
This section contains the motivation for the decision to develop a virtual agent instead of a physical robot. Several important aspects of Coco will be discussed to reach this, including its emotional intelligence, embodiment and costs. '''Disclaimer: this is still a concept and will thus be extended later'''<br />
<br />
<br />
'''''Emotional intelligence and communication'''''<br />
<br />
One of the qualities of Coco should be that it is able to provide social support (objective 3). Of course, the question arises whether artificial agents can actually ‘understand’ feelings, or whether they are simply manipulating symbols. A famous thought experiment, called the Chinese Room, covers this topic <ref name = "Searle1980">Searle, J. R. (1980). Minds, Brains and Programs. Behavioral and Brain Sciences, 417-424. https://doi.org/10.1017/S0140525X00005756</ref>. Regardless of this, we would like Coco to show emotional intelligent behavior (whether it then actually has intelligence or not is a topic that is left for discussion). <br />
<br />
Research has shown that artificial agents that show such intelligent behavior come across as more reliable than robots that lack this property (Fan, Scheutz, Lohani, Mccoy, & Stokes)<ref name = "Fan2017">Fan, L., Scheutz, M., Lohani, M., Mccoy, M., & Stokes, C. (2017). Do We Need Emotionally Intelligent Artificial Agents? First Results of Human Perceptions of Emotional Intelligence in Humans Compared to Robots. Springer International Publishing AG 2017, 129–141. https://doi.org/10.1007/978-3-319-67401-8_15</ref>. Moreover, robots that do not show intelligent behavior can be experienced as confusing or unpredictable to humans. As soon as behavior is misinterpreted by the users, emotional harm could be caused (Fan et al.)<ref name="Fan2017"/>. And emotional harm should of course be prevented from happening. <br />
<br />
Another advantage of a virtual agent is the fact that it can process both spoken and written language. This option allows easier communication between Coco and its users, as no confusions can arise due to accents or bad pronunciations. Even though a physical robot can also process spoken language, it cannot process written language if not connected to a laptop or computer. Additionally, language processing software on a laptop can be easily updated. <br />
<br />
A virtual agent with intelligent behavior would hence be preferred over a robot without intelligent behavior. <br />
<br />
<br />
'''''Physical versus simulated embodiment'''''<br />
<br />
Tanaka, Nakanishi & Ishiguro have found in an experiment that the physical embodiment of a robot enhanced the social telepresence, which is the feeling of face-to-face interaction while a person is not physically present (e.g. using video calls). However, they have also found that this social telepresence decreases the smoothness of speech of the participant(Tanaka, Nakanishi, & Ishiguro) <ref name = "Tanaka2014">Tanaka, K., Nakanishi, H., & Ishiguro, H. (2014). Comparing video, avatar, and robot mediated communication: Pros and cons of embodiment. Communications in Computer and Information Science, 96–110. https://doi.org/10.1007/978-3-662-44651-5_9</ref>.<br />
<br />
According to Milne et al., the most important advantages of virtual agents are that they can be accessed at any time and that they do not need to be repaired (Milne, Luerssen, Lewis, Leibbrandt, & Powers) <ref name = "Milne2010">Milne, M., Luerssen, M. H., Lewis, T. W., Leibbrandt, R. E., Powers, D. M. W. (2010). Development of a virtual agent based social tutor for children with autism spectrum disorders.Proceedings of the International Joint Conference on Neural Networks, 1–9. https://doi.org/10.1109/IJCNN.2010.5596584</ref>.<br />
<br />
The article of Lee, Jung, Kim & Kim investigates one of the most fundamental questions about social robots, namely whether physical embodiment adds value for good social interaction compared to social robots without embodiment. For instance, manufacturing of embodied robots is very expensive, and many technical difficulties can arise because of the many embedded sensors and motors. However, they did find that physically embodied agents may facilitate better social interaction because the physical robot can provide more affordance for social interaction. (affordance means that the properties of the robot show how it should be used). <br />
<br />
Lee et al. also found that if an embodied robot had an “anthropomorphic-physical embodiment”, which means that human characteristics are assigned to it, expectations of the robot were very high. When it then did not have the ability to react to touch-input, the high expectations of the people dropped and they became frustrated and disappointed in the robot. This might be a general negative effect of physical embodiment (especially for lonely people that might want to use the robot as a real companion). <ref name = "Lee2006">Lee, K. M., Jung, Y., Kim, J., & Kim, S. R. (2006). Are physically embodied social agents better than disembodied social agents?: The effects of physical embodiment, tactile interaction, and people's loneliness in human–robot interaction. International Journal of Human Computer Studies, 962-973. https://doi.org/10.1016/j.ijhcs.2006.05.002</ref>.<br />
<br />
Wang & Rau have researched the effect on users’ responses of different types of social robots. They distinguished on two factors; embodiment and substrates. It has been found that people prefer that the embodiment matches the substrate. This means that they would prefer if a physical robot has the best effect in a physical substrate, but a virtual robot the best in a virtual substrate. This means that our design idea matches their research findings and that a virtual context (i.e. a computer/laptop) will best comply with a virtual embodiment <ref name = "Wang2019">Wang, B., & Rau, P. L. P. (2019). Influence of Embodiment and Substrate of Social Robots on Users’ Decision-Making and Attitude. International Journal of Social Robotics, 411-421. https://doi.org/10.1007/s12369-018-0510-7</ref>. A different paper agrees with Wang & Rau and also recommends that a virtual agent works best in a 2D environment, while a physical robot works best in a 3D environment <ref name = "Shinozawa2005">Shinozawa, K., Naya, F., Yamato, J., Kogure, K. (2005). Differences in effect of robot and screen agent recommendations on human decision-making. International Journal of Human Computer Studies, 267-279. https://doi.org/10.1007/s12369-018-0510-7</ref>.<br />
<br />
<br />
'''''Costs'''''<br />
<br />
A paper on design of social robots from Puehn et al. compares a low-cost social robot ‘Philos’ to commercial social robots. Their design idea Philos has a commercial value of $3,000 and the associated software is free. But the more widely known, higher quality robots, like the animal robot Paro and humanoid robot Nao, cost way more than that, namely $6,000 and $15,000 respectively<ref name = "Puehn2014">Puehn, C. G., Liu, T., Feng, Y., Hornfeck, K., & Lee, K. (2014). Design of a low-cost social robot: Towards personalized human-robot interaction. Lecture Notes in Computer Science, 704-713. https://doi.org/10.1007/978-3-319-07446-7_67</ref>.<br />
<br />
Also, Avramova et al. mentions that physical robots are expensive in their development and maintenance, whereas software can easily be updated. <ref name = "Avramova2017">Avramova, V., Yang, F., Li, C., Peters, C., & Skantze, G. (2017). A virtual poster presenter using mixed reality. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-67401-8_3</ref>.<br />
<br />
* https://www.webfx.com/internet-marketing/ai-pricing.html is an interesting website to see how much it will cost to develop AI software. Comparing e.g. chatbots, large data analysis systems and virtual assistants.<br />
<br />
<br />
'''''Functionalities'''''<br />
<br />
In order to improve its user's concentration and motivation (objective 1), Coco would benefit from permission to control apps or websites. Imagine, for instance, that someone has his/her social media open when trying to make exercises. Incoming messages could cause a lot of distraction, which could be prevented by (temporarily) blocking this website. Related to this, Coco could monitor your activity on the computer. In this way, it could provide summaries and recommendations on how you should invest your time. <br />
<br />
Coco could also be used to make an efficient and motivating planning for its users. Being able to link Coco to the user's schedule would be necessary for this task. <br />
<br />
Hence, some of the desired functionalities of Coco are thought to be implemented easier in a virtual agent than in a physical robot. <br />
<br />
<br />
'''''Conclusion'''''<br />
<br />
Although physical robots could be a better choice for certain applications, it seems that in the end for Coco, the costs and benefits of a virtual agent outweigh the benefits of a physical robot. One large advance is the fact that a virtual agent would make the technology better accessible to a large public. Although not everyone has the money to buy an advanced robot, many would be able to invest in a license to use the software. In this way, the objectives of Coco, can be achieved the best by developing a virtual agent.<br />
<br />
===Analysis Survey 1===<br />
<br />
The Stata code for the analysis of survey 1 can be found in Appendix A.<br />
<br />
<br />
'''''Interest'''''<br />
<br />
The first part of the analysis was quite exploratory: a general question was asked about whether people were interested in having a virtual agent as described in the survey. The research question for this first part was formulated in the following way: 'what are the differences in interests and preferences for people with different demographical characteristics?''<br />
<br />
When it comes to interest in the VA, almost half of the respondents expressed their doubts. They did not immediately reject the idea, but thought their choice depended on for example the situation and the functionality of the agent. When looking at age, people that were not sure were distributed very evenly. A Wilcoxon rank-sum test however shows a significant difference in age for people who say they would be interested and people who say they are not (p=0.001). It seems younger people are more open to the idea of having a virtual agent.<br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Scatter_plot_of_age_by_interest.png|400px|thumb|left|Figure X: Scatter plot of interest by age]]<br />
[[File:Bar_chart_interest_age.png|400px|thumb|left|Figure X: Bar chart of interest by age]]<br />
</div><br />
<br />
<br />
Looking at gender, both males and females are interested for around 25% of the time. Even though the figure below suggests that females more often consider the idea of having a virtual agent, this difference is not significant (X^2, p=0.07).<br />
<br />
<div style="display:flex;flex-direction:row;"> [[File:Interest_gender.png|400px|thumb|left|Figure X: Bar chart of interest by gender]] </div><br />
<br />
<br />
From Figure 4 we can also conclude that people who use their laptop around 7-9 hours a day are most open to the idea of a virtual agent. Least interested are people who use their computer less than 2 hours a day, or more than 9 hours. For both groups respondents indicated that they do not need such a virtual agent.<br />
<br />
<div style="display:flex;flex-direction:row;"> [[File:Interest_daily_working_hours.png|400px|thumb|left|Figure X: Bar chart of interest by working hours]] </div><br />
<br />
<br />
<br />
'''''Corona impact on productivity and health'''''<br />
<br />
The second part of the analysis was about the influence of corona on productivity and health. Several research questions have been formulated:<br />
<br />
''Do people feel that their'' '''productivity, including concentration and motivation''' '', has decreased because of the corona virus?''<br />
<br />
''Do people feel that their'' '''physical health''' ''has decreased because of the corona virus?''<br />
<br />
''Do people feel that their'' '''mental health''' ''has decreased because of the corona virus?'' <br />
<br />
<br />
Considering the effect of COVID-19 on people's perception of their work situation, several observations can be made. Generally, mental health, loneliness and productivity are the three areas that have been negatively impacted by the corona crisis the most. <br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Bar_chart_breaks.png|300px|thumb|left|Figure X: Bar chart of breaks]]<br />
[[File:Bar_chart_concentration.png|300px|thumb|left|Figure X: Bar chart of concentration]]<br />
[[File:Bar_chart_loneliness.png|300px|thumb|left|Figure X: Bar chart of loneliness]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Bar_chart_mental_health.png|300px|thumb|left|Figure X: Bar chart of mental health]]<br />
[[File:Bar_chart_motivation.png|300px|thumb|left|Figure X: Bar chart of motivation]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Bar_chart_physical_health.png|300px|thumb|left|Figure X: Bar chart of physical health]]<br />
[[File:Bar_chart_productivity.png|300px|thumb|left|Figure X: Bar chart of productivity]]<br />
</div><br />
<br />
<br />
Since this questionnaire was also made to determine the user group that would need the help of a VA the most, the following matters will also be analyzed based on age. Generally, people older than 25 take less breaks since COVID-19, and younger people tend to take more breaks. Younger people generally think their concentration has decreased since COVID-19 and on the other hand, people older than 25 generally say their concentration has increased. However, both groups state their productivity has decreased. Although both groups state that their mental health, physical health and feeling of loneliness have decreased since COVID-19, the younger user group indicates a greater negative impact. <br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Bar_chart_breaks_age.png|300px|thumb|left|Figure X: Bar chart of breaks by age]]<br />
[[File:Bar_chart_concentration_age.png|300px|thumb|left|Figure X: Bar chart of concentration by age]]<br />
[[File:Bar_chart_loneliness_age.png|300px|thumb|left|Figure X: Bar chart of loneliness by age]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Bar_chart_mental_health_age.png|300px|thumb|left|Figure X: Bar chart of mental health by age]]<br />
[[File:Bar_chart_motivation_age.png|300px|thumb|left|Figure X: Bar chart of motivation by age]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Bar_chart_physical_health_age.png|300px|thumb|left|Figure X: Bar chart of physical health by age]]<br />
[[File:Bar_chart_productivity_age.png|300px|thumb|left|Figure X: Bar chart of productivity by age]]<br />
</div><br />
<br />
<br />
In Table 1 the means of the two groups are shown to clarify the above made statements. When the mean is smaller than 0, there is on average a decrease and if this impact is bigger than 0, there is on average an increase. There is a significant difference between the two groups if the p-value is smaller than 0.05. All this information suggests that it might be best to focus on people of 25 or younger for this research and the development of the computer companion. <br />
<br />
''Table 1: The means for several corona impact measurements and the differences between the different age groups.''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Aspects !! Mean <= 25 !! Mean > 25 !! p-value<br />
|-<br />
| Taking breaks || 0.19 || -0.49 || 0.0001<br />
|-<br />
| Concentration || -0.30 || 2.9 || 0.0004 <br />
|-<br />
| Feeling of loneliness || -0.40 || -0.36 || 0.7745 <br />
|-<br />
| Mental health || -0.61 || -0.28 || 0.0117 <br />
|-<br />
| Motivation || 0.37 || 0.19 || 0.1868 <br />
|-<br />
| Physical health || -0.74 || -0.09 || 0.0001 <br />
|-<br />
| Productivity || -0.47 || -0.31 || 0.2165 <br />
|-<br />
|}<br />
<br />
<br />
To summarize, considering all respondents the productivity, physical health and mental health have decreased. Generally, concentration has not been affected by COVID-19. Respondents even indicated an increase in motivation. Once the respondents were grouped by age, a clear difference in responses could be seen. The people of 25 years old and younger indicated a slight decrease in concentration, while people over 25 indicated a slight increase in concentration. Even though both groups indicated a decrease for most other cases, people of 25 years old and younger indicated a higher decrease in mental health, physical health and productivity. For both groups motivation increased, but there was a bigger increase indicated for the younger group.<br />
<br />
<br />
Regarding the open question 'Are there other activities than the ones mentioned above that you often experience to negatively impact your productivity?', around 20 people mentioned that phone calls, applications like Netflix, and message platforms like Microsoft Teams and WhatsApp disturb their productivity. Besides that, 15 participants mentioned that their family and other people in their household negatively impact productivity. Children for example ask for a lot of attention when parents are working at home, and they need to be helped with homeschooling (unfortunately, Coco will not be able to solve this problem). Besides these two main impact factors, some participants also mentioned no breaks, lack of physical contact, the mailman, spontaneous household chores, unstructured schedules, bad sleep quality, a non-stimulating workplace, ambient noise, and the monotony of the day as things that negatively impact productivity.<br />
<br />
<br />
'''''Helpful tasks for a virtual agent'''''<br />
<br />
The third part of the analysis was about the tasks of a virtual agent users would see as useful. The research question for this part is: ''Which tasks are most important for a computer companion to possess?''<br />
<br />
Sixteen different variables are used to see what users prefer as tasks for a virtual agent. For every variable, the respondents could choose five options from a Likert scale varying between ‘Not useful at all’ to ‘Very useful’. From the histograms it can be seen that ‘Help avoiding distractions by blocking applications’, ‘Help to split up daunting tasks’, ‘Encourage taking breaks’, ‘Encourage physical activity during breaks’, ‘Help increase productivity with your favorite concentration/focus application’, ‘Reflect on work at the end of the day’, ‘Help schedule tasks in your preferred scheduling application’, ‘Help you to focus on a currently scheduled task by getting you back from distractions’, ‘Providing reminders of your schedule’, ‘Make task switching smoother’ and ‘Help you to work according to your preferred time-scheduling technique’ all seem to lean to the right side, indicating that they are thought to be more useful. <br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Hist_1.png|300px|thumb|left|Figure X: Bar chart of interest in blocking applications]]<br />
[[File:Hist_2.png|300px|thumb|left|Figure X: Bar chart of interest in splitting up tasks]]<br />
[[File:Hist_3.png|300px|thumb|left|Figure X: Bar chart of interest in encouraging to take breaks]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Hist_4.png|300px|thumb|left|Figure X: Bar chart of interest in encouraging to be physically active]]<br />
[[File:Hist_5.png|300px|thumb|left|Figure X: Bar chart of interest in providing physical exercises]]<br />
[[File:Hist_6.png|300px|thumb|left|Figure X: Bar chart of interest in connecting with focus application]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Hist_7.png|300px|thumb|left|Figure X: Bar chart of interest in providing motivation]]<br />
[[File:Hist_8.png|300px|thumb|left|Figure X: Bar chart of interest in reflection at the end of the day]]<br />
[[File:Hist_9.png|300px|thumb|left|Figure X: Bar chart of interest in connecting with scheduling application]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Hist_10.png|300px|thumb|left|Figure X: Bar chart of interest in providing small talk]]<br />
[[File:Hist_11.png|300px|thumb|left|Figure X: Bar chart of interest in sending auditory reminders]]<br />
[[File:Hist_12.png|300px|thumb|left|Figure X: Bar chart of interest in helping focus on current tasks]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Hist_13.png|300px|thumb|left|Figure X: Bar chart of interest in connecting with applications to define tasks]]<br />
[[File:Hist_14.png|300px|thumb|left|Figure X: Bar chart of interest in providing reminders]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Hist_15.png|300px|thumb|left|Figure X: Bar chart of interest in making task switching smoother]]<br />
[[File:Hist_16.png|300px|thumb|left|Figure X: Bar chart of interest in helping work to scheduling technique]]<br />
</div><br />
<br />
<br />
These histograms can be supported by skewness values. A skewness value below zero means that the data is more distributed to the right, while a positive value means that the data is more distributed to the left. We try to find a negative skewness here to confirm what is mentioned above. As can be seen in table 2, all the skewness values of above topics are indeed negative, however, some are larger skewed than others and thus more often chosen to be useful. In conclusion, the most useful tasks (based on negative skewness values between –0.57 to –0.80) are, from highest to lowest; ‘Help avoiding distractions by blocking applications’, ‘Help increase productivity with your favorite concentration/focus application’, ‘Help schedule tasks in your preferred scheduling application’. All other variables have skewness values lower than -0.39 and are thus less helpful than these mentioned above.<br />
<br />
''Table 2: The skewness values for several variables of what could be helpful tasks of a VA.''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Variables !! Skewness<br />
|-<br />
| '''Help avoiding distractions by blocking applications''' || '''-0.7914'''<br />
|-<br />
| Help to split up daunting tasks || -0.3372<br />
|-<br />
| Encourage taking breaks || -0.1631<br />
|-<br />
| Encourage physical activity during breaks || -0.1469<br />
|-<br />
| '''Help increase productivity with your favorite concentration/focus application''' || '''-0.7717'''<br />
|-<br />
| Reflect on work at the end of the day || -0.3852<br />
|-<br />
| '''Help schedule tasks in your preferred scheduling application''' || '''-0.5737'''<br />
|-<br />
| Help you to focus on a currently scheduled task by getting you back from distractions || -0.2038<br />
|-<br />
| Providing reminders of your schedule || -0.2560<br />
|-<br />
| Make task switching smoother || -0.3755<br />
|-<br />
| Help you to work according to your preferred time-scheduling technique || -0.3555<br />
|-<br />
|}<br />
<br />
<br />
To check whether it was a good decision to focus on respondents under 25 years, we have made bar charts of the three most important variables ,grouped by age. The results can be found below and it can be seen that this is indeed a good choice. More younger people have interest in the three most helpful tasks and older people are doubtful or even sure that it will not be helpful. <br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Avoiddistractions_vs_age.png|300px|thumb|left|Bar chart of interest in blocking applications by age]]<br />
[[File:Focusapp_vs_age.png|300px|thumb|left|Bar chart of interest in connecting with focus application by age]]<br />
[[File:Scheduling_vs_age.png|300px|thumb|left|Bar chart of interest in connecting with scheduling application by age]]<br />
</div><br />
<br />
Furthermore, it can be checked using t-tests (or if not allowed; rank sum tests) whether there is a significant difference between age groups for the variables. We start with performing Shapiro-Wilk and Skewness/Kurtosis tests to see if the data is normally distributed. Both p-values should be larger than 0.05 here to not reject the null hypothesis (‘The data is normally distributed’). If this is the case, we may perform a t-test and otherwise we may only perform a non-parametrical test. We will only look for a significant difference in the three most helpful tasks, according to above. Table 3 shows that all variables have a corresponding p-value smaller than 0.05 and thus there is a significance difference between the two age groups and those helpful tasks.<br />
<br />
''Table 3: The corresponding significance values for the three main helpful tasks variables.''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Variables !! Significance value<br />
|-<br />
| Help avoiding distractions by blocking applications || 0.0287<br />
|-<br />
| Help increase productivity with your favorite concentration/focus application || 0.0023<br />
|-<br />
| Help schedule tasks in your preferred scheduling application || 0.0001<br />
|-<br />
|}<br />
<br />
<br />
Regarding the open question 'Are there any tasks which aren't mentioned in the list above that you would like a virtual agent to be able to do?', around 20 people have given useful suggestions for extra features of Coco or have confirmed that already existing features will be helpful. Quite a lot of people mentioned that Coco should be connected to streaming services for music or meditations. Furthermore, some people mentioned that they would like it if Coco were connected to their coffee machine or alarm.<br />
It is also mentioned a lot that Coco should suggest a planning for the day based on tasks and order their email on priority. Moreover, it has also been mentioned a few times that Coco should be able to block certain applications and signal to colleagues whether they are busy or not, some people add that this should happen automatically. These are ideas we already had, and it is thus great to hear that the respondents think the same.<br />
In the survey's additional comments, two people gave additional suggestions for functionality. The first mentioned that it would be useful if you can decide to turn of Coco for a while, especially during the weekend this might be useful. Another person worried that Coco should not make them lose their concentration: “The last thing I want is another application that sends me unsolicited and annoying notifications.”<br />
<br />
In conclusion, our research question ''Which tasks are most important for a computer companion to possess?'' will be answered with the three best options. Coco should be able to help avoid distractions by blocking applications, help increase productivity with users' favorite concentration/focus application, and finally help schedule tasks in users' preferred scheduling application.<br />
<br />
<br />
'''''Appearance'''''<br />
<br />
In the survey, participants had to indicate their preferences for the appearance of Coco. There were three options (human, robot, animal) which they could rank from 1 (most favorable) to 3 (least favorable). The frequency of option 1, 2, or 3 was then encoded for each appearance into three variables (“human_appearance", “robot_appearance", and “animal_appearance"). These results are shown in table 4. <br />
<br />
''Table 4: Percentages of ranking human, robot, and animal appearance.''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! !! Human !! Robot !! Animal<br />
|-<br />
| '''Ranking 1''' || 35.71% || 34.52% || 23.21% <br />
|-<br />
| '''Ranking 2''' || 27.98% || 27.38% || 32.74% <br />
|-<br />
| '''Ranking 3''' || 29.17% || 29.17% || 34.52% <br />
|-<br />
|}<br />
<br />
As becomes clear from table 4, people like a human or a robot almost exactly the same. An animal, on the other hand, is somewhat less favorable, as it is chosen less often as first option and more often as second or third option. It would be useful to focus more on the preferences between humans and robots in the second survey. <br />
<br />
To check for any different preferences between younger (< 25 years) and older (>= 25 years) participants, the histograms shown in Figure X, Figure X, and Figure X are analyzed. Looking at these plots, some differences seem to exist. To check whether these differences are significant, a Chi-square test has been performed for each variable “human_appearance", “robot_appearance", and “animal_appearance". The alpha-value has been set at its default-value of 0.05 and the p-values are 0.000 (human), 0.096 (robot), and 0.012 (animal). Hence, indeed a significant difference is found in the preferences for a human or animal appearance between younger and older people. Older people think a human appearance is most favorable, while younger people think the opposite. Also, older people prefer an animal appearance less than younger people. However, the differences in case of a robot appear to be insignificant. <br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Bar_chart_breaks.png|300px|thumb|left|Figure X: Bar chart of breaks]]<br />
[[File:Bar_chart_concentration.png|300px|thumb|left|Figure X: Bar chart of concentration]]<br />
[[File:Bar_chart_loneliness.png|300px|thumb|left|Figure X: Bar chart of loneliness]]<br />
</div><br />
<br />
Regarding the open question 'Would you rather have a different look for your computer buddy than indicated above?', most people did not have any additional comments about the appearance of Coco, however, some trends could be seen amongst the ones that did gave some input.<br />
First of all, five people mentioned that they would like the option to customize the appearance of Coco. They mention, for instance, that they would like to choose a figure on one day, and another one on the next day. One person even mentioned the difference between work and leisure. Next to this, they would like the option to adapt the figures themselves. Of course, this would require more (difficult) options in the software.<br />
Furthermore, six people mentioned that Coco did not even need an appearance. Something more simplistic, for example such as Siri (mentioned by a couple), would be sufficient in their opinion.<br />
Also, eighteen people gave additional options that could be designed. These ranged from “more natural elements” and “a Disney character” to “a self-chosen picture”.<br />
Lastly, two persons suggested to change our proposed appearances. One mentioned to make them “somewhat older", while the other would like to see some more details.<br />
<br />
<br />
'''''Distractions that negatively impact productivity'''''<br />
<br />
This section will try to answer the following research questions. The first question is, ''is there a significant difference in the extent to which the two groups (workers, age > 25 and students age <= 25) experience the negative impact on their productivity?'' If there is indeed a large difference between the two groups, it might be important to narrow down our user group. And the second research question is ''what types of distractions have the most influence on productivity?'' This question is asked so we can prioritize the functions that Coco needs to have. <br />
<br />
In the survey, we asked our responders the following; "How often do you get distracted from your work by the following activities?". These activities are the internet, lectures, news, personal mail, scheduled meetings, online shopping, social media, unplanned meetings and work mail. To see what types of distractors have the most influence on productivity, we calculated the mean response for both students and workers. The mean response was based on the different answers the respondents could give to the question, to which they could answer the following: never, less than once a month, a few times a month, a few times a week, daily, 2 or 3 times a day, more than three times a day, in which 1 means never and 7 means more than three times a day. The mean amount of distraction can be seen for both age groups per distraction factor in the '''table ....''' below. The significance values are also shown in the table because the effect of the type of distractor can differ significantly per age group. This statistically significant difference is computed with the Wilcoxon rank-sum test. <br />
<br />
''Table ...: Distraction factors''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Distraction factor !! Mean for age <= 25 !! Mean for age > 25 !! Significance value<br />
|-<br />
| ''Internet'' || 4.97 || 3.22 || p = 0.0000 <br />
|-<br />
| ''Lecture'' || 3.6 || 1.92 || p = 0.0000 <br />
|-<br />
| ''News'' || 3.71 || 3.08 || p = 0.1617 <br />
|-<br />
| ''Personal mail'' || 3.86 || 3.36 || p = 0.6041 <br />
|-<br />
| ''Scheduled meetings'' || 3.6 || 3.46 || p = 0.2918 <br />
|-<br />
| ''Shopping'' || 3.03 || 1.82 || p = 0.0000 <br />
|-<br />
| ''Social media'' || 5.4 || 2.9 || p = 0.0000 <br />
|-<br />
| ''Unplanned meetings'' || 2.8 || 3.58 || p = 0.0002 <br />
|-<br />
| ''Work mail'' || 4.06 || 4.64 || p = 0.0005 <br />
|}<br />
<br />
As you can see in '''table .....''', the mean distraction is higher for every sort of distraction type except for work-related mail. This highlights that students’ (people aged <= 25) productivity is more often affected by these distractions than the productivity of working people. To give an example, a mean of 4.97 (internet) for people aged 25 or lower means that they are distracted 'daily' on average. The differences between the two groups are significant for internet, lectures, shopping, social media, unplanned meetings and for work mail. This table also informs us about what types of distractors have the most influence on both groups. For clarity,''' table .....''' below shows the rank order of the amount of distraction per age group. <br />
<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Rank !! Students !! Workers<br />
|-<br />
| 1. || Social media || Work mail <br />
|-<br />
| 2. || Internet || Unplanned meetings<br />
|-<br />
| 3. || Work mail || Scheduled meetings<br />
|-<br />
| 4. || Personal mail|| Personal meetings<br />
|-<br />
| 5. || News || Internet<br />
|-<br />
| rowspan="2" | 6. || Lectures & Scheduled meetings || News<br />
|-<br />
| Scheduled meetings<br />
|-<br />
| 7. || Shopping || Social media <br />
|-<br />
| 8. || Unplanned meetings || Lectures<br />
|-<br />
| 9. || || Shopping<br />
|}<br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Graph_Internet_Distraction.jpg|400px|thumb|left|Frequency of distraction (internet), grouped by age]]<br />
[[File:Graph_Lectures_Distraction.jpg|400px|thumb|left|Frequency of distraction (lectures), grouped by age]]<br />
[[File:Graph_News_Distraction.jpg|400px|thumb|left|Frequency of distraction (news), grouped by age]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Graph_Personal_Mail_Distraction.jpg|400px|thumb|left|Frequency of distraction (personal mail), grouped by age]]<br />
[[File:Graph_Scheduled_Meetings_Distraction.jpg|400px|thumb|left|Frequency of distraction (scheduled meetings), grouped by age]]<br />
[[File:Graph_Shopping_Distraction.jpg|400px|thumb|left|Frequency of distraction (shopping), grouped by age]]<br />
</div><br />
<br />
<div style="display:flex;flex-direction:row;"><br />
[[File:Graph_Social_Media_Distraction.jpg|400px|thumb|left|Frequency of distraction (social media), grouped by age]]<br />
[[File:Graph_Unplanned_Meetings_Distraction.jpg|400px|thumb|left|Frequency of distraction (unplanned meetings), grouped by age]]<br />
[[File:Graph_Workmail_Distraction.jpg|400px|thumb|left|Frequency of distraction (work mail), grouped by age]]<br />
</div><br />
<br />
<br />
To summarize, there is indeed a significant difference between the two age groups and the times that they get distracted by different factors, so this again confirms that it is a good idea to split up the groups and investigate them separately. From both the graphs and the statistical analysis you can see that students are more often distracted by multiple factors than workers. For these students, the two largest distractor factors are, by far, social media and the internet. These factors distract students two or three times a day and daily (respectively).<br />
<br />
<br />
'''''Money'''''<br />
<br />
There were quite some different answers to the question what people were willing to pay for Coco. There were three main categories in which people answered the question. One was paying per certain period, for example a monthly or yearly fee. The second category of people wanted Coco to be a one-time buy, and the third category consists of other answers.<br />
<br />
<br />
''Payments per period''<br />
<br />
Around 17 people said that they would like to pay monthly or yearly, however the amount that they were willing to pay varied widely. Most of these people wanted to pay somewhere between 5 – 20 Euros per month.<br />
<br />
<br />
''One-time buy''<br />
<br />
The amount of money that people were willing to pay in a one-time buy varied widely again, between 2 Euros to 500 Euro's. However, most people responded with 10 Euro's and 50 Euro's. The average price that people were willing to pay was approximately 68 Euro's.<br />
<br />
<br />
''Other''<br />
<br />
17 people mentioned that they did not want to pay for the software and there were also some additional ideas posed by the respondents, for example, 9 people mentioned that 'work should pay for this'. Besides that, some people said that they wanted a free tryout first to see the functionalities before they want to pay for it. If results are good, they are willing to pay around 10 Euros per month or around 100 Euros per year. Moreover, a few people commented that it depends on the type of license; a one-time purchase, or pay on monthly base or yearly base? This was an unnecessary dubiety on our side.<br />
<br />
<br />
'''''General conclusion of analysis 1'''''<br />
<br />
As indicated in the second part of the analysis 'Corona impact on productivity and health', it might be best to focus on people of 25 years old or younger. Their productivity, concentration, mental health, and physical health have decreased the most since COVID-19. Coco would be best of help for the users if it could aid them during their work considering these aspects.<br />
<br />
Because almost all the frequencies of the distraction types are higher for students than for workers, it can be concluded that Coco would be more useful for students. The two things that distract students the most compared to the other distraction factors are social media, and the internet. Therefore it is of great importance that Coco will be able to prevent the students from getting distracted by at least these two factors.<br />
<br />
==References==<br />
<br />
<references/><br />
<br />
<br />
<br />
<br />
==Appendix==<br />
===Appendix A - Stata code analysis survey 1===<br />
<br />
Here the Stata code for the analysis of survey 1 can be found.<br />
<br />
[[File:Wiki_appendix_1.png|1500px]]<br />
[[File:Wiki_appendix_2.png|1500px]]<br />
[[File:Wiki_appendix_3.png|1500px]]<br />
[[File:Wiki_appendix_4.png|1500px]]<br />
[[File:Wiki_appendix_5.png|1500px]]<br />
[[File:Wiki_appendix_6.png|1500px]]<br />
[[File:Wiki_appendix_7.png|1500px]]<br />
[[File:Wiki_appendix_8.png|1500px]]<br />
[[File:Wiki_appendix_9.png|1500px]]<br />
[[File:Wiki_appendix_10.png|1500px]]<br />
[[File:Wiki_appendix_11.png|1500px]]<br />
[[File:Wiki_appendix_12.png|1500px]]<br />
[[File:Wiki_appendix_13.png|1500px]]</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Logbook_group04&diff=115792Logbook group042021-05-16T18:39:16Z<p>20182838: /* Week 4 */</p>
<hr />
<div><div style="font-family: 'Calibri'; font-size: 15px; line-height: 1.5; max-width: 1100px; word-wrap: break-word; color: #333; font-weight: 400; margin-left: auto; margin-right: auto; padding: 50px; background-color: white; padding-top: 25px;"><br />
<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Logbook Group 4 </font><br />
<br />
== Week 1 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4.5 hours), Meeting preparation (0.25 hours), Meeting (1.5 hours), Deliverables (1 hour), Checking text for Wiki (1.25 hours), Mendeley (0.5 hour), State of the Art (1,25 hours)<br />
|-<br />
| Ezra Gerris || 10 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5 hours), Writing the Approach + literature study (2 hours)<br />
|-<br />
| Kari Luijt || 11.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5h), User group (1h) , Subject + User group + Milestones + Checking document (1h 15min), Literatire studies + Problem statement +upload things to wiki (1h 30 min)<br />
|-<br />
| Julie van der Hijde || 12.25 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature Study (4 hours), Making referencing correct (0.5 h), Meeting (1.5h), Objectives&Problem statement + checking entire doc(2 h), Update Wiki (0.75h), Upload everything on wiki (1h)<br />
|-<br />
| Silke Franken || 16 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (1 h), learn more about Wikitext / update Wiki (3h 15), Meeting (1.5h), Literature study (7 h), Planning (1 h)<br />
|-<br />
|}<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 14.25 hours || Meeting (1h), Investigating AI possibilities (4.5h), Survey (1h), (Tutor) meeting (1.75h), Emails to tutor (0.25h), Reading Grover (1.5h), Literature study (1.5h), Writing State-of-the-art (1.75h), Checking text for Wiki (1h)<br />
<br />
|-<br />
| Ezra Gerris || 11 hours || Meeting (1h), Survey questions (0.5h), USE (1.25h), (Tutor) meeting (1.75h), Read Grover (2.5h), (Re)write motivation (2.5h), Checking and updating wiki (1.5h)<br />
<br />
|-<br />
| Kari Luijt || 13.25 hours || Meeting (1h), Preparation survey questions (1h), Making survey questions (6h), (Tutor) meeting (1.75h), Making the survey (1.25h), Making the outro (.25h), Read state of the art + motivation for COCO (.5h), Read Grover (1.5h) <br />
<br />
|-<br />
| Julie van der Hijde || 13.75 hours || Meeting (1h), USE (1.5h), Wiki (0.5h), Survey questions (1h), (Tutor) meeting (1.75h), Read Grover + notes for future (2.5h), search articles motivation (1.5h), motivation COCO (3h), Checking survey, wiki, motivation, state-of- the-art (1h)<br />
<br />
|-<br />
| Silke Franken || 16.5 hours || Meeting (1h), Making survey questions (6.5h), Literature study (1.5h), Read complete plan (0.5h) Design of AI companion (1h), (Tutor) meeting (1.75h), Update planning (15min), writing intro and checking survey (2h), State-of-the-Art (1h), Update Wiki (1h)<br />
<br />
|-<br />
|}<br />
<br />
== Week 3 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || (Tutor) meeting (1.5h), Boost AI (0.5h), Checking, testing, distributing survey (3h), Literature study design (2h), Meeting (0.75h), Literature study motivation (2.5h), Writing motivation (2.25h), Checking text (0.25h)<br />
|-<br />
| Ezra Gerris || 9.75 hours || (Tutor) meeting (1.5h), Translate informed consent (0.75h), Survey (i.e. translating, distributing, checking, testing) (3h), Meeting (0.75h), Sending mail to Boost AI (0.25h), Literature study motivation and updating wiki (3.5h)<br />
|-<br />
| Kari Luijt || 10.75 hours || (Tutor) meeting (1.5h), Survey (3h), Literature study (1.5h), Meeting (.75h), Text design (2u), Stata do-file (2u)<br />
|-<br />
| Julie van der Hijde || 9 hours || Agenda + update wiki (0.5h), (Tutor) meeting (1.5h), Survey + sending to network (3h), Literature study (4h)<br />
|-<br />
| Silke Franken || 16.75 hours || (Tutor) meeting (2h 15min), survey (3h), design (1h), literature study design (4h), Stata preprocessing do-file (3.5h), Stata do-file (2u), update Wiki (1h)<br />
|-<br />
|}<br />
<br />
== Week 4 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 13 hours || (Tutor) meeting (1h), Motivation (1,5h), Analysis "Appearance" and "Additional comments" questions (3.75h), Meetings (2.5h), Preparation Stata (1h), Stata Analysis "Appearance" (2.5h), Checking analysis (0.75h)<br />
|-<br />
| Ezra Gerris || 10 hours || (Tutor) meeting (1h), Write sources wiki (0.5h), Analysis "Helpful Tasks" question (2h), Analysis meeting 1 (1.25h), Analysis meeting 2 (1h), Analysis (3h), Updating wiki and motivation (1.25h)<br />
|-<br />
| Kari Luijt || 12.5 hours || (Tutor) meeting (1h), Remove duplicates from data (.5h), Analysis "Interest now" question (2h), Desgin recommendation (.25h), Meeting (1.5h), Meeting (1h), Analysis "Interest now and influence corona"(3.5u), Updating WIKI (2.75h)<br />
|-<br />
| Julie van der Hijde || 12.5 hours || Meeting (.5h), Analysis open questions (3h), Meeting (1.5h), Meeting (1h), data analysis + text + upload to wiki (6.5h)<br />
|-<br />
| Silke Franken || 13 hours || (Tutor) meeting (1h), Survey data preprocessing (1.5h), design recommendation (3h 15min), meeting/stata analysis (2h 15min), Analysis "interest postcorona" open question (2.5h), Analysis "Interest now and influence corona"(2.5u)<br />
<br />
|-<br />
|}<br />
<br />
== Week 5 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 6 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 7 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 8 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 9 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Logbook_group04&diff=115218Logbook group042021-05-09T19:32:49Z<p>20182838: /* Week 3 */</p>
<hr />
<div><div style="font-family: 'Calibri'; font-size: 15px; line-height: 1.5; max-width: 1100px; word-wrap: break-word; color: #333; font-weight: 400; margin-left: auto; margin-right: auto; padding: 50px; background-color: white; padding-top: 25px;"><br />
<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Logbook Group 4 </font><br />
<br />
== Week 1 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4.5 hours), Meeting preparation (0.25 hours), Meeting (1.5 hours), Deliverables (1 hour), Checking text for Wiki (1.25 hours), Mendeley (0.5 hour), State of the Art (1,25 hours)<br />
|-<br />
| Ezra Gerris || 10 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5 hours), Writing the Approach + literature study (2 hours)<br />
|-<br />
| Kari Luijt || 11.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5h), User group (1h) , Subject + User group + Milestones + Checking document (1h 15min), Literatire studies + Problem statement +upload things to wiki (1h 30 min)<br />
|-<br />
| Julie van der Hijde || 12.25 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature Study (4 hours), Making referencing correct (0.5 h), Meeting (1.5h), Objectives&Problem statement + checking entire doc(2 h), Update Wiki (0.75h), Upload everything on wiki (1h)<br />
|-<br />
| Silke Franken || 16 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (1 h), learn more about Wikitext / update Wiki (3h 15), Meeting (1.5h), Literature study (7 h), Planning (1 h)<br />
|-<br />
|}<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 14.25 hours || Meeting (1h), Investigating AI possibilities (4.5h), Survey (1h), (Tutor) meeting (1.75h), Emails to tutor (0.25h), Reading Grover (1.5h), Literature study (1.5h), Writing State-of-the-art (1.75h), Checking text for Wiki (1h)<br />
<br />
|-<br />
| Ezra Gerris || 11 hours || Meeting (1h), Survey questions (0.5h), USE (1.25h), (Tutor) meeting (1.75h), Read Grover (2.5h), (Re)write motivation (2.5h), Checking and updating wiki (1.5h)<br />
<br />
|-<br />
| Kari Luijt || 13.25 hours || Meeting (1h), Preparation survey questions (1h), Making survey questions (6h), (Tutor) meeting (1.75h), Making the survey (1.25h), Making the outro (.25h), Read state of the art + motivation for COCO (.5h), Read Grover (1.5h) <br />
<br />
|-<br />
| Julie van der Hijde || 13.75 hours || Meeting (1h), USE (1.5h), Wiki (0.5h), Survey questions (1h), (Tutor) meeting (1.75h), Read Grover + notes for future (2.5h), search articles motivation (1.5h), motivation COCO (3h), Checking survey, wiki, motivation, state-of- the-art (1h)<br />
<br />
|-<br />
| Silke Franken || 16.5 hours || Meeting (1h), Making survey questions (6.5h), Literature study (1.5h), Read complete plan (0.5h) Design of AI companion (1h), (Tutor) meeting (1.75h), Update planning (15min), writing intro and checking survey (2h), State-of-the-Art (1h), Update Wiki (1h)<br />
<br />
|-<br />
|}<br />
<br />
== Week 3 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || (Tutor) meeting (1.5h), Boost AI (0.5h), Checking, testing, distributing survey (3h), Literature study design (2h), Meeting (0.75h), Literature study motivation (2.5h), Writing motivation (2.25h), Checking text (0.25h)<br />
|-<br />
| Ezra Gerris || 6.25 hours || (Tutor) meeting (1.5h), Translate informed consent (0.75h), Survey (i.e. translating, distributing, checking, testing) (3h), Meeting (0.75h), Sending mail to Boost AI (0.25h), Literature study motivation (Xh)<br />
|-<br />
| Kari Luijt || 10.75 hours || (Tutor) meeting (1.5h), Survey (3h), Literature study (1.5h), Meeting (.75h), Text design (2u), Stata do-file (2u)<br />
|-<br />
| Julie van der Hijde || 6.5 hours || Agenda + update wiki (0.5h), (Tutor) meeting (1.5h), Survey + sending to network (2.5h), Literature study (2h)<br />
|-<br />
| Silke Franken || 16.75 hours || (Tutor) meeting (2h 15min), survey (3h), design (1h), literature study design (4h), Stata preprocessing do-file (3.5h), Stata do-file (2u), update Wiki (1h)<br />
|-<br />
|}<br />
<br />
== Week 4 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 5 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 6 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 7 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 8 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 9 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Related_Literature_Group_4,_design&diff=115217Related Literature Group 4, design2021-05-09T19:30:14Z<p>20182838: /* Behaviour and personality */</p>
<hr />
<div><div style="font-family: 'Calibri'; font-size: 15px; line-height: 1.5; max-width: 1100px; word-wrap: break-word; color: #333; font-weight: 400; margin-left: auto; margin-right: auto; padding: 50px; background-color: white; padding-top: 25px;"><br />
<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Excerpts & citations </font><br />
<br />
====Gender====<br />
<br />
* Females chose to gender-match and to interact with a more realistic VA. Males exhibited little preference for either gender, and a greater preference than females for realistic VAs. Thus, where it is not feasible to gender-match in SSCO, the recommendation is to implement a realistic female VA. <ref name="payne2013">Payne, J., Szymkowiak, A., Robertson, P., & Johnson, G. (2013). Gendering the machine: Preferred virtual assistant gender and realism in self-service. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8108 LNAI, 106–115. https://doi.org/10.1007/978-3-642-40415-3_9</ref><br />
<br />
* Young and attractive female agent positively impacts interest in learning, an older an unattractive male agent does not impact motivation. <ref name="shiban2015">Shiban, Y., Schelhorn, I., Jobst, V., Hörnlein, A., Puppe, F., Pauli, P., & Mühlberger, A. (2015). The appearance effect: Influences of virtual agent features on performance and motivation. Computers in Human Behavior, 49, 5–11. https://doi.org/10.1016/j.chb.2015.01.077</ref><br />
<br />
* As research suggests that the combination of an agent’s gender and personality can play an important role in user perceptions and expectations [1, 37], employing a male agent instead may result in some significant differences in user perceptions or ratings of the agent. We encourage future work to investigate how gender and personality of a workplace productivity agent might influence user experience. <ref name="grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref><br />
<br />
* participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
* We found that the designed characteristics of VAs affects some aspects of user impressions (i.e. personality and trustworthiness) of the VA, while other impressions are not affected (i.e. social ability). We also found that gender matching between the agent and the user affect user impressions. // Gender similarity --> more trustworthy <ref name="akbar2018">Akbar, F., Grover, T., Mark, G., & Zhou, M. X. (2018, March 5). The effects of virtual agents’ characteristics on user impressions and language use. International Conference on Intelligent User Interfaces, Proceedings IUI. https://doi.org/10.1145/3180308.3180365</ref><br />
<br />
* In addition, the similarity of an agent to the learner positively influences the learner’s motivation (Bailenson, Blascovich, & Guadagno, 2008 in a study with undergraduates). For example, computer-based female agents yielded better motivational outcomes for undergraduate women if they matched the students with respect to race and gender (Rosenberg-Kima, Plant, Doerr, & Baylor, 2010). Another study, conducted with undergraduates by Rosenberg-Kima, Baylor, Plant, and Doerr (2008) revealed that a female agent rated as young, attractive and “cool” succeeded in enhancing young female students’ self-efficacy, which is believed to be a driving force behind motivation (Bandura, 1997). All these findings are theoretically supported by Bandura’s social cognitive learning theory which states that people often learn behavior and norms by imitating people whom they perceive as similar (or superior: higher in rank or status) to them and who are therefore rather accepted as social role models (Bandura, 1986). This finding is supported by another study of Gulz, Haake, and Tärning (2007) which demonstrated that participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
====Appearance and human-likeness====<br />
<br />
* In this study, it was noted that PIAs’ human-like features could influence the manner in which participants (i.e. males or females) related to a particular PIA and participants’ preference level of a particular PIA. The result of the Fischer’s exact test conducted in this study (Table V) found no statistically significant effect between participants’ gender (male or female) and their preferred gender of PIAs. It is concluded that the gender of participants (i.e. male or female) has no role (impact) on their preferences regarding the type, features, or gender of PIA, neither on their preference level of PIA. <ref name="mabanza2019">Mabanza, N. (2019). Gender influences on preference of pedagogical interface agents. 2018 International Conference on Intelligent and Innovative Computing Applications, ICONIC 2018. https://doi.org/10.1109/ICONIC.2018.8601292</ref><br />
<br />
* On the other hand, previous studies have also shown that developing overly humanized agents results in high expectations and uncanny feelings. <ref name="chaves2021">Chaves, A. P., & Gerosa, M. A. (2021). How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Desi | Enhanced Reader. International Journal of Human–Computer Interaction, 37(8), 729–758. https://doi.org/10.1080/10447318.2020.1841438</ref><br />
<br />
* Many participants saw the human-like appearance of the VA prototype as setting the wrong expectations in terms of its capabilities, and they were disappointed when the agent’s intelligence only extended towards responding to the their emotion and prompting more self-reflection. (…) We believe incorporating human-like qualities and emotional intelligence into future agents to be worthwhile; however, intelligence should also extend into other aspects of the agent’s capabilities in order to better help users be as efficient as possible in achieving their work goals. <ref name="grover2020"/><br />
<br />
* A study by Go and Sundar <ref name="go2019">Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j.chb.2019.01.020</ref> states that revealing the identity of a chatbot as a non-human can have a positive effect: user will have less high expectations about the conversation, and will be impressed when an agent shows human-like behaviour. Furthermore, they emphasize the importance of the conversational style between a human an a computer. When the dialogue resembles that of an actual human, perceived feelings of social presence and homophily will increase, leading to more positive attitudes towards the agent (and in turn potential desired behaviour consequences).<br />
<br />
* the presence of a representation produced more positive social interactions than not having a representation <ref name="Yee2017">Yee, N., Bailenson, J. N., & Rickertsen, K. (2007). A Meta-Analysis of the Impact of the Inclusion and Realism of Human-Like Faces on User Experiences in Interfaces.</ref><br />
** human-like representations with higher realism produced more positive social interactions than representations with lower realism; however, this effect was only found when subjective measures were used. Behavioral measures did not reveal a significant difference between representations of low and high realism. <br />
*** the difference we found may also be driven by demand characteristics. Participants interacting with an animated character (as opposed to a photograph) may suppose that the researcher is expecting a high appraisal.<br />
**while the presence of a face is better than no face at all, the quality of the face matters much less.<br />
***it is quite possible that animating highly realistic faces inherently allows for residual attributes of the faces that are negative—for example making 3D human faces may produce gestures and animations that appear unnatural or disturbing<br />
**while most studies have found that interface agents have positive effects on task performance, these effects are overall actually quite small.<br />
<br />
* In addition to understanding human social behavior around computers, another extensive line of work examines humans’ responses to computers with more expressive, human-like qualities, such as faces and facial expressions. In general, these studies have found that anthropomorphic properties of computers influence users’ perceptions (e.g., [13, 70, 86]), attitude (e.g., [13]), and behavior around such systems (e.g., [48, 86, 105]). For example, Sproull et al. [86] report that people respond to a text-based interface differently than to a talking face. On the one hand, users are aroused more and present themselves more positively when interacting with a talking face. On the other hand, users are less relaxed or assured when interacting with a talking face. Another study shows that users find the interface with the anthropomorphic qualities—faces and facial expressions—more likeable and engaging, although such an interface takes the users’ effort to interpret the meaning of the human-like expressions and may even be a distraction [48, 86]. More recent studies show that human-like features with higher realism elicit more positive social interactions while having no significant impact on user task performance [105]. Furthermore, a study reveals that anthropomorphism may even elicit user objections due to users’ own biases (e.g., sexism) [70]. <ref name="zhou2019">Zhou, M. X., Yang, H., Mark, G., & Li, J. (2019). Trusting Virtual Agents: The Effect of Personality. ACM Trans. Interact. Intell. Syst, 9(3). https://doi.org/10.1145/3232077</ref><br />
<br />
* Physically present agent leads to better motivational outcomes than a voice or text-box. It is important to design agents realistically, because e.g. cartoon figures have been shown to diminish the positive motivating effects in comparison to realistic figures. <ref name="shiban2015"/><br />
<br />
* It was found also that attractive VT did not improve perceived value of exercise and perceived risks of health. This result is different from some prior studies (e.g. Shiban et al., 2015) which find that attractive virtual pedagogical agents are effective for engaging students in learning. There are two possible explanations for this observed difference. First, attractive virtual agents may catch the attention of users and make users more confident in using a VTS. However, they may also distract users from core learning materials (Moreno and Flowerday, 2006). Second, attractive agents are closer to friends rather than experts. The incongruence of expert perception may lead to contextual irrelevance which makes users take health-related information from virtual trainers lightly (Veletsianos, 2010). <ref name="kwok2021">Kwok, R. C. W., Leung, A. C. M., Hui, S. S. chuen, & Wong, C. C. K. (2021). Virtual trainer system: a tool to increase exercise participation and work productivity. Internet Research. https://doi.org/10.1108/INTR-04-2020-0236</ref><br />
<br />
* If McCloud’s (1993) framework is applied, a teacher character, representing the other to a higher extent than a learning companion, might benefit from more realism in the representation. A learning companion character, being to a higher extent conceived of as an extension of oneself, may, on the other hand, benefit from a more iconic representation. <ref name="gulz2006">Gulz, A., & Haake, M. (2006). Design of animated pedagogical agents - A look at their look. International Journal of Human Computer Studies, 64(4), 322–339. https://doi.org/10.1016/j.ijhcs.2005.08.006</ref><br />
<br />
* The findings of this experiment suggest a new design method for human-agent collaboration work. If we use an agent having a non-human-like appearance (for example, the robot-like agent), the user seemed to attribute much responsibility to the agent and not to trust the agent. We suggest using the agent having human-like appearance in human-agent collaboration work from this experiment. A question is raised about the commonly accepted beliefs about agent design for social tasks. We should consider the attribution of responsibility to construct trustworthy agents. <ref name="matsui2021">Matsui, T., & Koike, A. (2021). Who is to blame? The appearance of virtual agents and the attribution of perceived responsibility. Sensors, 21(8), 1–13. https://doi.org/10.3390/s21082646</ref><br />
<br />
====Behaviour and personality====<br />
<br />
=====Behaviour=====<br />
<br />
* It is possible to program a chatbot in a way that it will interact with you via a moving and talking avatar. The paper also suggests that displaying facial expressions are beneficial in the interaction with such an agent, since displaying the avatar’s emotion can enhance perceived emotional intelligence. <ref name="angga2016">Angga, P. A., Fachri, W. E., Elevanita, A., Suryadi, & Agushinta, R. D. (2016). Design of chatbot with 3D avatar, voice interface, and facial expression. Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015, 326–330. https://doi.org/10.1109/ICSITech.2015.7407826</ref><br />
<br />
* Looije et al researched the guidelines that are needed when developing a personal assistant. These guidelines were derived from interviewing, persuasive technologies and from existing guidelines for personal assistants. In their research they found that guidelines were best expressed in iCat (a personal assistant) that was able to show socially intelligent behavior compared to a non-social or text interface based iCat. <ref name="looije2006">Looije, R., Cnossen, F., & Neerincx, M. A. (2006). Incorporating guidelines for health assistance into a socially intelligent robot. Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, 515–520. https://doi.org/10.1109/ROMAN.2006.314441</ref><br />
<br />
* “The results suggested that it is possible to express extroversion and confidence in the agent by changing the agent's gaze amount regardless of the agent's embodiment. <ref name="koda2018">Koda, T., & Ishioh, T. (2018). Analysis of the effect of agent’s embodiment and gaze amount on personality perception. Proceedings of the 4th Workshop on Multimodal Analyses Enabling Artificial Agents in Human-Machine Interaction, MA3HMI 2018 - In Conjunction with ICMI 2018, 1–5. https://doi.org/10.1145/3279972.3279973</ref><br />
<br />
* Social fidelity is related to certain human-like behaviour and cues. This includes for example speech content (personalized language, feedback, politeness, social memory, personality) and visual cues (facial expressions, gestures, gaze, emotions). <ref name="sinatra2021">Sinatra, A. M., Pollard, K. A., Files, B. T., Oiknine, A. H., Ericson, M., & Khooshabeh, P. (2021). Social fidelity in virtual agents: Impacts on presence and learning. Computers in Human Behavior, 114, 106562. https://doi.org/10.1016/j.chb.2020.106562</ref><br />
<br />
* An expressive emotionally intelligent VA is perceived as more emotionally intelligent when expressing itself through words, tone, body language, and facial expressions. <ref name="ma2019">Ma, X., Yang, E. Y., & Fung, P. (2019). Exploring Perceived Emotional Intelligence of Personality-Driven Virtual Agents in Handling User Challenges. https://doi.org/10.1145/3308558.3313400</ref><br />
<br />
* “Expressions are no more defined by a static representation; rather they are constituted as a succession of signals that appear dynamically. Using few expressions limits the interaction. Endowing agents with large variety of expressions ensures more naturalness in the agent’s behaviour.” <ref name="pelachaud2009">Pelachaud, C. (2009). Modelling multimodal expression of emotion in a virtual agent. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1535), 3539–3548. https://doi.org/10.1098/rstb.2009.0186</ref> Examples are given in this paper as well, see for instance figure 5 or 6. <br />
<br />
* Communication features like nonverbal cues seem to be crucial in maintaining learning motivation in virtual learning environments, probably because they inform the observer about states, involvement, responsiveness, and understanding. <ref name = "shiban2015"/><br />
<br />
* Deictic gestures (pointing towards something, indicating directions, objects etc) have been shown to guide attention, especially when the agent is static. <ref name = "shiban2015"/><br />
<br />
* A paper by Grover et al. <ref name = "grover2020"/> described an experiment in which two chatbots were compared, one of which has a face and appeared to be more emotionally intelligent. Results suggested that the emotionally expressive agent can lead participants (average age 33) to be more productive and focused during working hours. The participants also reported to feel more satisfied with their achievements.<br />
<br />
* A study by Go and Sundar <ref name="go2019"/> states that revealing the identity of a chatbot as a non-human can have a positive effect: user will have less high expectations about the conversation, and will be impressed when an agent shows human-like behaviour. Furthermore, they emphasize the importance of the conversational style between a human an a computer. When the dialogue resembles that of an actual human, perceived feelings of social presence and homophily will increase, leading to more positive attitudes towards the agent (and in turn potential desired behaviour consequences).<br />
<br />
=====Personality=====<br />
<br />
* Higher feelings of social presence can be achieved when an agent’s language usage shows a consistent personality, which can be either introvert or extravert. <ref name="sinatra2021"/><br />
<br />
* As we pointed out in Section 2, education and customer services are the task-oriented domains most reported in the literature. We found conscientiousness, damage control, thoroughness, manners, emotional intelligence, and identity in studies for both domains. However, manners and emotional intelligence have a different goal in these domains. In the education context, these characteristics are designed to encourage students, especially in a situation of failure, in which the chatbot should be comforting and sensitive. This function aligns with other domains, such as health-care. The education domain also reports needs for personality, so the chatbot can be recognized as either an instructor or a student, and proactivity, so the chatbot can motivate students to participate in the interactions. Personality is also reported in other domains in which the chatbots’ character influences the interactions, such as gaming and humorous talk. Proactivity is also consistently reported in domains in which the chatbot provides guidance, such as coaching, health, ethnography, and assessment interviews. <ref name="chaves2021"/><br />
<br />
====Language====<br />
<br />
* Virtual agents which are communicating in a personalized way (using “I” and “you”) will behave more human-like and it will therefore gain more social fidelity. It will also lead to increased feelings of social presence and better learning performance and motivation. <ref name="sinatra2021"/><ref name="picciano2002">Picciano, A. G. (2002). BEYOND STUDENT PERCEPTIONS: ISSUES OF INTERACTION, PRESENCE, AND PERFORMANCE IN AN ONLINE COURSE. In JALN (Vol. 6, Issue 1).</ref><br />
<br />
* The previous statement is supported by Araujo <ref name="araujo2018">Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189. https://doi.org/10.1016/j.chb.2018.03.051</ref>. In his paper, he showed that social presence increased when the machine shows a more intelligent interaction style. <br />
<br />
* Elaborative feedback and polite conversation has been shown to have a positive influence on performance. Furthermore, compliments (for correct answers) can encourage intrinsic motivation by positively influencing feelings of competence, self-control, self-efficacy and curiosity.<br />
<br />
* As Emoji use in text has been shown to strengthen the perceived affect of a message (either in a positive or negative direction) compared to the same text without accompanying emojis [32], we saw incorporating emojis into the VA prototype as an easy first step towards introducing more emotional expressiveness. <ref name="grover2020"/><br />
<br />
* Recent research also suggests that the psychological benefits of disclosure and reflection with an agent are similar to reflection with another human [13]. Therefore, we considered the ability for users to reflect on their feelings and sense of productivity to be a beneficial final extra feature in the VA prototype. After users reported how they were feeling during the morning dialogue, they were asked to reflect upon their feelings in an open-ended response. <ref name="grover2020"/><br />
<br />
* The study concludes that the effects of flattery from a computer can produce the same general effects as flattery from humans, as described in the psychology literature. These findings may suggest significant implications for the design of interactive technologies. <ref name="fogg1997">Fogg, B. J., & Nass, C. (1997). Silicon sycophants: The effects of computers that flatter. International Journal of Human Computer Studies, 46(5), 551–561. https://doi.org/10.1006/ijhc.1996.0104</ref><br />
<br />
===References===<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Related_Literature_Group_4,_design&diff=115216Related Literature Group 4, design2021-05-09T19:29:23Z<p>20182838: /* Language */</p>
<hr />
<div><div style="font-family: 'Calibri'; font-size: 15px; line-height: 1.5; max-width: 1100px; word-wrap: break-word; color: #333; font-weight: 400; margin-left: auto; margin-right: auto; padding: 50px; background-color: white; padding-top: 25px;"><br />
<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Excerpts & citations </font><br />
<br />
====Gender====<br />
<br />
* Females chose to gender-match and to interact with a more realistic VA. Males exhibited little preference for either gender, and a greater preference than females for realistic VAs. Thus, where it is not feasible to gender-match in SSCO, the recommendation is to implement a realistic female VA. <ref name="payne2013">Payne, J., Szymkowiak, A., Robertson, P., & Johnson, G. (2013). Gendering the machine: Preferred virtual assistant gender and realism in self-service. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8108 LNAI, 106–115. https://doi.org/10.1007/978-3-642-40415-3_9</ref><br />
<br />
* Young and attractive female agent positively impacts interest in learning, an older an unattractive male agent does not impact motivation. <ref name="shiban2015">Shiban, Y., Schelhorn, I., Jobst, V., Hörnlein, A., Puppe, F., Pauli, P., & Mühlberger, A. (2015). The appearance effect: Influences of virtual agent features on performance and motivation. Computers in Human Behavior, 49, 5–11. https://doi.org/10.1016/j.chb.2015.01.077</ref><br />
<br />
* As research suggests that the combination of an agent’s gender and personality can play an important role in user perceptions and expectations [1, 37], employing a male agent instead may result in some significant differences in user perceptions or ratings of the agent. We encourage future work to investigate how gender and personality of a workplace productivity agent might influence user experience. <ref name="grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref><br />
<br />
* participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
* We found that the designed characteristics of VAs affects some aspects of user impressions (i.e. personality and trustworthiness) of the VA, while other impressions are not affected (i.e. social ability). We also found that gender matching between the agent and the user affect user impressions. // Gender similarity --> more trustworthy <ref name="akbar2018">Akbar, F., Grover, T., Mark, G., & Zhou, M. X. (2018, March 5). The effects of virtual agents’ characteristics on user impressions and language use. International Conference on Intelligent User Interfaces, Proceedings IUI. https://doi.org/10.1145/3180308.3180365</ref><br />
<br />
* In addition, the similarity of an agent to the learner positively influences the learner’s motivation (Bailenson, Blascovich, & Guadagno, 2008 in a study with undergraduates). For example, computer-based female agents yielded better motivational outcomes for undergraduate women if they matched the students with respect to race and gender (Rosenberg-Kima, Plant, Doerr, & Baylor, 2010). Another study, conducted with undergraduates by Rosenberg-Kima, Baylor, Plant, and Doerr (2008) revealed that a female agent rated as young, attractive and “cool” succeeded in enhancing young female students’ self-efficacy, which is believed to be a driving force behind motivation (Bandura, 1997). All these findings are theoretically supported by Bandura’s social cognitive learning theory which states that people often learn behavior and norms by imitating people whom they perceive as similar (or superior: higher in rank or status) to them and who are therefore rather accepted as social role models (Bandura, 1986). This finding is supported by another study of Gulz, Haake, and Tärning (2007) which demonstrated that participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
====Appearance and human-likeness====<br />
<br />
* In this study, it was noted that PIAs’ human-like features could influence the manner in which participants (i.e. males or females) related to a particular PIA and participants’ preference level of a particular PIA. The result of the Fischer’s exact test conducted in this study (Table V) found no statistically significant effect between participants’ gender (male or female) and their preferred gender of PIAs. It is concluded that the gender of participants (i.e. male or female) has no role (impact) on their preferences regarding the type, features, or gender of PIA, neither on their preference level of PIA. <ref name="mabanza2019">Mabanza, N. (2019). Gender influences on preference of pedagogical interface agents. 2018 International Conference on Intelligent and Innovative Computing Applications, ICONIC 2018. https://doi.org/10.1109/ICONIC.2018.8601292</ref><br />
<br />
* On the other hand, previous studies have also shown that developing overly humanized agents results in high expectations and uncanny feelings. <ref name="chaves2021">Chaves, A. P., & Gerosa, M. A. (2021). How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Desi | Enhanced Reader. International Journal of Human–Computer Interaction, 37(8), 729–758. https://doi.org/10.1080/10447318.2020.1841438</ref><br />
<br />
* Many participants saw the human-like appearance of the VA prototype as setting the wrong expectations in terms of its capabilities, and they were disappointed when the agent’s intelligence only extended towards responding to the their emotion and prompting more self-reflection. (…) We believe incorporating human-like qualities and emotional intelligence into future agents to be worthwhile; however, intelligence should also extend into other aspects of the agent’s capabilities in order to better help users be as efficient as possible in achieving their work goals. <ref name="grover2020"/><br />
<br />
* A study by Go and Sundar <ref name="go2019">Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j.chb.2019.01.020</ref> states that revealing the identity of a chatbot as a non-human can have a positive effect: user will have less high expectations about the conversation, and will be impressed when an agent shows human-like behaviour. Furthermore, they emphasize the importance of the conversational style between a human an a computer. When the dialogue resembles that of an actual human, perceived feelings of social presence and homophily will increase, leading to more positive attitudes towards the agent (and in turn potential desired behaviour consequences).<br />
<br />
* the presence of a representation produced more positive social interactions than not having a representation <ref name="Yee2017">Yee, N., Bailenson, J. N., & Rickertsen, K. (2007). A Meta-Analysis of the Impact of the Inclusion and Realism of Human-Like Faces on User Experiences in Interfaces.</ref><br />
** human-like representations with higher realism produced more positive social interactions than representations with lower realism; however, this effect was only found when subjective measures were used. Behavioral measures did not reveal a significant difference between representations of low and high realism. <br />
*** the difference we found may also be driven by demand characteristics. Participants interacting with an animated character (as opposed to a photograph) may suppose that the researcher is expecting a high appraisal.<br />
**while the presence of a face is better than no face at all, the quality of the face matters much less.<br />
***it is quite possible that animating highly realistic faces inherently allows for residual attributes of the faces that are negative—for example making 3D human faces may produce gestures and animations that appear unnatural or disturbing<br />
**while most studies have found that interface agents have positive effects on task performance, these effects are overall actually quite small.<br />
<br />
* In addition to understanding human social behavior around computers, another extensive line of work examines humans’ responses to computers with more expressive, human-like qualities, such as faces and facial expressions. In general, these studies have found that anthropomorphic properties of computers influence users’ perceptions (e.g., [13, 70, 86]), attitude (e.g., [13]), and behavior around such systems (e.g., [48, 86, 105]). For example, Sproull et al. [86] report that people respond to a text-based interface differently than to a talking face. On the one hand, users are aroused more and present themselves more positively when interacting with a talking face. On the other hand, users are less relaxed or assured when interacting with a talking face. Another study shows that users find the interface with the anthropomorphic qualities—faces and facial expressions—more likeable and engaging, although such an interface takes the users’ effort to interpret the meaning of the human-like expressions and may even be a distraction [48, 86]. More recent studies show that human-like features with higher realism elicit more positive social interactions while having no significant impact on user task performance [105]. Furthermore, a study reveals that anthropomorphism may even elicit user objections due to users’ own biases (e.g., sexism) [70]. <ref name="zhou2019">Zhou, M. X., Yang, H., Mark, G., & Li, J. (2019). Trusting Virtual Agents: The Effect of Personality. ACM Trans. Interact. Intell. Syst, 9(3). https://doi.org/10.1145/3232077</ref><br />
<br />
* Physically present agent leads to better motivational outcomes than a voice or text-box. It is important to design agents realistically, because e.g. cartoon figures have been shown to diminish the positive motivating effects in comparison to realistic figures. <ref name="shiban2015"/><br />
<br />
* It was found also that attractive VT did not improve perceived value of exercise and perceived risks of health. This result is different from some prior studies (e.g. Shiban et al., 2015) which find that attractive virtual pedagogical agents are effective for engaging students in learning. There are two possible explanations for this observed difference. First, attractive virtual agents may catch the attention of users and make users more confident in using a VTS. However, they may also distract users from core learning materials (Moreno and Flowerday, 2006). Second, attractive agents are closer to friends rather than experts. The incongruence of expert perception may lead to contextual irrelevance which makes users take health-related information from virtual trainers lightly (Veletsianos, 2010). <ref name="kwok2021">Kwok, R. C. W., Leung, A. C. M., Hui, S. S. chuen, & Wong, C. C. K. (2021). Virtual trainer system: a tool to increase exercise participation and work productivity. Internet Research. https://doi.org/10.1108/INTR-04-2020-0236</ref><br />
<br />
* If McCloud’s (1993) framework is applied, a teacher character, representing the other to a higher extent than a learning companion, might benefit from more realism in the representation. A learning companion character, being to a higher extent conceived of as an extension of oneself, may, on the other hand, benefit from a more iconic representation. <ref name="gulz2006">Gulz, A., & Haake, M. (2006). Design of animated pedagogical agents - A look at their look. International Journal of Human Computer Studies, 64(4), 322–339. https://doi.org/10.1016/j.ijhcs.2005.08.006</ref><br />
<br />
* The findings of this experiment suggest a new design method for human-agent collaboration work. If we use an agent having a non-human-like appearance (for example, the robot-like agent), the user seemed to attribute much responsibility to the agent and not to trust the agent. We suggest using the agent having human-like appearance in human-agent collaboration work from this experiment. A question is raised about the commonly accepted beliefs about agent design for social tasks. We should consider the attribution of responsibility to construct trustworthy agents. <ref name="matsui2021">Matsui, T., & Koike, A. (2021). Who is to blame? The appearance of virtual agents and the attribution of perceived responsibility. Sensors, 21(8), 1–13. https://doi.org/10.3390/s21082646</ref><br />
<br />
====Behaviour and personality====<br />
<br />
* It is possible to program a chatbot in a way that it will interact with you via a moving and talking avatar. The paper also suggests that displaying facial expressions are beneficial in the interaction with such an agent, since displaying the avatar’s emotion can enhance perceived emotional intelligence. <ref name="angga2016">Angga, P. A., Fachri, W. E., Elevanita, A., Suryadi, & Agushinta, R. D. (2016). Design of chatbot with 3D avatar, voice interface, and facial expression. Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015, 326–330. https://doi.org/10.1109/ICSITech.2015.7407826</ref><br />
<br />
* Looije et al researched the guidelines that are needed when developing a personal assistant. These guidelines were derived from interviewing, persuasive technologies and from existing guidelines for personal assistants. In their research they found that guidelines were best expressed in iCat (a personal assistant) that was able to show socially intelligent behavior compared to a non-social or text interface based iCat. <ref name="looije2006">Looije, R., Cnossen, F., & Neerincx, M. A. (2006). Incorporating guidelines for health assistance into a socially intelligent robot. Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, 515–520. https://doi.org/10.1109/ROMAN.2006.314441</ref><br />
<br />
* “The results suggested that it is possible to express extroversion and confidence in the agent by changing the agent's gaze amount regardless of the agent's embodiment. <ref name="koda2018">Koda, T., & Ishioh, T. (2018). Analysis of the effect of agent’s embodiment and gaze amount on personality perception. Proceedings of the 4th Workshop on Multimodal Analyses Enabling Artificial Agents in Human-Machine Interaction, MA3HMI 2018 - In Conjunction with ICMI 2018, 1–5. https://doi.org/10.1145/3279972.3279973</ref><br />
<br />
* Social fidelity is related to certain human-like behaviour and cues. This includes for example speech content (personalized language, feedback, politeness, social memory, personality) and visual cues (facial expressions, gestures, gaze, emotions). <ref name="sinatra2021">Sinatra, A. M., Pollard, K. A., Files, B. T., Oiknine, A. H., Ericson, M., & Khooshabeh, P. (2021). Social fidelity in virtual agents: Impacts on presence and learning. Computers in Human Behavior, 114, 106562. https://doi.org/10.1016/j.chb.2020.106562</ref><br />
<br />
* An expressive emotionally intelligent VA is perceived as more emotionally intelligent when expressing itself through words, tone, body language, and facial expressions. <ref name="ma2019">Ma, X., Yang, E. Y., & Fung, P. (2019). Exploring Perceived Emotional Intelligence of Personality-Driven Virtual Agents in Handling User Challenges. https://doi.org/10.1145/3308558.3313400</ref><br />
<br />
* “Expressions are no more defined by a static representation; rather they are constituted as a succession of signals that appear dynamically. Using few expressions limits the interaction. Endowing agents with large variety of expressions ensures more naturalness in the agent’s behaviour.” <ref name="pelachaud2009">Pelachaud, C. (2009). Modelling multimodal expression of emotion in a virtual agent. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1535), 3539–3548. https://doi.org/10.1098/rstb.2009.0186</ref> Examples are given in this paper as well, see for instance figure 5 or 6. <br />
<br />
* Communication features like nonverbal cues seem to be crucial in maintaining learning motivation in virtual learning environments, probably because they inform the observer about states, involvement, responsiveness, and understanding. <ref name = "shiban2015"/><br />
<br />
* Deictic gestures (pointing towards something, indicating directions, objects etc) have been shown to guide attention, especially when the agent is static. <ref name = "shiban2015"/><br />
<br />
* A paper by Grover et al. <ref name = "grover2020"/> described an experiment in which two chatbots were compared, one of which has a face and appeared to be more emotionally intelligent. Results suggested that the emotionally expressive agent can lead participants (average age 33) to be more productive and focused during working hours. The participants also reported to feel more satisfied with their achievements.<br />
<br />
* A study by Go and Sundar <ref name="go2019"/> states that revealing the identity of a chatbot as a non-human can have a positive effect: user will have less high expectations about the conversation, and will be impressed when an agent shows human-like behaviour. Furthermore, they emphasize the importance of the conversational style between a human an a computer. When the dialogue resembles that of an actual human, perceived feelings of social presence and homophily will increase, leading to more positive attitudes towards the agent (and in turn potential desired behaviour consequences).<br />
<br />
=====Personality=====<br />
<br />
* Higher feelings of social presence can be achieved when an agent’s language usage shows a consistent personality, which can be either introvert or extravert. <ref name="sinatra2021"/><br />
<br />
* As we pointed out in Section 2, education and customer services are the task-oriented domains most reported in the literature. We found conscientiousness, damage control, thoroughness, manners, emotional intelligence, and identity in studies for both domains. However, manners and emotional intelligence have a different goal in these domains. In the education context, these characteristics are designed to encourage students, especially in a situation of failure, in which the chatbot should be comforting and sensitive. This function aligns with other domains, such as health-care. The education domain also reports needs for personality, so the chatbot can be recognized as either an instructor or a student, and proactivity, so the chatbot can motivate students to participate in the interactions. Personality is also reported in other domains in which the chatbots’ character influences the interactions, such as gaming and humorous talk. Proactivity is also consistently reported in domains in which the chatbot provides guidance, such as coaching, health, ethnography, and assessment interviews. <ref name="chaves2021"/><br />
<br />
====Language====<br />
<br />
* Virtual agents which are communicating in a personalized way (using “I” and “you”) will behave more human-like and it will therefore gain more social fidelity. It will also lead to increased feelings of social presence and better learning performance and motivation. <ref name="sinatra2021"/><ref name="picciano2002">Picciano, A. G. (2002). BEYOND STUDENT PERCEPTIONS: ISSUES OF INTERACTION, PRESENCE, AND PERFORMANCE IN AN ONLINE COURSE. In JALN (Vol. 6, Issue 1).</ref><br />
<br />
* The previous statement is supported by Araujo <ref name="araujo2018">Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189. https://doi.org/10.1016/j.chb.2018.03.051</ref>. In his paper, he showed that social presence increased when the machine shows a more intelligent interaction style. <br />
<br />
* Elaborative feedback and polite conversation has been shown to have a positive influence on performance. Furthermore, compliments (for correct answers) can encourage intrinsic motivation by positively influencing feelings of competence, self-control, self-efficacy and curiosity.<br />
<br />
* As Emoji use in text has been shown to strengthen the perceived affect of a message (either in a positive or negative direction) compared to the same text without accompanying emojis [32], we saw incorporating emojis into the VA prototype as an easy first step towards introducing more emotional expressiveness. <ref name="grover2020"/><br />
<br />
* Recent research also suggests that the psychological benefits of disclosure and reflection with an agent are similar to reflection with another human [13]. Therefore, we considered the ability for users to reflect on their feelings and sense of productivity to be a beneficial final extra feature in the VA prototype. After users reported how they were feeling during the morning dialogue, they were asked to reflect upon their feelings in an open-ended response. <ref name="grover2020"/><br />
<br />
* The study concludes that the effects of flattery from a computer can produce the same general effects as flattery from humans, as described in the psychology literature. These findings may suggest significant implications for the design of interactive technologies. <ref name="fogg1997">Fogg, B. J., & Nass, C. (1997). Silicon sycophants: The effects of computers that flatter. International Journal of Human Computer Studies, 46(5), 551–561. https://doi.org/10.1006/ijhc.1996.0104</ref><br />
<br />
===References===<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Related_Literature_Group_4,_design&diff=115215Related Literature Group 4, design2021-05-09T19:28:37Z<p>20182838: /* Behaviour and personality */</p>
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<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Excerpts & citations </font><br />
<br />
====Gender====<br />
<br />
* Females chose to gender-match and to interact with a more realistic VA. Males exhibited little preference for either gender, and a greater preference than females for realistic VAs. Thus, where it is not feasible to gender-match in SSCO, the recommendation is to implement a realistic female VA. <ref name="payne2013">Payne, J., Szymkowiak, A., Robertson, P., & Johnson, G. (2013). Gendering the machine: Preferred virtual assistant gender and realism in self-service. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8108 LNAI, 106–115. https://doi.org/10.1007/978-3-642-40415-3_9</ref><br />
<br />
* Young and attractive female agent positively impacts interest in learning, an older an unattractive male agent does not impact motivation. <ref name="shiban2015">Shiban, Y., Schelhorn, I., Jobst, V., Hörnlein, A., Puppe, F., Pauli, P., & Mühlberger, A. (2015). The appearance effect: Influences of virtual agent features on performance and motivation. Computers in Human Behavior, 49, 5–11. https://doi.org/10.1016/j.chb.2015.01.077</ref><br />
<br />
* As research suggests that the combination of an agent’s gender and personality can play an important role in user perceptions and expectations [1, 37], employing a male agent instead may result in some significant differences in user perceptions or ratings of the agent. We encourage future work to investigate how gender and personality of a workplace productivity agent might influence user experience. <ref name="grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref><br />
<br />
* participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
* We found that the designed characteristics of VAs affects some aspects of user impressions (i.e. personality and trustworthiness) of the VA, while other impressions are not affected (i.e. social ability). We also found that gender matching between the agent and the user affect user impressions. // Gender similarity --> more trustworthy <ref name="akbar2018">Akbar, F., Grover, T., Mark, G., & Zhou, M. X. (2018, March 5). The effects of virtual agents’ characteristics on user impressions and language use. International Conference on Intelligent User Interfaces, Proceedings IUI. https://doi.org/10.1145/3180308.3180365</ref><br />
<br />
* In addition, the similarity of an agent to the learner positively influences the learner’s motivation (Bailenson, Blascovich, & Guadagno, 2008 in a study with undergraduates). For example, computer-based female agents yielded better motivational outcomes for undergraduate women if they matched the students with respect to race and gender (Rosenberg-Kima, Plant, Doerr, & Baylor, 2010). Another study, conducted with undergraduates by Rosenberg-Kima, Baylor, Plant, and Doerr (2008) revealed that a female agent rated as young, attractive and “cool” succeeded in enhancing young female students’ self-efficacy, which is believed to be a driving force behind motivation (Bandura, 1997). All these findings are theoretically supported by Bandura’s social cognitive learning theory which states that people often learn behavior and norms by imitating people whom they perceive as similar (or superior: higher in rank or status) to them and who are therefore rather accepted as social role models (Bandura, 1986). This finding is supported by another study of Gulz, Haake, and Tärning (2007) which demonstrated that participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
====Appearance and human-likeness====<br />
<br />
* In this study, it was noted that PIAs’ human-like features could influence the manner in which participants (i.e. males or females) related to a particular PIA and participants’ preference level of a particular PIA. The result of the Fischer’s exact test conducted in this study (Table V) found no statistically significant effect between participants’ gender (male or female) and their preferred gender of PIAs. It is concluded that the gender of participants (i.e. male or female) has no role (impact) on their preferences regarding the type, features, or gender of PIA, neither on their preference level of PIA. <ref name="mabanza2019">Mabanza, N. (2019). Gender influences on preference of pedagogical interface agents. 2018 International Conference on Intelligent and Innovative Computing Applications, ICONIC 2018. https://doi.org/10.1109/ICONIC.2018.8601292</ref><br />
<br />
* On the other hand, previous studies have also shown that developing overly humanized agents results in high expectations and uncanny feelings. <ref name="chaves2021">Chaves, A. P., & Gerosa, M. A. (2021). How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Desi | Enhanced Reader. International Journal of Human–Computer Interaction, 37(8), 729–758. https://doi.org/10.1080/10447318.2020.1841438</ref><br />
<br />
* Many participants saw the human-like appearance of the VA prototype as setting the wrong expectations in terms of its capabilities, and they were disappointed when the agent’s intelligence only extended towards responding to the their emotion and prompting more self-reflection. (…) We believe incorporating human-like qualities and emotional intelligence into future agents to be worthwhile; however, intelligence should also extend into other aspects of the agent’s capabilities in order to better help users be as efficient as possible in achieving their work goals. <ref name="grover2020"/><br />
<br />
* A study by Go and Sundar <ref name="go2019">Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j.chb.2019.01.020</ref> states that revealing the identity of a chatbot as a non-human can have a positive effect: user will have less high expectations about the conversation, and will be impressed when an agent shows human-like behaviour. Furthermore, they emphasize the importance of the conversational style between a human an a computer. When the dialogue resembles that of an actual human, perceived feelings of social presence and homophily will increase, leading to more positive attitudes towards the agent (and in turn potential desired behaviour consequences).<br />
<br />
* the presence of a representation produced more positive social interactions than not having a representation <ref name="Yee2017">Yee, N., Bailenson, J. N., & Rickertsen, K. (2007). A Meta-Analysis of the Impact of the Inclusion and Realism of Human-Like Faces on User Experiences in Interfaces.</ref><br />
** human-like representations with higher realism produced more positive social interactions than representations with lower realism; however, this effect was only found when subjective measures were used. Behavioral measures did not reveal a significant difference between representations of low and high realism. <br />
*** the difference we found may also be driven by demand characteristics. Participants interacting with an animated character (as opposed to a photograph) may suppose that the researcher is expecting a high appraisal.<br />
**while the presence of a face is better than no face at all, the quality of the face matters much less.<br />
***it is quite possible that animating highly realistic faces inherently allows for residual attributes of the faces that are negative—for example making 3D human faces may produce gestures and animations that appear unnatural or disturbing<br />
**while most studies have found that interface agents have positive effects on task performance, these effects are overall actually quite small.<br />
<br />
* In addition to understanding human social behavior around computers, another extensive line of work examines humans’ responses to computers with more expressive, human-like qualities, such as faces and facial expressions. In general, these studies have found that anthropomorphic properties of computers influence users’ perceptions (e.g., [13, 70, 86]), attitude (e.g., [13]), and behavior around such systems (e.g., [48, 86, 105]). For example, Sproull et al. [86] report that people respond to a text-based interface differently than to a talking face. On the one hand, users are aroused more and present themselves more positively when interacting with a talking face. On the other hand, users are less relaxed or assured when interacting with a talking face. Another study shows that users find the interface with the anthropomorphic qualities—faces and facial expressions—more likeable and engaging, although such an interface takes the users’ effort to interpret the meaning of the human-like expressions and may even be a distraction [48, 86]. More recent studies show that human-like features with higher realism elicit more positive social interactions while having no significant impact on user task performance [105]. Furthermore, a study reveals that anthropomorphism may even elicit user objections due to users’ own biases (e.g., sexism) [70]. <ref name="zhou2019">Zhou, M. X., Yang, H., Mark, G., & Li, J. (2019). Trusting Virtual Agents: The Effect of Personality. ACM Trans. Interact. Intell. Syst, 9(3). https://doi.org/10.1145/3232077</ref><br />
<br />
* Physically present agent leads to better motivational outcomes than a voice or text-box. It is important to design agents realistically, because e.g. cartoon figures have been shown to diminish the positive motivating effects in comparison to realistic figures. <ref name="shiban2015"/><br />
<br />
* It was found also that attractive VT did not improve perceived value of exercise and perceived risks of health. This result is different from some prior studies (e.g. Shiban et al., 2015) which find that attractive virtual pedagogical agents are effective for engaging students in learning. There are two possible explanations for this observed difference. First, attractive virtual agents may catch the attention of users and make users more confident in using a VTS. However, they may also distract users from core learning materials (Moreno and Flowerday, 2006). Second, attractive agents are closer to friends rather than experts. The incongruence of expert perception may lead to contextual irrelevance which makes users take health-related information from virtual trainers lightly (Veletsianos, 2010). <ref name="kwok2021">Kwok, R. C. W., Leung, A. C. M., Hui, S. S. chuen, & Wong, C. C. K. (2021). Virtual trainer system: a tool to increase exercise participation and work productivity. Internet Research. https://doi.org/10.1108/INTR-04-2020-0236</ref><br />
<br />
* If McCloud’s (1993) framework is applied, a teacher character, representing the other to a higher extent than a learning companion, might benefit from more realism in the representation. A learning companion character, being to a higher extent conceived of as an extension of oneself, may, on the other hand, benefit from a more iconic representation. <ref name="gulz2006">Gulz, A., & Haake, M. (2006). Design of animated pedagogical agents - A look at their look. International Journal of Human Computer Studies, 64(4), 322–339. https://doi.org/10.1016/j.ijhcs.2005.08.006</ref><br />
<br />
* The findings of this experiment suggest a new design method for human-agent collaboration work. If we use an agent having a non-human-like appearance (for example, the robot-like agent), the user seemed to attribute much responsibility to the agent and not to trust the agent. We suggest using the agent having human-like appearance in human-agent collaboration work from this experiment. A question is raised about the commonly accepted beliefs about agent design for social tasks. We should consider the attribution of responsibility to construct trustworthy agents. <ref name="matsui2021">Matsui, T., & Koike, A. (2021). Who is to blame? The appearance of virtual agents and the attribution of perceived responsibility. Sensors, 21(8), 1–13. https://doi.org/10.3390/s21082646</ref><br />
<br />
====Behaviour and personality====<br />
<br />
* It is possible to program a chatbot in a way that it will interact with you via a moving and talking avatar. The paper also suggests that displaying facial expressions are beneficial in the interaction with such an agent, since displaying the avatar’s emotion can enhance perceived emotional intelligence. <ref name="angga2016">Angga, P. A., Fachri, W. E., Elevanita, A., Suryadi, & Agushinta, R. D. (2016). Design of chatbot with 3D avatar, voice interface, and facial expression. Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015, 326–330. https://doi.org/10.1109/ICSITech.2015.7407826</ref><br />
<br />
* Looije et al researched the guidelines that are needed when developing a personal assistant. These guidelines were derived from interviewing, persuasive technologies and from existing guidelines for personal assistants. In their research they found that guidelines were best expressed in iCat (a personal assistant) that was able to show socially intelligent behavior compared to a non-social or text interface based iCat. <ref name="looije2006">Looije, R., Cnossen, F., & Neerincx, M. A. (2006). Incorporating guidelines for health assistance into a socially intelligent robot. Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, 515–520. https://doi.org/10.1109/ROMAN.2006.314441</ref><br />
<br />
* “The results suggested that it is possible to express extroversion and confidence in the agent by changing the agent's gaze amount regardless of the agent's embodiment. <ref name="koda2018">Koda, T., & Ishioh, T. (2018). Analysis of the effect of agent’s embodiment and gaze amount on personality perception. Proceedings of the 4th Workshop on Multimodal Analyses Enabling Artificial Agents in Human-Machine Interaction, MA3HMI 2018 - In Conjunction with ICMI 2018, 1–5. https://doi.org/10.1145/3279972.3279973</ref><br />
<br />
* Social fidelity is related to certain human-like behaviour and cues. This includes for example speech content (personalized language, feedback, politeness, social memory, personality) and visual cues (facial expressions, gestures, gaze, emotions). <ref name="sinatra2021">Sinatra, A. M., Pollard, K. A., Files, B. T., Oiknine, A. H., Ericson, M., & Khooshabeh, P. (2021). Social fidelity in virtual agents: Impacts on presence and learning. Computers in Human Behavior, 114, 106562. https://doi.org/10.1016/j.chb.2020.106562</ref><br />
<br />
* An expressive emotionally intelligent VA is perceived as more emotionally intelligent when expressing itself through words, tone, body language, and facial expressions. <ref name="ma2019">Ma, X., Yang, E. Y., & Fung, P. (2019). Exploring Perceived Emotional Intelligence of Personality-Driven Virtual Agents in Handling User Challenges. https://doi.org/10.1145/3308558.3313400</ref><br />
<br />
* “Expressions are no more defined by a static representation; rather they are constituted as a succession of signals that appear dynamically. Using few expressions limits the interaction. Endowing agents with large variety of expressions ensures more naturalness in the agent’s behaviour.” <ref name="pelachaud2009">Pelachaud, C. (2009). Modelling multimodal expression of emotion in a virtual agent. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1535), 3539–3548. https://doi.org/10.1098/rstb.2009.0186</ref> Examples are given in this paper as well, see for instance figure 5 or 6. <br />
<br />
* Communication features like nonverbal cues seem to be crucial in maintaining learning motivation in virtual learning environments, probably because they inform the observer about states, involvement, responsiveness, and understanding. <ref name = "shiban2015"/><br />
<br />
* Deictic gestures (pointing towards something, indicating directions, objects etc) have been shown to guide attention, especially when the agent is static. <ref name = "shiban2015"/><br />
<br />
* A paper by Grover et al. <ref name = "grover2020"/> described an experiment in which two chatbots were compared, one of which has a face and appeared to be more emotionally intelligent. Results suggested that the emotionally expressive agent can lead participants (average age 33) to be more productive and focused during working hours. The participants also reported to feel more satisfied with their achievements.<br />
<br />
* A study by Go and Sundar <ref name="go2019"/> states that revealing the identity of a chatbot as a non-human can have a positive effect: user will have less high expectations about the conversation, and will be impressed when an agent shows human-like behaviour. Furthermore, they emphasize the importance of the conversational style between a human an a computer. When the dialogue resembles that of an actual human, perceived feelings of social presence and homophily will increase, leading to more positive attitudes towards the agent (and in turn potential desired behaviour consequences).<br />
<br />
=====Personality=====<br />
<br />
* Higher feelings of social presence can be achieved when an agent’s language usage shows a consistent personality, which can be either introvert or extravert. <ref name="sinatra2021"/><br />
<br />
* As we pointed out in Section 2, education and customer services are the task-oriented domains most reported in the literature. We found conscientiousness, damage control, thoroughness, manners, emotional intelligence, and identity in studies for both domains. However, manners and emotional intelligence have a different goal in these domains. In the education context, these characteristics are designed to encourage students, especially in a situation of failure, in which the chatbot should be comforting and sensitive. This function aligns with other domains, such as health-care. The education domain also reports needs for personality, so the chatbot can be recognized as either an instructor or a student, and proactivity, so the chatbot can motivate students to participate in the interactions. Personality is also reported in other domains in which the chatbots’ character influences the interactions, such as gaming and humorous talk. Proactivity is also consistently reported in domains in which the chatbot provides guidance, such as coaching, health, ethnography, and assessment interviews. <ref name="chaves2021"/><br />
<br />
====Language====<br />
<br />
* Virtual agents which are communicating in a personalized way (using “I” and “you”) will behave more human-like and it will therefore gain more social fidelity. It will also lead to increased feelings of social presence and better learning performance and motivation. <ref name="sinatra2021"/><ref name="">Picciano, A. G. (2002). BEYOND STUDENT PERCEPTIONS: ISSUES OF INTERACTION, PRESENCE, AND PERFORMANCE IN AN ONLINE COURSE. In JALN (Vol. 6, Issue 1).</ref><br />
<br />
* The previous statement is supported by Araujo <ref name="">Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189. https://doi.org/10.1016/j.chb.2018.03.051</ref>. In his paper, he showed that social presence increased when the machine shows a more intelligent interaction style. <br />
<br />
* Elaborative feedback and polite conversation has been shown to have a positive influence on performance. Furthermore, compliments (for correct answers) can encourage intrinsic motivation by positively influencing feelings of competence, self-control, self-efficacy and curiosity.<br />
<br />
* As Emoji use in text has been shown to strengthen the perceived affect of a message (either in a positive or negative direction) compared to the same text without accompanying emojis [32], we saw incorporating emojis into the VA prototype as an easy first step towards introducing more emotional expressiveness. <ref name="grover2020"/><br />
<br />
* Recent research also suggests that the psychological benefits of disclosure and reflection with an agent are similar to reflection with another human [13]. Therefore, we considered the ability for users to reflect on their feelings and sense of productivity to be a beneficial final extra feature in the VA prototype. After users reported how they were feeling during the morning dialogue, they were asked to reflect upon their feelings in an open-ended response. <ref name="grover2020"/><br />
<br />
* The study concludes that the effects of flattery from a computer can produce the same general effects as flattery from humans, as described in the psychology literature. These findings may suggest significant implications for the design of interactive technologies. <ref name="">Fogg, B. J., & Nass, C. (1997). Silicon sycophants: The effects of computers that flatter. International Journal of Human Computer Studies, 46(5), 551–561. https://doi.org/10.1006/ijhc.1996.0104</ref><br />
<br />
===References===<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Related_Literature_Group_4,_design&diff=115214Related Literature Group 4, design2021-05-09T19:21:43Z<p>20182838: /* Behaviour and personality */</p>
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<div><div style="font-family: 'Calibri'; font-size: 15px; line-height: 1.5; max-width: 1100px; word-wrap: break-word; color: #333; font-weight: 400; margin-left: auto; margin-right: auto; padding: 50px; background-color: white; padding-top: 25px;"><br />
<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Excerpts & citations </font><br />
<br />
====Gender====<br />
<br />
* Females chose to gender-match and to interact with a more realistic VA. Males exhibited little preference for either gender, and a greater preference than females for realistic VAs. Thus, where it is not feasible to gender-match in SSCO, the recommendation is to implement a realistic female VA. <ref name="payne2013">Payne, J., Szymkowiak, A., Robertson, P., & Johnson, G. (2013). Gendering the machine: Preferred virtual assistant gender and realism in self-service. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8108 LNAI, 106–115. https://doi.org/10.1007/978-3-642-40415-3_9</ref><br />
<br />
* Young and attractive female agent positively impacts interest in learning, an older an unattractive male agent does not impact motivation. <ref name="shiban2015">Shiban, Y., Schelhorn, I., Jobst, V., Hörnlein, A., Puppe, F., Pauli, P., & Mühlberger, A. (2015). The appearance effect: Influences of virtual agent features on performance and motivation. Computers in Human Behavior, 49, 5–11. https://doi.org/10.1016/j.chb.2015.01.077</ref><br />
<br />
* As research suggests that the combination of an agent’s gender and personality can play an important role in user perceptions and expectations [1, 37], employing a male agent instead may result in some significant differences in user perceptions or ratings of the agent. We encourage future work to investigate how gender and personality of a workplace productivity agent might influence user experience. <ref name="grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref><br />
<br />
* participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
* We found that the designed characteristics of VAs affects some aspects of user impressions (i.e. personality and trustworthiness) of the VA, while other impressions are not affected (i.e. social ability). We also found that gender matching between the agent and the user affect user impressions. // Gender similarity --> more trustworthy <ref name="akbar2018">Akbar, F., Grover, T., Mark, G., & Zhou, M. X. (2018, March 5). The effects of virtual agents’ characteristics on user impressions and language use. International Conference on Intelligent User Interfaces, Proceedings IUI. https://doi.org/10.1145/3180308.3180365</ref><br />
<br />
* In addition, the similarity of an agent to the learner positively influences the learner’s motivation (Bailenson, Blascovich, & Guadagno, 2008 in a study with undergraduates). For example, computer-based female agents yielded better motivational outcomes for undergraduate women if they matched the students with respect to race and gender (Rosenberg-Kima, Plant, Doerr, & Baylor, 2010). Another study, conducted with undergraduates by Rosenberg-Kima, Baylor, Plant, and Doerr (2008) revealed that a female agent rated as young, attractive and “cool” succeeded in enhancing young female students’ self-efficacy, which is believed to be a driving force behind motivation (Bandura, 1997). All these findings are theoretically supported by Bandura’s social cognitive learning theory which states that people often learn behavior and norms by imitating people whom they perceive as similar (or superior: higher in rank or status) to them and who are therefore rather accepted as social role models (Bandura, 1986). This finding is supported by another study of Gulz, Haake, and Tärning (2007) which demonstrated that participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
====Appearance and human-likeness====<br />
<br />
* In this study, it was noted that PIAs’ human-like features could influence the manner in which participants (i.e. males or females) related to a particular PIA and participants’ preference level of a particular PIA. The result of the Fischer’s exact test conducted in this study (Table V) found no statistically significant effect between participants’ gender (male or female) and their preferred gender of PIAs. It is concluded that the gender of participants (i.e. male or female) has no role (impact) on their preferences regarding the type, features, or gender of PIA, neither on their preference level of PIA. <ref name="mabanza2019">Mabanza, N. (2019). Gender influences on preference of pedagogical interface agents. 2018 International Conference on Intelligent and Innovative Computing Applications, ICONIC 2018. https://doi.org/10.1109/ICONIC.2018.8601292</ref><br />
<br />
* On the other hand, previous studies have also shown that developing overly humanized agents results in high expectations and uncanny feelings. <ref name="chaves2021">Chaves, A. P., & Gerosa, M. A. (2021). How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Desi | Enhanced Reader. International Journal of Human–Computer Interaction, 37(8), 729–758. https://doi.org/10.1080/10447318.2020.1841438</ref><br />
<br />
* Many participants saw the human-like appearance of the VA prototype as setting the wrong expectations in terms of its capabilities, and they were disappointed when the agent’s intelligence only extended towards responding to the their emotion and prompting more self-reflection. (…) We believe incorporating human-like qualities and emotional intelligence into future agents to be worthwhile; however, intelligence should also extend into other aspects of the agent’s capabilities in order to better help users be as efficient as possible in achieving their work goals. <ref name="grover2020"/><br />
<br />
* A study by Go and Sundar <ref name="go2019">Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j.chb.2019.01.020</ref> states that revealing the identity of a chatbot as a non-human can have a positive effect: user will have less high expectations about the conversation, and will be impressed when an agent shows human-like behaviour. Furthermore, they emphasize the importance of the conversational style between a human an a computer. When the dialogue resembles that of an actual human, perceived feelings of social presence and homophily will increase, leading to more positive attitudes towards the agent (and in turn potential desired behaviour consequences).<br />
<br />
* the presence of a representation produced more positive social interactions than not having a representation <ref name="Yee2017">Yee, N., Bailenson, J. N., & Rickertsen, K. (2007). A Meta-Analysis of the Impact of the Inclusion and Realism of Human-Like Faces on User Experiences in Interfaces.</ref><br />
** human-like representations with higher realism produced more positive social interactions than representations with lower realism; however, this effect was only found when subjective measures were used. Behavioral measures did not reveal a significant difference between representations of low and high realism. <br />
*** the difference we found may also be driven by demand characteristics. Participants interacting with an animated character (as opposed to a photograph) may suppose that the researcher is expecting a high appraisal.<br />
**while the presence of a face is better than no face at all, the quality of the face matters much less.<br />
***it is quite possible that animating highly realistic faces inherently allows for residual attributes of the faces that are negative—for example making 3D human faces may produce gestures and animations that appear unnatural or disturbing<br />
**while most studies have found that interface agents have positive effects on task performance, these effects are overall actually quite small.<br />
<br />
* In addition to understanding human social behavior around computers, another extensive line of work examines humans’ responses to computers with more expressive, human-like qualities, such as faces and facial expressions. In general, these studies have found that anthropomorphic properties of computers influence users’ perceptions (e.g., [13, 70, 86]), attitude (e.g., [13]), and behavior around such systems (e.g., [48, 86, 105]). For example, Sproull et al. [86] report that people respond to a text-based interface differently than to a talking face. On the one hand, users are aroused more and present themselves more positively when interacting with a talking face. On the other hand, users are less relaxed or assured when interacting with a talking face. Another study shows that users find the interface with the anthropomorphic qualities—faces and facial expressions—more likeable and engaging, although such an interface takes the users’ effort to interpret the meaning of the human-like expressions and may even be a distraction [48, 86]. More recent studies show that human-like features with higher realism elicit more positive social interactions while having no significant impact on user task performance [105]. Furthermore, a study reveals that anthropomorphism may even elicit user objections due to users’ own biases (e.g., sexism) [70]. <ref name="zhou2019">Zhou, M. X., Yang, H., Mark, G., & Li, J. (2019). Trusting Virtual Agents: The Effect of Personality. ACM Trans. Interact. Intell. Syst, 9(3). https://doi.org/10.1145/3232077</ref><br />
<br />
* Physically present agent leads to better motivational outcomes than a voice or text-box. It is important to design agents realistically, because e.g. cartoon figures have been shown to diminish the positive motivating effects in comparison to realistic figures. <ref name="shiban2015"/><br />
<br />
* It was found also that attractive VT did not improve perceived value of exercise and perceived risks of health. This result is different from some prior studies (e.g. Shiban et al., 2015) which find that attractive virtual pedagogical agents are effective for engaging students in learning. There are two possible explanations for this observed difference. First, attractive virtual agents may catch the attention of users and make users more confident in using a VTS. However, they may also distract users from core learning materials (Moreno and Flowerday, 2006). Second, attractive agents are closer to friends rather than experts. The incongruence of expert perception may lead to contextual irrelevance which makes users take health-related information from virtual trainers lightly (Veletsianos, 2010). <ref name="kwok2021">Kwok, R. C. W., Leung, A. C. M., Hui, S. S. chuen, & Wong, C. C. K. (2021). Virtual trainer system: a tool to increase exercise participation and work productivity. Internet Research. https://doi.org/10.1108/INTR-04-2020-0236</ref><br />
<br />
* If McCloud’s (1993) framework is applied, a teacher character, representing the other to a higher extent than a learning companion, might benefit from more realism in the representation. A learning companion character, being to a higher extent conceived of as an extension of oneself, may, on the other hand, benefit from a more iconic representation. <ref name="gulz2006">Gulz, A., & Haake, M. (2006). Design of animated pedagogical agents - A look at their look. International Journal of Human Computer Studies, 64(4), 322–339. https://doi.org/10.1016/j.ijhcs.2005.08.006</ref><br />
<br />
* The findings of this experiment suggest a new design method for human-agent collaboration work. If we use an agent having a non-human-like appearance (for example, the robot-like agent), the user seemed to attribute much responsibility to the agent and not to trust the agent. We suggest using the agent having human-like appearance in human-agent collaboration work from this experiment. A question is raised about the commonly accepted beliefs about agent design for social tasks. We should consider the attribution of responsibility to construct trustworthy agents. <ref name="matsui2021">Matsui, T., & Koike, A. (2021). Who is to blame? The appearance of virtual agents and the attribution of perceived responsibility. Sensors, 21(8), 1–13. https://doi.org/10.3390/s21082646</ref><br />
<br />
====Behaviour and personality====<br />
<br />
* It is possible to program a chatbot in a way that it will interact with you via a moving and talking avatar. The paper also suggests that displaying facial expressions are beneficial in the interaction with such an agent, since displaying the avatar’s emotion can enhance perceived emotional intelligence. <ref name="angga2016">Angga, P. A., Fachri, W. E., Elevanita, A., Suryadi, & Agushinta, R. D. (2016). Design of chatbot with 3D avatar, voice interface, and facial expression. Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015, 326–330. https://doi.org/10.1109/ICSITech.2015.7407826</ref><br />
<br />
* Looije et al researched the guidelines that are needed when developing a personal assistant. These guidelines were derived from interviewing, persuasive technologies and from existing guidelines for personal assistants. In their research they found that guidelines were best expressed in iCat (a personal assistant) that was able to show socially intelligent behavior compared to a non-social or text interface based iCat. <ref name="looije2006">Looije, R., Cnossen, F., & Neerincx, M. A. (2006). Incorporating guidelines for health assistance into a socially intelligent robot. Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, 515–520. https://doi.org/10.1109/ROMAN.2006.314441</ref><br />
<br />
* “The results suggested that it is possible to express extroversion and confidence in the agent by changing the agent's gaze amount regardless of the agent's embodiment. <ref name="koda2018">Koda, T., & Ishioh, T. (2018). Analysis of the effect of agent’s embodiment and gaze amount on personality perception. Proceedings of the 4th Workshop on Multimodal Analyses Enabling Artificial Agents in Human-Machine Interaction, MA3HMI 2018 - In Conjunction with ICMI 2018, 1–5. https://doi.org/10.1145/3279972.3279973</ref><br />
<br />
* Social fidelity is related to certain human-like behaviour and cues. This includes for example speech content (personalized language, feedback, politeness, social memory, personality) and visual cues (facial expressions, gestures, gaze, emotions). <ref name="sinatra2021">Sinatra, A. M., Pollard, K. A., Files, B. T., Oiknine, A. H., Ericson, M., & Khooshabeh, P. (2021). Social fidelity in virtual agents: Impacts on presence and learning. Computers in Human Behavior, 114, 106562. https://doi.org/10.1016/j.chb.2020.106562</ref><br />
<br />
* An expressive emotionally intelligent VA is perceived as more emotionally intelligent when expressing itself through words, tone, body language, and facial expressions. <ref name="ma2019">Ma, X., Yang, E. Y., & Fung, P. (2019). Exploring Perceived Emotional Intelligence of Personality-Driven Virtual Agents in Handling User Challenges. https://doi.org/10.1145/3308558.3313400</ref><br />
<br />
* “Expressions are no more defined by a static representation; rather they are constituted as a succession of signals that appear dynamically. Using few expressions limits the interaction. Endowing agents with large variety of expressions ensures more naturalness in the agent’s behaviour.” <ref name="pelachaud2009">Pelachaud, C. (2009). Modelling multimodal expression of emotion in a virtual agent. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1535), 3539–3548. https://doi.org/10.1098/rstb.2009.0186</ref> Examples are given in this paper as well, see for instance figure 5 or 6. <br />
<br />
* Communication features like nonverbal cues seem to be crucial in maintaining learning motivation in virtual learning environments, probably because they inform the observer about states, involvement, responsiveness, and understanding. <ref name = "shiban2015"/><br />
<br />
* Deictic gestures (pointing towards something, indicating directions, objects etc) have been shown to guide attention, especially when the agent is static. <ref name = "shiban2015"/><br />
<br />
* A paper by Grover et al. <ref name = "grover2020"/> described an experiment in which two chatbots were compared, one of which has a face and appeared to be more emotionally intelligent. Results suggested that the emotionally expressive agent can lead participants (average age 33) to be more productive and focused during working hours. The participants also reported to feel more satisfied with their achievements.<br />
<br />
* A study by Go and Sundar <ref name="go2019"/> states that revealing the identity of a chatbot as a non-human can have a positive effect: user will have less high expectations about the conversation, and will be impressed when an agent shows human-like behaviour. Furthermore, they emphasize the importance of the conversational style between a human an a computer. When the dialogue resembles that of an actual human, perceived feelings of social presence and homophily will increase, leading to more positive attitudes towards the agent (and in turn potential desired behaviour consequences).<br />
<br />
=====Personality=====<br />
<br />
* Higher feelings of social presence can be achieved when an agent’s language usage shows a consistent personality, which can be either introvert or extravert. <ref name="sinatra2021"/><br />
<br />
* As we pointed out in Section 2, education and customer services are the task-oriented domains most reported in the literature. We found conscientiousness, damage control, thoroughness, manners, emotional intelligence, and identity in studies for both domains. However, manners and emotional intelligence have a different goal in these domains. In the education context, these characteristics are designed to encourage students, especially in a situation of failure, in which the chatbot should be comforting and sensitive. This function aligns with other domains, such as health-care. The education domain also reports needs for personality, so the chatbot can be recognized as either an instructor or a student, and proactivity, so the chatbot can motivate students to participate in the interactions. Personality is also reported in other domains in which the chatbots’ character influences the interactions, such as gaming and humorous talk. Proactivity is also consistently reported in domains in which the chatbot provides guidance, such as coaching, health, ethnography, and assessment interviews. <ref name="chaves2021"/><br />
<br />
===References===<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Related_Literature_Group_4,_design&diff=115213Related Literature Group 4, design2021-05-09T19:12:14Z<p>20182838: </p>
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<div><div style="font-family: 'Calibri'; font-size: 15px; line-height: 1.5; max-width: 1100px; word-wrap: break-word; color: #333; font-weight: 400; margin-left: auto; margin-right: auto; padding: 50px; background-color: white; padding-top: 25px;"><br />
<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Excerpts & citations </font><br />
<br />
====Gender====<br />
<br />
* Females chose to gender-match and to interact with a more realistic VA. Males exhibited little preference for either gender, and a greater preference than females for realistic VAs. Thus, where it is not feasible to gender-match in SSCO, the recommendation is to implement a realistic female VA. <ref name="payne2013">Payne, J., Szymkowiak, A., Robertson, P., & Johnson, G. (2013). Gendering the machine: Preferred virtual assistant gender and realism in self-service. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8108 LNAI, 106–115. https://doi.org/10.1007/978-3-642-40415-3_9</ref><br />
<br />
* Young and attractive female agent positively impacts interest in learning, an older an unattractive male agent does not impact motivation. <ref name="shiban2015">Shiban, Y., Schelhorn, I., Jobst, V., Hörnlein, A., Puppe, F., Pauli, P., & Mühlberger, A. (2015). The appearance effect: Influences of virtual agent features on performance and motivation. Computers in Human Behavior, 49, 5–11. https://doi.org/10.1016/j.chb.2015.01.077</ref><br />
<br />
* As research suggests that the combination of an agent’s gender and personality can play an important role in user perceptions and expectations [1, 37], employing a male agent instead may result in some significant differences in user perceptions or ratings of the agent. We encourage future work to investigate how gender and personality of a workplace productivity agent might influence user experience. <ref name="grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref><br />
<br />
* participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
* We found that the designed characteristics of VAs affects some aspects of user impressions (i.e. personality and trustworthiness) of the VA, while other impressions are not affected (i.e. social ability). We also found that gender matching between the agent and the user affect user impressions. // Gender similarity --> more trustworthy <ref name="akbar2018">Akbar, F., Grover, T., Mark, G., & Zhou, M. X. (2018, March 5). The effects of virtual agents’ characteristics on user impressions and language use. International Conference on Intelligent User Interfaces, Proceedings IUI. https://doi.org/10.1145/3180308.3180365</ref><br />
<br />
* In addition, the similarity of an agent to the learner positively influences the learner’s motivation (Bailenson, Blascovich, & Guadagno, 2008 in a study with undergraduates). For example, computer-based female agents yielded better motivational outcomes for undergraduate women if they matched the students with respect to race and gender (Rosenberg-Kima, Plant, Doerr, & Baylor, 2010). Another study, conducted with undergraduates by Rosenberg-Kima, Baylor, Plant, and Doerr (2008) revealed that a female agent rated as young, attractive and “cool” succeeded in enhancing young female students’ self-efficacy, which is believed to be a driving force behind motivation (Bandura, 1997). All these findings are theoretically supported by Bandura’s social cognitive learning theory which states that people often learn behavior and norms by imitating people whom they perceive as similar (or superior: higher in rank or status) to them and who are therefore rather accepted as social role models (Bandura, 1986). This finding is supported by another study of Gulz, Haake, and Tärning (2007) which demonstrated that participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
====Appearance and human-likeness====<br />
<br />
* In this study, it was noted that PIAs’ human-like features could influence the manner in which participants (i.e. males or females) related to a particular PIA and participants’ preference level of a particular PIA. The result of the Fischer’s exact test conducted in this study (Table V) found no statistically significant effect between participants’ gender (male or female) and their preferred gender of PIAs. It is concluded that the gender of participants (i.e. male or female) has no role (impact) on their preferences regarding the type, features, or gender of PIA, neither on their preference level of PIA. <ref name="mabanza2019">Mabanza, N. (2019). Gender influences on preference of pedagogical interface agents. 2018 International Conference on Intelligent and Innovative Computing Applications, ICONIC 2018. https://doi.org/10.1109/ICONIC.2018.8601292</ref><br />
<br />
* On the other hand, previous studies have also shown that developing overly humanized agents results in high expectations and uncanny feelings. <ref name="chaves2021">Chaves, A. P., & Gerosa, M. A. (2021). How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Desi | Enhanced Reader. International Journal of Human–Computer Interaction, 37(8), 729–758. https://doi.org/10.1080/10447318.2020.1841438</ref><br />
<br />
* Many participants saw the human-like appearance of the VA prototype as setting the wrong expectations in terms of its capabilities, and they were disappointed when the agent’s intelligence only extended towards responding to the their emotion and prompting more self-reflection. (…) We believe incorporating human-like qualities and emotional intelligence into future agents to be worthwhile; however, intelligence should also extend into other aspects of the agent’s capabilities in order to better help users be as efficient as possible in achieving their work goals. <ref name="grover2020"/><br />
<br />
* A study by Go and Sundar <ref name="go2019">Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j.chb.2019.01.020</ref> states that revealing the identity of a chatbot as a non-human can have a positive effect: user will have less high expectations about the conversation, and will be impressed when an agent shows human-like behaviour. Furthermore, they emphasize the importance of the conversational style between a human an a computer. When the dialogue resembles that of an actual human, perceived feelings of social presence and homophily will increase, leading to more positive attitudes towards the agent (and in turn potential desired behaviour consequences).<br />
<br />
* the presence of a representation produced more positive social interactions than not having a representation <ref name="Yee2017">Yee, N., Bailenson, J. N., & Rickertsen, K. (2007). A Meta-Analysis of the Impact of the Inclusion and Realism of Human-Like Faces on User Experiences in Interfaces.</ref><br />
** human-like representations with higher realism produced more positive social interactions than representations with lower realism; however, this effect was only found when subjective measures were used. Behavioral measures did not reveal a significant difference between representations of low and high realism. <br />
*** the difference we found may also be driven by demand characteristics. Participants interacting with an animated character (as opposed to a photograph) may suppose that the researcher is expecting a high appraisal.<br />
**while the presence of a face is better than no face at all, the quality of the face matters much less.<br />
***it is quite possible that animating highly realistic faces inherently allows for residual attributes of the faces that are negative—for example making 3D human faces may produce gestures and animations that appear unnatural or disturbing<br />
**while most studies have found that interface agents have positive effects on task performance, these effects are overall actually quite small.<br />
<br />
* In addition to understanding human social behavior around computers, another extensive line of work examines humans’ responses to computers with more expressive, human-like qualities, such as faces and facial expressions. In general, these studies have found that anthropomorphic properties of computers influence users’ perceptions (e.g., [13, 70, 86]), attitude (e.g., [13]), and behavior around such systems (e.g., [48, 86, 105]). For example, Sproull et al. [86] report that people respond to a text-based interface differently than to a talking face. On the one hand, users are aroused more and present themselves more positively when interacting with a talking face. On the other hand, users are less relaxed or assured when interacting with a talking face. Another study shows that users find the interface with the anthropomorphic qualities—faces and facial expressions—more likeable and engaging, although such an interface takes the users’ effort to interpret the meaning of the human-like expressions and may even be a distraction [48, 86]. More recent studies show that human-like features with higher realism elicit more positive social interactions while having no significant impact on user task performance [105]. Furthermore, a study reveals that anthropomorphism may even elicit user objections due to users’ own biases (e.g., sexism) [70]. <ref name="zhou2019">Zhou, M. X., Yang, H., Mark, G., & Li, J. (2019). Trusting Virtual Agents: The Effect of Personality. ACM Trans. Interact. Intell. Syst, 9(3). https://doi.org/10.1145/3232077</ref><br />
<br />
* Physically present agent leads to better motivational outcomes than a voice or text-box. It is important to design agents realistically, because e.g. cartoon figures have been shown to diminish the positive motivating effects in comparison to realistic figures. <ref name="shiban2015"/><br />
<br />
* It was found also that attractive VT did not improve perceived value of exercise and perceived risks of health. This result is different from some prior studies (e.g. Shiban et al., 2015) which find that attractive virtual pedagogical agents are effective for engaging students in learning. There are two possible explanations for this observed difference. First, attractive virtual agents may catch the attention of users and make users more confident in using a VTS. However, they may also distract users from core learning materials (Moreno and Flowerday, 2006). Second, attractive agents are closer to friends rather than experts. The incongruence of expert perception may lead to contextual irrelevance which makes users take health-related information from virtual trainers lightly (Veletsianos, 2010). <ref name="kwok2021">Kwok, R. C. W., Leung, A. C. M., Hui, S. S. chuen, & Wong, C. C. K. (2021). Virtual trainer system: a tool to increase exercise participation and work productivity. Internet Research. https://doi.org/10.1108/INTR-04-2020-0236</ref><br />
<br />
* If McCloud’s (1993) framework is applied, a teacher character, representing the other to a higher extent than a learning companion, might benefit from more realism in the representation. A learning companion character, being to a higher extent conceived of as an extension of oneself, may, on the other hand, benefit from a more iconic representation. <ref name="gulz2006">Gulz, A., & Haake, M. (2006). Design of animated pedagogical agents - A look at their look. International Journal of Human Computer Studies, 64(4), 322–339. https://doi.org/10.1016/j.ijhcs.2005.08.006</ref><br />
<br />
* The findings of this experiment suggest a new design method for human-agent collaboration work. If we use an agent having a non-human-like appearance (for example, the robot-like agent), the user seemed to attribute much responsibility to the agent and not to trust the agent. We suggest using the agent having human-like appearance in human-agent collaboration work from this experiment. A question is raised about the commonly accepted beliefs about agent design for social tasks. We should consider the attribution of responsibility to construct trustworthy agents. <ref name="matsui2021">Matsui, T., & Koike, A. (2021). Who is to blame? The appearance of virtual agents and the attribution of perceived responsibility. Sensors, 21(8), 1–13. https://doi.org/10.3390/s21082646</ref><br />
<br />
====Behaviour and personality====<br />
<br />
* It is possible to program a chatbot in a way that it will interact with you via a moving and talking avatar. The paper also suggests that displaying facial expressions are beneficial in the interaction with such an agent, since displaying the avatar’s emotion can enhance perceived emotional intelligence. <ref name="">Angga, P. A., Fachri, W. E., Elevanita, A., Suryadi, & Agushinta, R. D. (2016). Design of chatbot with 3D avatar, voice interface, and facial expression. Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015, 326–330. https://doi.org/10.1109/ICSITech.2015.7407826</ref><br />
<br />
* Looije et al researched the guidelines that are needed when developing a personal assistant. These guidelines were derived from interviewing, persuasive technologies and from existing guidelines for personal assistants. In their research they found that guidelines were best expressed in iCat (a personal assistant) that was able to show socially intelligent behavior compared to a non-social or text interface based iCat. <ref name="">Looije, R., Cnossen, F., & Neerincx, M. A. (2006). Incorporating guidelines for health assistance into a socially intelligent robot. Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, 515–520. https://doi.org/10.1109/ROMAN.2006.314441</ref><br />
<br />
* “The results suggested that it is possible to express extroversion and confidence in the agent by changing the agent's gaze amount regardless of the agent's embodiment. <ref name="">Koda, T., & Ishioh, T. (2018). Analysis of the effect of agent’s embodiment and gaze amount on personality perception. Proceedings of the 4th Workshop on Multimodal Analyses Enabling Artificial Agents in Human-Machine Interaction, MA3HMI 2018 - In Conjunction with ICMI 2018, 1–5. https://doi.org/10.1145/3279972.3279973</ref><br />
<br />
* Social fidelity is related to certain human-like behaviour and cues. This includes for example speech content (personalized language, feedback, politeness, social memory, personality) and visual cues (facial expressions, gestures, gaze, emotions). <ref name="">Sinatra, A. M., Pollard, K. A., Files, B. T., Oiknine, A. H., Ericson, M., & Khooshabeh, P. (2021). Social fidelity in virtual agents: Impacts on presence and learning. Computers in Human Behavior, 114, 106562. https://doi.org/10.1016/j.chb.2020.106562</ref><br />
<br />
* An expressive emotionally intelligent VA is perceived as more emotionally intelligent when expressing itself through words, tone, body language, and facial expressions. <ref name="">Ma, X., Yang, E. Y., & Fung, P. (2019). Exploring Perceived Emotional Intelligence of Personality-Driven Virtual Agents in Handling User Challenges. https://doi.org/10.1145/3308558.3313400</ref><br />
<br />
* “Expressions are no more defined by a static representation; rather they are constituted as a succession of signals that appear dynamically. Using few expressions limits the interaction. Endowing agents with large variety of expressions ensures more naturalness in the agent’s behaviour.” <ref name="">Pelachaud, C. (2009). Modelling multimodal expression of emotion in a virtual agent. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1535), 3539–3548. https://doi.org/10.1098/rstb.2009.0186</ref> Examples are given in this paper as well, see for instance figure 5 or 6. <br />
<br />
* Communication features like nonverbal cues seem to be crucial in maintaining learning motivation in virtual learning environments, probably because they inform the observer about states, involvement, responsiveness, and understanding. <ref name = "shiban2015"/><br />
<br />
* Deictic gestures (pointing towards something, indicating directions, objects etc) have been shown to guide attention, especially when the agent is static. <ref name = "shiban2015"/><br />
<br />
* A paper by Grover et al. <ref name = "grover2020"/> described an experiment in which two chatbots were compared, one of which has a face and appeared to be more emotionally intelligent. Results suggested that the emotionally expressive agent can lead participants (average age 33) to be more productive and focused during working hours. The participants also reported to feel more satisfied with their achievements.<br />
<br />
* A study by Go and Sundar <ref name="go2019"/> states that revealing the identity of a chatbot as a non-human can have a positive effect: user will have less high expectations about the conversation, and will be impressed when an agent shows human-like behaviour. Furthermore, they emphasize the importance of the conversational style between a human an a computer. When the dialogue resembles that of an actual human, perceived feelings of social presence and homophily will increase, leading to more positive attitudes towards the agent (and in turn potential desired behaviour consequences).<br />
<br />
=====Personality=====<br />
<br />
* Higher feelings of social presence can be achieved when an agent’s language usage shows a consistent personality, which can be either introvert or extravert. <ref name="sinatra2021"/><br />
<br />
* As we pointed out in Section 2, education and customer services are the task-oriented domains most reported in the literature. We found conscientiousness, damage control, thoroughness, manners, emotional intelligence, and identity in studies for both domains. However, manners and emotional intelligence have a different goal in these domains. In the education context, these characteristics are designed to encourage students, especially in a situation of failure, in which the chatbot should be comforting and sensitive. This function aligns with other domains, such as health-care. The education domain also reports needs for personality, so the chatbot can be recognized as either an instructor or a student, and proactivity, so the chatbot can motivate students to participate in the interactions. Personality is also reported in other domains in which the chatbots’ character influences the interactions, such as gaming and humorous talk. Proactivity is also consistently reported in domains in which the chatbot provides guidance, such as coaching, health, ethnography, and assessment interviews. <ref name="chaves2021"/><br />
<br />
===References===<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Related_Literature_Group_4,_design&diff=115212Related Literature Group 4, design2021-05-09T18:45:14Z<p>20182838: </p>
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<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Excerpts & citations </font><br />
<br />
====Gender====<br />
<br />
* Females chose to gender-match and to interact with a more realistic VA. Males exhibited little preference for either gender, and a greater preference than females for realistic VAs. Thus, where it is not feasible to gender-match in SSCO, the recommendation is to implement a realistic female VA. <ref name="payne2013">Payne, J., Szymkowiak, A., Robertson, P., & Johnson, G. (2013). Gendering the machine: Preferred virtual assistant gender and realism in self-service. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8108 LNAI, 106–115. https://doi.org/10.1007/978-3-642-40415-3_9</ref><br />
<br />
* Young and attractive female agent positively impacts interest in learning, an older an unattractive male agent does not impact motivation. <ref name="shiban2015">Shiban, Y., Schelhorn, I., Jobst, V., Hörnlein, A., Puppe, F., Pauli, P., & Mühlberger, A. (2015). The appearance effect: Influences of virtual agent features on performance and motivation. Computers in Human Behavior, 49, 5–11. https://doi.org/10.1016/j.chb.2015.01.077</ref><br />
<br />
* As research suggests that the combination of an agent’s gender and personality can play an important role in user perceptions and expectations [1, 37], employing a male agent instead may result in some significant differences in user perceptions or ratings of the agent. We encourage future work to investigate how gender and personality of a workplace productivity agent might influence user experience. <ref name="grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref><br />
<br />
* participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
* We found that the designed characteristics of VAs affects some aspects of user impressions (i.e. personality and trustworthiness) of the VA, while other impressions are not affected (i.e. social ability). We also found that gender matching between the agent and the user affect user impressions. // Gender similarity --> more trustworthy <ref name="akbar2018">Akbar, F., Grover, T., Mark, G., & Zhou, M. X. (2018, March 5). The effects of virtual agents’ characteristics on user impressions and language use. International Conference on Intelligent User Interfaces, Proceedings IUI. https://doi.org/10.1145/3180308.3180365</ref><br />
<br />
* In addition, the similarity of an agent to the learner positively influences the learner’s motivation (Bailenson, Blascovich, & Guadagno, 2008 in a study with undergraduates). For example, computer-based female agents yielded better motivational outcomes for undergraduate women if they matched the students with respect to race and gender (Rosenberg-Kima, Plant, Doerr, & Baylor, 2010). Another study, conducted with undergraduates by Rosenberg-Kima, Baylor, Plant, and Doerr (2008) revealed that a female agent rated as young, attractive and “cool” succeeded in enhancing young female students’ self-efficacy, which is believed to be a driving force behind motivation (Bandura, 1997). All these findings are theoretically supported by Bandura’s social cognitive learning theory which states that people often learn behavior and norms by imitating people whom they perceive as similar (or superior: higher in rank or status) to them and who are therefore rather accepted as social role models (Bandura, 1986). This finding is supported by another study of Gulz, Haake, and Tärning (2007) which demonstrated that participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
====Appearance and human-likeness====<br />
<br />
* In this study, it was noted that PIAs’ human-like features could influence the manner in which participants (i.e. males or females) related to a particular PIA and participants’ preference level of a particular PIA. The result of the Fischer’s exact test conducted in this study (Table V) found no statistically significant effect between participants’ gender (male or female) and their preferred gender of PIAs. It is concluded that the gender of participants (i.e. male or female) has no role (impact) on their preferences regarding the type, features, or gender of PIA, neither on their preference level of PIA. <ref name="mabanza2019">Mabanza, N. (2019). Gender influences on preference of pedagogical interface agents. 2018 International Conference on Intelligent and Innovative Computing Applications, ICONIC 2018. https://doi.org/10.1109/ICONIC.2018.8601292</ref><br />
<br />
* On the other hand, previous studies have also shown that developing overly humanized agents results in high expectations and uncanny feelings. <ref name="chaves2021">Chaves, A. P., & Gerosa, M. A. (2021). How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Desi | Enhanced Reader. International Journal of Human–Computer Interaction, 37(8), 729–758. https://doi.org/10.1080/10447318.2020.1841438</ref><br />
<br />
* Many participants saw the human-like appearance of the VA prototype as setting the wrong expectations in terms of its capabilities, and they were disappointed when the agent’s intelligence only extended towards responding to the their emotion and prompting more self-reflection. (…) We believe incorporating human-like qualities and emotional intelligence into future agents to be worthwhile; however, intelligence should also extend into other aspects of the agent’s capabilities in order to better help users be as efficient as possible in achieving their work goals. <ref name="grover2020"/><br />
<br />
* A study by Go and Sundar <ref name="go2019">Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j.chb.2019.01.020</ref> states that revealing the identity of a chatbot as a non-human can have a positive effect: user will have less high expectations about the conversation, and will be impressed when an agent shows human-like behaviour. Furthermore, they emphasize the importance of the conversational style between a human an a computer. When the dialogue resembles that of an actual human, perceived feelings of social presence and homophily will increase, leading to more positive attitudes towards the agent (and in turn potential desired behaviour consequences).<br />
<br />
* the presence of a representation produced more positive social interactions than not having a representation <ref name="Yee2017">Yee, N., Bailenson, J. N., & Rickertsen, K. (2007). A Meta-Analysis of the Impact of the Inclusion and Realism of Human-Like Faces on User Experiences in Interfaces.</ref><br />
** human-like representations with higher realism produced more positive social interactions than representations with lower realism; however, this effect was only found when subjective measures were used. Behavioral measures did not reveal a significant difference between representations of low and high realism. <br />
*** the difference we found may also be driven by demand characteristics. Participants interacting with an animated character (as opposed to a photograph) may suppose that the researcher is expecting a high appraisal.<br />
**while the presence of a face is better than no face at all, the quality of the face matters much less.<br />
***it is quite possible that animating highly realistic faces inherently allows for residual attributes of the faces that are negative—for example making 3D human faces may produce gestures and animations that appear unnatural or disturbing<br />
**while most studies have found that interface agents have positive effects on task performance, these effects are overall actually quite small.<br />
<br />
* In addition to understanding human social behavior around computers, another extensive line of work examines humans’ responses to computers with more expressive, human-like qualities, such as faces and facial expressions. In general, these studies have found that anthropomorphic properties of computers influence users’ perceptions (e.g., [13, 70, 86]), attitude (e.g., [13]), and behavior around such systems (e.g., [48, 86, 105]). For example, Sproull et al. [86] report that people respond to a text-based interface differently than to a talking face. On the one hand, users are aroused more and present themselves more positively when interacting with a talking face. On the other hand, users are less relaxed or assured when interacting with a talking face. Another study shows that users find the interface with the anthropomorphic qualities—faces and facial expressions—more likeable and engaging, although such an interface takes the users’ effort to interpret the meaning of the human-like expressions and may even be a distraction [48, 86]. More recent studies show that human-like features with higher realism elicit more positive social interactions while having no significant impact on user task performance [105]. Furthermore, a study reveals that anthropomorphism may even elicit user objections due to users’ own biases (e.g., sexism) [70]. <ref name="zhou2019">Zhou, M. X., Yang, H., Mark, G., & Li, J. (2019). Trusting Virtual Agents: The Effect of Personality. ACM Trans. Interact. Intell. Syst, 9(3). https://doi.org/10.1145/3232077</ref><br />
<br />
* Physically present agent leads to better motivational outcomes than a voice or text-box. It is important to design agents realistically, because e.g. cartoon figures have been shown to diminish the positive motivating effects in comparison to realistic figures. <ref name="shiban2015"/><br />
<br />
* It was found also that attractive VT did not improve perceived value of exercise and perceived risks of health. This result is different from some prior studies (e.g. Shiban et al., 2015) which find that attractive virtual pedagogical agents are effective for engaging students in learning. There are two possible explanations for this observed difference. First, attractive virtual agents may catch the attention of users and make users more confident in using a VTS. However, they may also distract users from core learning materials (Moreno and Flowerday, 2006). Second, attractive agents are closer to friends rather than experts. The incongruence of expert perception may lead to contextual irrelevance which makes users take health-related information from virtual trainers lightly (Veletsianos, 2010). <ref name="kwok2021">Kwok, R. C. W., Leung, A. C. M., Hui, S. S. chuen, & Wong, C. C. K. (2021). Virtual trainer system: a tool to increase exercise participation and work productivity. Internet Research. https://doi.org/10.1108/INTR-04-2020-0236</ref><br />
<br />
* If McCloud’s (1993) framework is applied, a teacher character, representing the other to a higher extent than a learning companion, might benefit from more realism in the representation. A learning companion character, being to a higher extent conceived of as an extension of oneself, may, on the other hand, benefit from a more iconic representation. <ref name="gulz2006">Gulz, A., & Haake, M. (2006). Design of animated pedagogical agents - A look at their look. International Journal of Human Computer Studies, 64(4), 322–339. https://doi.org/10.1016/j.ijhcs.2005.08.006</ref><br />
<br />
* The findings of this experiment suggest a new design method for human-agent collaboration work. If we use an agent having a non-human-like appearance (for example, the robot-like agent), the user seemed to attribute much responsibility to the agent and not to trust the agent. We suggest using the agent having human-like appearance in human-agent collaboration work from this experiment. A question is raised about the commonly accepted beliefs about agent design for social tasks. We should consider the attribution of responsibility to construct trustworthy agents. <ref name="matsui2021">Matsui, T., & Koike, A. (2021). Who is to blame? The appearance of virtual agents and the attribution of perceived responsibility. Sensors, 21(8), 1–13. https://doi.org/10.3390/s21082646</ref><br />
<br />
====Behaviour and personality====<br />
<br />
* It is possible to program a chatbot in a way that it will interact with you via a moving and talking avatar. The paper also suggests that displaying facial expressions are beneficial in the interaction with such an agent, since displaying the avatar’s emotion can enhance perceived emotional intelligence. <ref name="">Angga, P. A., Fachri, W. E., Elevanita, A., Suryadi, & Agushinta, R. D. (2016). Design of chatbot with 3D avatar, voice interface, and facial expression. Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015, 326–330. https://doi.org/10.1109/ICSITech.2015.7407826</ref><br />
<br />
* Looije et al researched the guidelines that are needed when developing a personal assistant. These guidelines were derived from interviewing, persuasive technologies and from existing guidelines for personal assistants. In their research they found that guidelines were best expressed in iCat (a personal assistant) that was able to show socially intelligent behavior compared to a non-social or text interface based iCat. <ref name="">Looije, R., Cnossen, F., & Neerincx, M. A. (2006). Incorporating guidelines for health assistance into a socially intelligent robot. Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, 515–520. https://doi.org/10.1109/ROMAN.2006.314441</ref><br />
<br />
* “The results suggested that it is possible to express extroversion and confidence in the agent by changing the agent's gaze amount regardless of the agent's embodiment. <ref name="">Koda, T., & Ishioh, T. (2018). Analysis of the effect of agent’s embodiment and gaze amount on personality perception. Proceedings of the 4th Workshop on Multimodal Analyses Enabling Artificial Agents in Human-Machine Interaction, MA3HMI 2018 - In Conjunction with ICMI 2018, 1–5. https://doi.org/10.1145/3279972.3279973</ref><br />
<br />
* Social fidelity is related to certain human-like behaviour and cues. This includes for example speech content (personalized language, feedback, politeness, social memory, personality) and visual cues (facial expressions, gestures, gaze, emotions). <ref name="">Sinatra, A. M., Pollard, K. A., Files, B. T., Oiknine, A. H., Ericson, M., & Khooshabeh, P. (2021). Social fidelity in virtual agents: Impacts on presence and learning. Computers in Human Behavior, 114, 106562. https://doi.org/10.1016/j.chb.2020.106562</ref><br />
<br />
* An expressive emotionally intelligent VA is perceived as more emotionally intelligent when expressing itself through words, tone, body language, and facial expressions. <ref name="">Ma, X., Yang, E. Y., & Fung, P. (2019). Exploring Perceived Emotional Intelligence of Personality-Driven Virtual Agents in Handling User Challenges. https://doi.org/10.1145/3308558.3313400</ref><br />
<br />
* “Expressions are no more defined by a static representation; rather they are constituted as a succession of signals that appear dynamically. Using few expressions limits the interaction. Endowing agents with large variety of expressions ensures more naturalness in the agent’s behaviour.” <ref name="">Pelachaud, C. (2009). Modelling multimodal expression of emotion in a virtual agent. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1535), 3539–3548. https://doi.org/10.1098/rstb.2009.0186</ref> Examples are given in this paper as well, see for instance figure 5 or 6. <br />
<br />
* Communication features like nonverbal cues seem to be crucial in maintaining learning motivation in virtual learning environments, probably because they inform the observer about states, involvement, responsiveness, and understanding. <ref name = "shiban2015"/><br />
<br />
* Deictic gestures (pointing towards something, indicating directions, objects etc) have been shown to guide attention, especially when the agent is static. <ref name = "shiban2015"/><br />
<br />
* A paper by Grover et al. <ref name = "grover2020"/> described an experiment in which two chatbots were compared, one of which has a face and appeared to be more emotionally intelligent. Results suggested that the emotionally expressive agent can lead participants (average age 33) to be more productive and focused during working hours. The participants also reported to feel more satisfied with their achievements.<br />
<br />
* A study by Go and Sundar <ref name="go2019"/> states that revealing the identity of a chatbot as a non-human can have a positive effect: user will have less high expectations about the conversation, and will be impressed when an agent shows human-like behaviour. Furthermore, they emphasize the importance of the conversational style between a human an a computer. When the dialogue resembles that of an actual human, perceived feelings of social presence and homophily will increase, leading to more positive attitudes towards the agent (and in turn potential desired behaviour consequences).<br />
<br />
=====Personality=====<br />
<br />
* Higher feelings of social presence can be achieved when an agent’s language usage shows a consistent personality, which can be either introvert or extravert.<br />
<br />
* As we pointed out in Section 2, education and customer services are the task-oriented domains most reported in the literature. We found conscientiousness, damage control, thoroughness, manners, emotional intelligence, and identity in studies for both domains. However, manners and emotional intelligence have a different goal in these domains. In the education context, these characteristics are designed to encourage students, especially in a situation of failure, in which the chatbot should be comforting and sensitive. This function aligns with other domains, such as health-care. The education domain also reports needs for personality, so the chatbot can be recognized as either an instructor or a student, and proactivity, so the chatbot can motivate students to participate in the interactions. Personality is also reported in other domains in which the chatbots’ character influences the interactions, such as gaming and humorous talk. Proactivity is also consistently reported in domains in which the chatbot provides guidance, such as coaching, health, ethnography, and assessment interviews. <ref name="chaves2021"/><br />
<br />
===References===<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Related_Literature_Group_4,_design&diff=115211Related Literature Group 4, design2021-05-09T18:24:32Z<p>20182838: /* Behaviour and personality */</p>
<hr />
<div><div style="font-family: 'Calibri'; font-size: 15px; line-height: 1.5; max-width: 1100px; word-wrap: break-word; color: #333; font-weight: 400; margin-left: auto; margin-right: auto; padding: 50px; background-color: white; padding-top: 25px;"><br />
<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Excerpts & citations </font><br />
<br />
====Gender====<br />
<br />
* Females chose to gender-match and to interact with a more realistic VA. Males exhibited little preference for either gender, and a greater preference than females for realistic VAs. Thus, where it is not feasible to gender-match in SSCO, the recommendation is to implement a realistic female VA. <ref name="payne2013">Payne, J., Szymkowiak, A., Robertson, P., & Johnson, G. (2013). Gendering the machine: Preferred virtual assistant gender and realism in self-service. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8108 LNAI, 106–115. https://doi.org/10.1007/978-3-642-40415-3_9</ref><br />
<br />
* Young and attractive female agent positively impacts interest in learning, an older an unattractive male agent does not impact motivation. <ref name="shiban2015">Shiban, Y., Schelhorn, I., Jobst, V., Hörnlein, A., Puppe, F., Pauli, P., & Mühlberger, A. (2015). The appearance effect: Influences of virtual agent features on performance and motivation. Computers in Human Behavior, 49, 5–11. https://doi.org/10.1016/j.chb.2015.01.077</ref><br />
<br />
* As research suggests that the combination of an agent’s gender and personality can play an important role in user perceptions and expectations [1, 37], employing a male agent instead may result in some significant differences in user perceptions or ratings of the agent. We encourage future work to investigate how gender and personality of a workplace productivity agent might influence user experience. <ref name="grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref><br />
<br />
* participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
* We found that the designed characteristics of VAs affects some aspects of user impressions (i.e. personality and trustworthiness) of the VA, while other impressions are not affected (i.e. social ability). We also found that gender matching between the agent and the user affect user impressions. // Gender similarity --> more trustworthy <ref name="akbar2018">Akbar, F., Grover, T., Mark, G., & Zhou, M. X. (2018, March 5). The effects of virtual agents’ characteristics on user impressions and language use. International Conference on Intelligent User Interfaces, Proceedings IUI. https://doi.org/10.1145/3180308.3180365</ref><br />
<br />
* In addition, the similarity of an agent to the learner positively influences the learner’s motivation (Bailenson, Blascovich, & Guadagno, 2008 in a study with undergraduates). For example, computer-based female agents yielded better motivational outcomes for undergraduate women if they matched the students with respect to race and gender (Rosenberg-Kima, Plant, Doerr, & Baylor, 2010). Another study, conducted with undergraduates by Rosenberg-Kima, Baylor, Plant, and Doerr (2008) revealed that a female agent rated as young, attractive and “cool” succeeded in enhancing young female students’ self-efficacy, which is believed to be a driving force behind motivation (Bandura, 1997). All these findings are theoretically supported by Bandura’s social cognitive learning theory which states that people often learn behavior and norms by imitating people whom they perceive as similar (or superior: higher in rank or status) to them and who are therefore rather accepted as social role models (Bandura, 1986). This finding is supported by another study of Gulz, Haake, and Tärning (2007) which demonstrated that participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
====Appearance and human-likeness====<br />
<br />
* In this study, it was noted that PIAs’ human-like features could influence the manner in which participants (i.e. males or females) related to a particular PIA and participants’ preference level of a particular PIA. The result of the Fischer’s exact test conducted in this study (Table V) found no statistically significant effect between participants’ gender (male or female) and their preferred gender of PIAs. It is concluded that the gender of participants (i.e. male or female) has no role (impact) on their preferences regarding the type, features, or gender of PIA, neither on their preference level of PIA. <ref name="mabanza2019">Mabanza, N. (2019). Gender influences on preference of pedagogical interface agents. 2018 International Conference on Intelligent and Innovative Computing Applications, ICONIC 2018. https://doi.org/10.1109/ICONIC.2018.8601292</ref><br />
<br />
* On the other hand, previous studies have also shown that developing overly humanized agents results in high expectations and uncanny feelings. <ref name="chaves2021">Chaves, A. P., & Gerosa, M. A. (2021). How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Desi | Enhanced Reader. International Journal of Human–Computer Interaction, 37(8), 729–758. https://doi.org/10.1080/10447318.2020.1841438</ref><br />
<br />
* Many participants saw the human-like appearance of the VA prototype as setting the wrong expectations in terms of its capabilities, and they were disappointed when the agent’s intelligence only extended towards responding to the their emotion and prompting more self-reflection. (…) We believe incorporating human-like qualities and emotional intelligence into future agents to be worthwhile; however, intelligence should also extend into other aspects of the agent’s capabilities in order to better help users be as efficient as possible in achieving their work goals. <ref name="grover2020"/><br />
<br />
* A study by Go and Sundar <ref name="go2019">Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j.chb.2019.01.020</ref> states that revealing the identity of a chatbot as a non-human can have a positive effect: user will have less high expectations about the conversation, and will be impressed when an agent shows human-like behaviour. Furthermore, they emphasize the importance of the conversational style between a human an a computer. When the dialogue resembles that of an actual human, perceived feelings of social presence and homophily will increase, leading to more positive attitudes towards the agent (and in turn potential desired behaviour consequences).<br />
<br />
* the presence of a representation produced more positive social interactions than not having a representation <ref name="Yee2017">Yee, N., Bailenson, J. N., & Rickertsen, K. (2007). A Meta-Analysis of the Impact of the Inclusion and Realism of Human-Like Faces on User Experiences in Interfaces.</ref><br />
** human-like representations with higher realism produced more positive social interactions than representations with lower realism; however, this effect was only found when subjective measures were used. Behavioral measures did not reveal a significant difference between representations of low and high realism. <br />
*** the difference we found may also be driven by demand characteristics. Participants interacting with an animated character (as opposed to a photograph) may suppose that the researcher is expecting a high appraisal.<br />
**while the presence of a face is better than no face at all, the quality of the face matters much less.<br />
***it is quite possible that animating highly realistic faces inherently allows for residual attributes of the faces that are negative—for example making 3D human faces may produce gestures and animations that appear unnatural or disturbing<br />
**while most studies have found that interface agents have positive effects on task performance, these effects are overall actually quite small.<br />
<br />
* In addition to understanding human social behavior around computers, another extensive line of work examines humans’ responses to computers with more expressive, human-like qualities, such as faces and facial expressions. In general, these studies have found that anthropomorphic properties of computers influence users’ perceptions (e.g., [13, 70, 86]), attitude (e.g., [13]), and behavior around such systems (e.g., [48, 86, 105]). For example, Sproull et al. [86] report that people respond to a text-based interface differently than to a talking face. On the one hand, users are aroused more and present themselves more positively when interacting with a talking face. On the other hand, users are less relaxed or assured when interacting with a talking face. Another study shows that users find the interface with the anthropomorphic qualities—faces and facial expressions—more likeable and engaging, although such an interface takes the users’ effort to interpret the meaning of the human-like expressions and may even be a distraction [48, 86]. More recent studies show that human-like features with higher realism elicit more positive social interactions while having no significant impact on user task performance [105]. Furthermore, a study reveals that anthropomorphism may even elicit user objections due to users’ own biases (e.g., sexism) [70]. <ref name="zhou2019">Zhou, M. X., Yang, H., Mark, G., & Li, J. (2019). Trusting Virtual Agents: The Effect of Personality. ACM Trans. Interact. Intell. Syst, 9(3). https://doi.org/10.1145/3232077</ref><br />
<br />
* Physically present agent leads to better motivational outcomes than a voice or text-box. It is important to design agents realistically, because e.g. cartoon figures have been shown to diminish the positive motivating effects in comparison to realistic figures. <ref name="shiban2015"/><br />
<br />
* It was found also that attractive VT did not improve perceived value of exercise and perceived risks of health. This result is different from some prior studies (e.g. Shiban et al., 2015) which find that attractive virtual pedagogical agents are effective for engaging students in learning. There are two possible explanations for this observed difference. First, attractive virtual agents may catch the attention of users and make users more confident in using a VTS. However, they may also distract users from core learning materials (Moreno and Flowerday, 2006). Second, attractive agents are closer to friends rather than experts. The incongruence of expert perception may lead to contextual irrelevance which makes users take health-related information from virtual trainers lightly (Veletsianos, 2010). <ref name="kwok2021">Kwok, R. C. W., Leung, A. C. M., Hui, S. S. chuen, & Wong, C. C. K. (2021). Virtual trainer system: a tool to increase exercise participation and work productivity. Internet Research. https://doi.org/10.1108/INTR-04-2020-0236</ref><br />
<br />
* If McCloud’s (1993) framework is applied, a teacher character, representing the other to a higher extent than a learning companion, might benefit from more realism in the representation. A learning companion character, being to a higher extent conceived of as an extension of oneself, may, on the other hand, benefit from a more iconic representation. <ref name="gulz2006">Gulz, A., & Haake, M. (2006). Design of animated pedagogical agents - A look at their look. International Journal of Human Computer Studies, 64(4), 322–339. https://doi.org/10.1016/j.ijhcs.2005.08.006</ref><br />
<br />
* The findings of this experiment suggest a new design method for human-agent collaboration work. If we use an agent having a non-human-like appearance (for example, the robot-like agent), the user seemed to attribute much responsibility to the agent and not to trust the agent. We suggest using the agent having human-like appearance in human-agent collaboration work from this experiment. A question is raised about the commonly accepted beliefs about agent design for social tasks. We should consider the attribution of responsibility to construct trustworthy agents. <ref name="matsui2021">Matsui, T., & Koike, A. (2021). Who is to blame? The appearance of virtual agents and the attribution of perceived responsibility. Sensors, 21(8), 1–13. https://doi.org/10.3390/s21082646</ref><br />
<br />
====Behaviour and personality====<br />
<br />
* It is possible to program a chatbot in a way that it will interact with you via a moving and talking avatar. The paper also suggests that displaying facial expressions are beneficial in the interaction with such an agent, since displaying the avatar’s emotion can enhance perceived emotional intelligence. <br />
<br />
* <br />
<br />
* <br />
<br />
* <br />
<br />
* <br />
<br />
* <br />
<br />
* <br />
<br />
* <br />
<br />
*<br />
<br />
===References===<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Related_Literature_Group_4,_design&diff=115210Related Literature Group 4, design2021-05-09T18:21:23Z<p>20182838: </p>
<hr />
<div><div style="font-family: 'Calibri'; font-size: 15px; line-height: 1.5; max-width: 1100px; word-wrap: break-word; color: #333; font-weight: 400; margin-left: auto; margin-right: auto; padding: 50px; background-color: white; padding-top: 25px;"><br />
<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Excerpts & citations </font><br />
<br />
====Gender====<br />
<br />
* Females chose to gender-match and to interact with a more realistic VA. Males exhibited little preference for either gender, and a greater preference than females for realistic VAs. Thus, where it is not feasible to gender-match in SSCO, the recommendation is to implement a realistic female VA. <ref name="payne2013">Payne, J., Szymkowiak, A., Robertson, P., & Johnson, G. (2013). Gendering the machine: Preferred virtual assistant gender and realism in self-service. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8108 LNAI, 106–115. https://doi.org/10.1007/978-3-642-40415-3_9</ref><br />
<br />
* Young and attractive female agent positively impacts interest in learning, an older an unattractive male agent does not impact motivation. <ref name="shiban2015">Shiban, Y., Schelhorn, I., Jobst, V., Hörnlein, A., Puppe, F., Pauli, P., & Mühlberger, A. (2015). The appearance effect: Influences of virtual agent features on performance and motivation. Computers in Human Behavior, 49, 5–11. https://doi.org/10.1016/j.chb.2015.01.077</ref><br />
<br />
* As research suggests that the combination of an agent’s gender and personality can play an important role in user perceptions and expectations [1, 37], employing a male agent instead may result in some significant differences in user perceptions or ratings of the agent. We encourage future work to investigate how gender and personality of a workplace productivity agent might influence user experience. <ref name="grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref><br />
<br />
* participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
* We found that the designed characteristics of VAs affects some aspects of user impressions (i.e. personality and trustworthiness) of the VA, while other impressions are not affected (i.e. social ability). We also found that gender matching between the agent and the user affect user impressions. // Gender similarity --> more trustworthy <ref name="akbar2018">Akbar, F., Grover, T., Mark, G., & Zhou, M. X. (2018, March 5). The effects of virtual agents’ characteristics on user impressions and language use. International Conference on Intelligent User Interfaces, Proceedings IUI. https://doi.org/10.1145/3180308.3180365</ref><br />
<br />
* In addition, the similarity of an agent to the learner positively influences the learner’s motivation (Bailenson, Blascovich, & Guadagno, 2008 in a study with undergraduates). For example, computer-based female agents yielded better motivational outcomes for undergraduate women if they matched the students with respect to race and gender (Rosenberg-Kima, Plant, Doerr, & Baylor, 2010). Another study, conducted with undergraduates by Rosenberg-Kima, Baylor, Plant, and Doerr (2008) revealed that a female agent rated as young, attractive and “cool” succeeded in enhancing young female students’ self-efficacy, which is believed to be a driving force behind motivation (Bandura, 1997). All these findings are theoretically supported by Bandura’s social cognitive learning theory which states that people often learn behavior and norms by imitating people whom they perceive as similar (or superior: higher in rank or status) to them and who are therefore rather accepted as social role models (Bandura, 1986). This finding is supported by another study of Gulz, Haake, and Tärning (2007) which demonstrated that participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
====Appearance and human-likeness====<br />
<br />
* In this study, it was noted that PIAs’ human-like features could influence the manner in which participants (i.e. males or females) related to a particular PIA and participants’ preference level of a particular PIA. The result of the Fischer’s exact test conducted in this study (Table V) found no statistically significant effect between participants’ gender (male or female) and their preferred gender of PIAs. It is concluded that the gender of participants (i.e. male or female) has no role (impact) on their preferences regarding the type, features, or gender of PIA, neither on their preference level of PIA. <ref name="mabanza2019">Mabanza, N. (2019). Gender influences on preference of pedagogical interface agents. 2018 International Conference on Intelligent and Innovative Computing Applications, ICONIC 2018. https://doi.org/10.1109/ICONIC.2018.8601292</ref><br />
<br />
* On the other hand, previous studies have also shown that developing overly humanized agents results in high expectations and uncanny feelings. <ref name="chaves2021">Chaves, A. P., & Gerosa, M. A. (2021). How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Desi | Enhanced Reader. International Journal of Human–Computer Interaction, 37(8), 729–758. https://doi.org/10.1080/10447318.2020.1841438</ref><br />
<br />
* Many participants saw the human-like appearance of the VA prototype as setting the wrong expectations in terms of its capabilities, and they were disappointed when the agent’s intelligence only extended towards responding to the their emotion and prompting more self-reflection. (…) We believe incorporating human-like qualities and emotional intelligence into future agents to be worthwhile; however, intelligence should also extend into other aspects of the agent’s capabilities in order to better help users be as efficient as possible in achieving their work goals. <ref name="grover2020"/><br />
<br />
* A study by Go and Sundar <ref name="go2019">Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j.chb.2019.01.020</ref> states that revealing the identity of a chatbot as a non-human can have a positive effect: user will have less high expectations about the conversation, and will be impressed when an agent shows human-like behaviour. Furthermore, they emphasize the importance of the conversational style between a human an a computer. When the dialogue resembles that of an actual human, perceived feelings of social presence and homophily will increase, leading to more positive attitudes towards the agent (and in turn potential desired behaviour consequences).<br />
<br />
* the presence of a representation produced more positive social interactions than not having a representation <ref name="Yee2017">Yee, N., Bailenson, J. N., & Rickertsen, K. (2007). A Meta-Analysis of the Impact of the Inclusion and Realism of Human-Like Faces on User Experiences in Interfaces.</ref><br />
** human-like representations with higher realism produced more positive social interactions than representations with lower realism; however, this effect was only found when subjective measures were used. Behavioral measures did not reveal a significant difference between representations of low and high realism. <br />
*** the difference we found may also be driven by demand characteristics. Participants interacting with an animated character (as opposed to a photograph) may suppose that the researcher is expecting a high appraisal.<br />
**while the presence of a face is better than no face at all, the quality of the face matters much less.<br />
***it is quite possible that animating highly realistic faces inherently allows for residual attributes of the faces that are negative—for example making 3D human faces may produce gestures and animations that appear unnatural or disturbing<br />
**while most studies have found that interface agents have positive effects on task performance, these effects are overall actually quite small.<br />
<br />
* In addition to understanding human social behavior around computers, another extensive line of work examines humans’ responses to computers with more expressive, human-like qualities, such as faces and facial expressions. In general, these studies have found that anthropomorphic properties of computers influence users’ perceptions (e.g., [13, 70, 86]), attitude (e.g., [13]), and behavior around such systems (e.g., [48, 86, 105]). For example, Sproull et al. [86] report that people respond to a text-based interface differently than to a talking face. On the one hand, users are aroused more and present themselves more positively when interacting with a talking face. On the other hand, users are less relaxed or assured when interacting with a talking face. Another study shows that users find the interface with the anthropomorphic qualities—faces and facial expressions—more likeable and engaging, although such an interface takes the users’ effort to interpret the meaning of the human-like expressions and may even be a distraction [48, 86]. More recent studies show that human-like features with higher realism elicit more positive social interactions while having no significant impact on user task performance [105]. Furthermore, a study reveals that anthropomorphism may even elicit user objections due to users’ own biases (e.g., sexism) [70]. <ref name="zhou2019">Zhou, M. X., Yang, H., Mark, G., & Li, J. (2019). Trusting Virtual Agents: The Effect of Personality. ACM Trans. Interact. Intell. Syst, 9(3). https://doi.org/10.1145/3232077</ref><br />
<br />
* Physically present agent leads to better motivational outcomes than a voice or text-box. It is important to design agents realistically, because e.g. cartoon figures have been shown to diminish the positive motivating effects in comparison to realistic figures. <ref name="shiban2015"/><br />
<br />
* It was found also that attractive VT did not improve perceived value of exercise and perceived risks of health. This result is different from some prior studies (e.g. Shiban et al., 2015) which find that attractive virtual pedagogical agents are effective for engaging students in learning. There are two possible explanations for this observed difference. First, attractive virtual agents may catch the attention of users and make users more confident in using a VTS. However, they may also distract users from core learning materials (Moreno and Flowerday, 2006). Second, attractive agents are closer to friends rather than experts. The incongruence of expert perception may lead to contextual irrelevance which makes users take health-related information from virtual trainers lightly (Veletsianos, 2010). <ref name="kwok2021">Kwok, R. C. W., Leung, A. C. M., Hui, S. S. chuen, & Wong, C. C. K. (2021). Virtual trainer system: a tool to increase exercise participation and work productivity. Internet Research. https://doi.org/10.1108/INTR-04-2020-0236</ref><br />
<br />
* If McCloud’s (1993) framework is applied, a teacher character, representing the other to a higher extent than a learning companion, might benefit from more realism in the representation. A learning companion character, being to a higher extent conceived of as an extension of oneself, may, on the other hand, benefit from a more iconic representation. <ref name="gulz2006">Gulz, A., & Haake, M. (2006). Design of animated pedagogical agents - A look at their look. International Journal of Human Computer Studies, 64(4), 322–339. https://doi.org/10.1016/j.ijhcs.2005.08.006</ref><br />
<br />
* The findings of this experiment suggest a new design method for human-agent collaboration work. If we use an agent having a non-human-like appearance (for example, the robot-like agent), the user seemed to attribute much responsibility to the agent and not to trust the agent. We suggest using the agent having human-like appearance in human-agent collaboration work from this experiment. A question is raised about the commonly accepted beliefs about agent design for social tasks. We should consider the attribution of responsibility to construct trustworthy agents. <ref name="matsui2021">Matsui, T., & Koike, A. (2021). Who is to blame? The appearance of virtual agents and the attribution of perceived responsibility. Sensors, 21(8), 1–13. https://doi.org/10.3390/s21082646</ref><br />
<br />
====Behaviour and personality====<br />
<br />
<br />
===References===<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Related_Literature_Group_4,_design&diff=115209Related Literature Group 4, design2021-05-09T18:11:14Z<p>20182838: </p>
<hr />
<div><div style="font-family: 'Calibri'; font-size: 15px; line-height: 1.5; max-width: 1100px; word-wrap: break-word; color: #333; font-weight: 400; margin-left: auto; margin-right: auto; padding: 50px; background-color: white; padding-top: 25px;"><br />
<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Excerpts & citations </font><br />
<br />
====Gender====<br />
<br />
* Females chose to gender-match and to interact with a more realistic VA. Males exhibited little preference for either gender, and a greater preference than females for realistic VAs. Thus, where it is not feasible to gender-match in SSCO, the recommendation is to implement a realistic female VA. <ref name="payne2013">Payne, J., Szymkowiak, A., Robertson, P., & Johnson, G. (2013). Gendering the machine: Preferred virtual assistant gender and realism in self-service. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8108 LNAI, 106–115. https://doi.org/10.1007/978-3-642-40415-3_9</ref><br />
<br />
* Young and attractive female agent positively impacts interest in learning, an older an unattractive male agent does not impact motivation. <ref name="shiban2015">Shiban, Y., Schelhorn, I., Jobst, V., Hörnlein, A., Puppe, F., Pauli, P., & Mühlberger, A. (2015). The appearance effect: Influences of virtual agent features on performance and motivation. Computers in Human Behavior, 49, 5–11. https://doi.org/10.1016/j.chb.2015.01.077</ref><br />
<br />
* As research suggests that the combination of an agent’s gender and personality can play an important role in user perceptions and expectations [1, 37], employing a male agent instead may result in some significant differences in user perceptions or ratings of the agent. We encourage future work to investigate how gender and personality of a workplace productivity agent might influence user experience. <ref name="grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref><br />
<br />
* participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
<br />
* We found that the designed characteristics of VAs affects some aspects of user impressions (i.e. personality and trustworthiness) of the VA, while other impressions are not affected (i.e. social ability). We also found that gender matching between the agent and the user affect user impressions. // Gender similarity --> more trustworthy <ref name="akbar2018">Akbar, F., Grover, T., Mark, G., & Zhou, M. X. (2018, March 5). The effects of virtual agents’ characteristics on user impressions and language use. International Conference on Intelligent User Interfaces, Proceedings IUI. https://doi.org/10.1145/3180308.3180365</ref><br />
<br />
====Human-likeness====<br />
<br />
* In this study, it was noted that PIAs’ human-like features could influence the manner in which participants (i.e. males or females) related to a particular PIA and participants’ preference level of a particular PIA. The result of the Fischer’s exact test conducted in this study (Table V) found no statistically significant effect between participants’ gender (male or female) and their preferred gender of PIAs. It is concluded that the gender of participants (i.e. male or female) has no role (impact) on their preferences regarding the type, features, or gender of PIA, neither on their preference level of PIA. <ref name="mabanza2019">Mabanza, N. (2019). Gender influences on preference of pedagogical interface agents. 2018 International Conference on Intelligent and Innovative Computing Applications, ICONIC 2018. https://doi.org/10.1109/ICONIC.2018.8601292</ref><br />
<br />
* On the other hand, previous studies have also shown that developing overly humanized agents results in high expectations and uncanny feelings. <ref name="chaves2021">Chaves, A. P., & Gerosa, M. A. (2021). How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Desi | Enhanced Reader. International Journal of Human–Computer Interaction, 37(8), 729–758. https://doi.org/10.1080/10447318.2020.1841438</ref><br />
<br />
* Many participants saw the human-like appearance of the VA prototype as setting the wrong expectations in terms of its capabilities, and they were disappointed when the agent’s intelligence only extended towards responding to the their emotion and prompting more self-reflection. (…) We believe incorporating human-like qualities and emotional intelligence into future agents to be worthwhile; however, intelligence should also extend into other aspects of the agent’s capabilities in order to better help users be as efficient as possible in achieving their work goals. <ref name="grover2020"/><br />
<br />
* A study by Go and Sundar <ref name="go2019">Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j.chb.2019.01.020</ref> states that revealing the identity of a chatbot as a non-human can have a positive effect: user will have less high expectations about the conversation, and will be impressed when an agent shows human-like behaviour. Furthermore, they emphasize the importance of the conversational style between a human an a computer. When the dialogue resembles that of an actual human, perceived feelings of social presence and homophily will increase, leading to more positive attitudes towards the agent (and in turn potential desired behaviour consequences).<br />
<br />
* the presence of a representation produced more positive social interactions than not having a representation <ref name="Yee2017">Yee, N., Bailenson, J. N., & Rickertsen, K. (2007). A Meta-Analysis of the Impact of the Inclusion and Realism of Human-Like Faces on User Experiences in Interfaces.</ref><br />
** human-like representations with higher realism produced more positive social interactions than representations with lower realism; however, this effect was only found when subjective measures were used. Behavioral measures did not reveal a significant difference between representations of low and high realism. <br />
*** the difference we found may also be driven by demand characteristics. Participants interacting with an animated character (as opposed to a photograph) may suppose that the researcher is expecting a high appraisal.<br />
**while the presence of a face is better than no face at all, the quality of the face matters much less.<br />
***it is quite possible that animating highly realistic faces inherently allows for residual attributes of the faces that are negative—for example making 3D human faces may produce gestures and animations that appear unnatural or disturbing<br />
**while most studies have found that interface agents have positive effects on task performance, these effects are overall actually quite small.<br />
<br />
* In addition to understanding human social behavior around computers, another extensive line of work examines humans’ responses to computers with more expressive, human-like qualities, such as faces and facial expressions. In general, these studies have found that anthropomorphic properties of computers influence users’ perceptions (e.g., [13, 70, 86]), attitude (e.g., [13]), and behavior around such systems (e.g., [48, 86, 105]). For example, Sproull et al. [86] report that people respond to a text-based interface differently than to a talking face. On the one hand, users are aroused more and present themselves more positively when interacting with a talking face. On the other hand, users are less relaxed or assured when interacting with a talking face. Another study shows that users find the interface with the anthropomorphic qualities—faces and facial expressions—more likeable and engaging, although such an interface takes the users’ effort to interpret the meaning of the human-like expressions and may even be a distraction [48, 86]. More recent studies show that human-like features with higher realism elicit more positive social interactions while having no significant impact on user task performance [105]. Furthermore, a study reveals that anthropomorphism may even elicit user objections due to users’ own biases (e.g., sexism) [70]. <ref name="zhou2019">Zhou, M. X., Yang, H., Mark, G., & Li, J. (2019). Trusting Virtual Agents: The Effect of Personality. ACM Trans. Interact. Intell. Syst, 9(3). https://doi.org/10.1145/3232077</ref><br />
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===References===<br />
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===Gender===<br />
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* Females chose to gender-match and to interact with a more realistic VA. Males exhibited little preference for either gender, and a greater preference than females for realistic VAs. Thus, where it is not feasible to gender-match in SSCO, the recommendation is to implement a realistic female VA. <ref name="payne2013">Payne, J., Szymkowiak, A., Robertson, P., & Johnson, G. (2013). Gendering the machine: Preferred virtual assistant gender and realism in self-service. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8108 LNAI, 106–115. https://doi.org/10.1007/978-3-642-40415-3_9</ref><br />
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* Young and attractive female agent positively impacts interest in learning, an older an unattractive male agent does not impact motivation. <ref name="shiban2015">Shiban, Y., Schelhorn, I., Jobst, V., Hörnlein, A., Puppe, F., Pauli, P., & Mühlberger, A. (2015). The appearance effect: Influences of virtual agent features on performance and motivation. Computers in Human Behavior, 49, 5–11. https://doi.org/10.1016/j.chb.2015.01.077</ref><br />
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* As research suggests that the combination of an agent’s gender and personality can play an important role in user perceptions and expectations [1, 37], employing a male agent instead may result in some significant differences in user perceptions or ratings of the agent. We encourage future work to investigate how gender and personality of a workplace productivity agent might influence user experience. <ref name="grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref><br />
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* participants prefer same-gender agents when they are asked to choose their preferred agent as presenter for a multimedia slideshow. <ref name="shiban2015"/><br />
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* We found that the designed characteristics of VAs affects some aspects of user impressions (i.e. personality and trustworthiness) of the VA, while other impressions are not affected (i.e. social ability). We also found that gender matching between the agent and the user affect user impressions. // Gender similarity --> more trustworthy <ref name="akbar2018">Akbar, F., Grover, T., Mark, G., & Zhou, M. X. (2018, March 5). The effects of virtual agents’ characteristics on user impressions and language use. International Conference on Intelligent User Interfaces, Proceedings IUI. https://doi.org/10.1145/3180308.3180365</ref><br />
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===Gender===<br />
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* Females chose to gender-match and to interact with a more realistic VA. Males exhibited little preference for either gender, and a greater preference than females for realistic VAs. Thus, where it is not feasible to gender-match in SSCO, the recommendation is to implement a realistic female VA <ref name=payne2013>Payne, J., Szymkowiak, A., Robertson, P., & Johnson, G. (2013). Gendering the machine: Preferred virtual assistant gender and realism in self-service. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8108 LNAI, 106–115. https://doi.org/10.1007/978-3-642-40415-3_9</ref><br />
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<div>===Gender===<br />
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* Females chose to gender-match and to interact with a more realistic VA. Males exhibited little preference for either gender, and a greater preference than females for realistic VAs. Thus, where it is not feasible to gender-match in SSCO, the recommendation is to implement a realistic female VA <ref name=payne2013>Payne, J., Szymkowiak, A., Robertson, P., & Johnson, G. (2013). Gendering the machine: Preferred virtual assistant gender and realism in self-service. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8108 LNAI, 106–115. https://doi.org/10.1007/978-3-642-40415-3_9</ref><br />
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<div>===Gender===<br />
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* Females chose to gender-match and to interact with a more realistic VA. Males exhibited little preference for either gender, and a greater preference than females for realistic VAs. Thus, where it is not feasible to gender-match in SSCO, the recommendation is to implement a realistic female VA <ref name=payne2013>Payne, J., Szymkowiak, A., Robertson, P., & Johnson, G. (2013). Gendering the machine: Preferred virtual assistant gender and realism in self-service. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8108 LNAI, 106–115. https://doi.org/10.1007/978-3-642-40415-3_9</ref><br />
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<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Coco, The Computer Companion </font><br />
<br />
==Group Description==<br />
<br />
===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
<br />
===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
<br />
<br />
<br />
== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
<br />
<br />
<br />
==Problem Statement and Objectives==<br />
<br />
===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
<br />
Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
<br />
=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
<br />
<br />
<br />
==USE: User, Society and Enterprise==<br />
<br />
<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
<br />
=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
<br />
Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
<br />
Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
<br />
=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
<br />
=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
<br />
==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
<br />
Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref> have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
<br />
Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
<br />
Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
<br />
===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
<br />
''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
<br />
<br />
=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
<br />
''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
<br />
<br />
===Survey===<br />
<br />
To find out more about the group of people that will be most interested in having a virtual companion, a survey will be conducted. This survey acts as a means the specify the target group and to get to know their preferences regarding the functionality of the virtual agent. <br />
<br />
An early draft of the survey can be found [https://docs.google.com/forms/d/e/1FAIpQLScLvw_KTaaE6cRBcPJzjQ5dLDJIcvuLODW9L-skfng-X9q0Gg/viewform?usp=pp_url here].<br />
<br />
<br />
===Planning===<br />
<br />
[[File:0LAUK0 Planning Group 4 (Q4).PNG|1100 px|alt=Planning Group 4]]<br />
<br />
==Research==<br />
<br />
===State of the Art===<br />
<br />
<br />
'''''Productivity agents'''''<br />
<br />
As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
<br />
Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
<br />
With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
<br />
<br />
'''''Companion agents'''''<br />
<br />
When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. <ref name="Ta"/> investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements.<br />
<br />
<br />
'''''Physical health agents'''''<br />
<br />
Cambo, Avrahami, & Lee <ref name = "Cambo2017">Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021</ref> investigated the application “BreakSense” and concluded that the technology should let the user decide for themselves when to take a break. They discovered that in this way, the physical activity became part of their daily routine.<br />
<br />
<br />
'''''Computer assistants'''''<br />
<br />
Although these agents focus on specific tasks, there also exist personal computer assistants that are developed to help, for instance children, more generally with their daily activities. The study by Kessens et al. <ref name = "Kessens2009">Kessens, J. M., Neerincx, M. A., Looije, R., Kroes, M., & Bloothooft, G. (2009). Facial and vocal emotion expression of a personal computer assistant to engage, educate and motivate children. Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009. https://doi.org/10.1109/ACII.2009.5349582</ref> investigated such a computer assistant, namely the Philips iCat. This animated virtual robot can show varying emotional expressions and fulfilled the roles of both companion, educator and motivator.<br />
<br />
===Related Literature===<br />
<br />
'''''General'''''<br />
<br />
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]].<br />
<br />
<br />
'''''Design of the Virtual Agent'''''<br />
<br />
In week 3 a separate literature study has been conducted, specifically focused on the design aspect of the virtual agent. As a result of this study, it is planned to form a scientifically grounded recommendation for the design of the virtual agent, based on the direction of all papers taken together.<br />
<br />
An overview of the collected data regarding the design and appearance of the agent can be found [[Related Literature Group 4, design|here]].<br />
<br />
===Motivation for virtual agent===<br />
<br />
This section contains the motivation for the decision to develop a virtual agent instead of a physical robot. Several important topics will be discussed to reach this, namely emotional intelligence, embodiment and costs. '''Disclaimer: this is still a concept and will thus be extended later'''<br />
<br />
'''''Emotional intelligence'''''<br />
* Without emotional intelligence, artificial agents can display behaviours that can be perceived as unexpected and confusing by humans. Moreover, their behaviour can be misinterpreted and can also violate human expectations, which could then possibly cause emotional harm (Fan, Scheutz, Lohani, Mccoy, & Stokes) <ref name = "Fan2017">Fan, L., Scheutz, M., Lohani, M., Mccoy, M., & Stokes, C. (2017). Do We Need Emotionally Intelligent Artificial Agents? First Results of Human Perceptions of Emotional Intelligence in Humans Compared to Robots. Springer International Publishing AG 2017, 129–141. https://doi.org/10.1007/978-3-319-67401-8_15</ref>.<br />
* Artificial agents with high emotional intelligence are being perceived as more trustworthy compared to robots that do not display intelligent behaviour (Fan et al.) <ref name="Fan2017"/>. <br />
<br />
'''''Physical versus simulated embodiment'''''<br />
* The physical embodiment that enhanced social telepresence decreases the smoothness of speech (Tanaka, Nakanishi, & Ishiguro) <ref name = "Tanaka2014">Tanaka, K., Nakanishi, H., Ishiguro, H. (2014). Comparing video, avatar, and robot mediated communication: Pros and cons of embodiment. Communications in Computer and Information Science, 96–110. https://doi.org/10.1007/978-3-662-44651-5_9</ref>.<br />
* According to Milne et al., the most important advantages of virtual agents are that they can be accessed at any time and that they do not need to be repaired (Milne, Luerssen, Lewis, Leibbrandt, & Powers) <ref name = "Milne2010">Milne, M., Luerssen, M. H., Lewis, T. W., Leibbrandt, R. E., Powers, D. M. W. (2010). Development of a virtual agent based social tutor for children with autism spectrum disorders.Proceedings of the International Joint Conference on Neural Networks, 1–9. https://doi.org/10.1109/IJCNN.2010.5596584</ref>.<br />
* The article of Lee, Jung, Kim & Kim researches one of the most fundamental questions about social robots, namely whether or not physical embodiment adds value for good social interaction compared to disembodied social robots. For instance, manufacturing of embodied robots is very expensive, and many technical difficulties can arise because of the many embedded sensors and motors. Moreover, “physical embodied agents may facilitate better social interaction with its users by providing more affordance for proper social interaction”, where affordance means the fundamental properties of a device that determine its ways of use. <br />
* Lee et al. also found that if an embodied robot had anthropomorphic-physical embodiment, expectations of the robot were very high. When it then did not have the ability to react to touch-input, the high expectations of the people dropped and they became frustrated and disappointed in the robot. This might be a general negative effect of physical embodiment (especially for lonely people that might use the robot as a real companion). '''source'''<br />
* Wang & Rau have researched the effect on users’ responses of different types of social robots. They distinghuised on two factors; embodiment and substrates. It has been found that people prefer that the embodiment matches the substrate. This means that they would prefer if a physical robot has the best effect in a physical substrate, but a virtual robot the best in a virtual substrate. '''source'''<br />
<br />
'''''Costs'''''<br />
* Paper on design of a low-cost social robot ‘Philos’: commercial value = $3.000 dollars, associated software = free. Paro costs $6.000 dollars and Nao over $15.000 '''(source: Puehn, Liu, Feng, Hornfeck, & Lee, 2014)'''.<br />
* "Physical robot platforms are typically very expensive to build, alter and maintain." However, software can easily be updated '''(Avramova, Yang, Li, Peters, & Skantze, 2017)'''<br />
* https://www.webfx.com/internet-marketing/ai-pricing.html is an interesting website to see how much it will cost to develop AI software. Comparing e.g. chatbots, large data analysis systems and virtual assistants.<br />
<br />
==References==<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=PRE2020_4_Group4&diff=115200PRE2020 4 Group42021-05-09T17:50:14Z<p>20182838: /* Related Literature */</p>
<hr />
<div><div style="font-family: 'Calibri'; font-size: 15px; line-height: 1.5; max-width: 1100px; word-wrap: break-word; color: #333; font-weight: 400; margin-left: auto; margin-right: auto; padding: 50px; background-color: white; padding-top: 25px;"><br />
<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Coco, The Computer Companion </font><br />
<br />
==Group Description==<br />
<br />
===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
<br />
===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
<br />
<br />
<br />
== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
<br />
<br />
<br />
==Problem Statement and Objectives==<br />
<br />
===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
<br />
Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
<br />
=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
<br />
<br />
<br />
==USE: User, Society and Enterprise==<br />
<br />
<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
<br />
=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
<br />
Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
<br />
Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
<br />
=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
<br />
=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
<br />
==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
<br />
Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref> have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
<br />
Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
<br />
Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
<br />
===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
<br />
''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
<br />
<br />
=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
<br />
''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
<br />
<br />
===Survey===<br />
<br />
To find out more about the group of people that will be most interested in having a virtual companion, a survey will be conducted. This survey acts as a means the specify the target group and to get to know their preferences regarding the functionality of the virtual agent. <br />
<br />
An early draft of the survey can be found [https://docs.google.com/forms/d/e/1FAIpQLScLvw_KTaaE6cRBcPJzjQ5dLDJIcvuLODW9L-skfng-X9q0Gg/viewform?usp=pp_url here].<br />
<br />
<br />
===Planning===<br />
<br />
[[File:0LAUK0 Planning Group 4 (Q4).PNG|1100 px|alt=Planning Group 4]]<br />
<br />
==Research==<br />
<br />
===State of the Art===<br />
<br />
<br />
'''''Productivity agents'''''<br />
<br />
As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
<br />
Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
<br />
With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
<br />
<br />
'''''Companion agents'''''<br />
<br />
When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. <ref name="Ta"/> investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements.<br />
<br />
<br />
'''''Physical health agents'''''<br />
<br />
Cambo, Avrahami, & Lee <ref name = "Cambo2017">Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021</ref> investigated the application “BreakSense” and concluded that the technology should let the user decide for themselves when to take a break. They discovered that in this way, the physical activity became part of their daily routine.<br />
<br />
<br />
'''''Computer assistants'''''<br />
<br />
Although these agents focus on specific tasks, there also exist personal computer assistants that are developed to help, for instance children, more generally with their daily activities. The study by Kessens et al. <ref name = "Kessens2009">Kessens, J. M., Neerincx, M. A., Looije, R., Kroes, M., & Bloothooft, G. (2009). Facial and vocal emotion expression of a personal computer assistant to engage, educate and motivate children. Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009. https://doi.org/10.1109/ACII.2009.5349582</ref> investigated such a computer assistant, namely the Philips iCat. This animated virtual robot can show varying emotional expressions and fulfilled the roles of both companion, educator and motivator.<br />
<br />
===Related Literature===<br />
<br />
'''''General'''''<br />
<br />
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4, general|here]].<br />
<br />
<br />
'''''Design of the Virtual Agent'''''<br />
<br />
In week 3 a separate literature study has been conducted, specifically focused on the design aspect of the virtual agent. As a result of this study, it is planned to form a scientifically grounded recommendation for the design of the virtual agent, based on the direction of all papers taken together.<br />
<br />
An overview of the collected data regarding the design and appearance of the agent can be found [[Related Literature Group 4, design|here]].<br />
<br />
===Motivation for virtual agent===<br />
<br />
This section contains the motivation for the decision to develop a virtual agent instead of a physical robot. Several important topics will be discussed to reach this, namely emotional intelligence, embodiment and costs. '''Disclaimer: this is still a concept and will thus be extended later'''<br />
<br />
'''''Emotional intelligence'''''<br />
* Without emotional intelligence, artificial agents can display behaviours that can be perceived as unexpected and confusing by humans. Moreover, their behaviour can be misinterpreted and can also violate human expectations, which could then possibly cause emotional harm (Fan, Scheutz, Lohani, Mccoy, & Stokes) <ref name = "Fan2017">Fan, L., Scheutz, M., Lohani, M., Mccoy, M., & Stokes, C. (2017). Do We Need Emotionally Intelligent Artificial Agents? First Results of Human Perceptions of Emotional Intelligence in Humans Compared to Robots. Springer International Publishing AG 2017, 129–141. https://doi.org/10.1007/978-3-319-67401-8_15</ref>.<br />
* Artificial agents with high emotional intelligence are being perceived as more trustworthy compared to robots that do not display intelligent behaviour (Fan et al.) <ref name="Fan2017"/>. <br />
<br />
'''''Physical versus simulated embodiment'''''<br />
* The physical embodiment that enhanced social telepresence decreases the smoothness of speech (Tanaka, Nakanishi, & Ishiguro) <ref name = "Tanaka2014">Tanaka, K., Nakanishi, H., Ishiguro, H. (2014). Comparing video, avatar, and robot mediated communication: Pros and cons of embodiment. Communications in Computer and Information Science, 96–110. https://doi.org/10.1007/978-3-662-44651-5_9</ref>.<br />
* According to Milne et al., the most important advantages of virtual agents are that they can be accessed at any time and that they do not need to be repaired (Milne, Luerssen, Lewis, Leibbrandt, & Powers) <ref name = "Milne2010">Milne, M., Luerssen, M. H., Lewis, T. W., Leibbrandt, R. E., Powers, D. M. W. (2010). Development of a virtual agent based social tutor for children with autism spectrum disorders.Proceedings of the International Joint Conference on Neural Networks, 1–9. https://doi.org/10.1109/IJCNN.2010.5596584</ref>.<br />
* The article of Lee, Jung, Kim & Kim researches one of the most fundamental questions about social robots, namely whether or not physical embodiment adds value for good social interaction compared to disembodied social robots. For instance, manufacturing of embodied robots is very expensive, and many technical difficulties can arise because of the many embedded sensors and motors. Moreover, “physical embodied agents may facilitate better social interaction with its users by providing more affordance for proper social interaction”, where affordance means the fundamental properties of a device that determine its ways of use. <br />
* Lee et al. also found that if an embodied robot had anthropomorphic-physical embodiment, expectations of the robot were very high. When it then did not have the ability to react to touch-input, the high expectations of the people dropped and they became frustrated and disappointed in the robot. This might be a general negative effect of physical embodiment (especially for lonely people that might use the robot as a real companion). '''source'''<br />
* Wang & Rau have researched the effect on users’ responses of different types of social robots. They distinghuised on two factors; embodiment and substrates. It has been found that people prefer that the embodiment matches the substrate. This means that they would prefer if a physical robot has the best effect in a physical substrate, but a virtual robot the best in a virtual substrate. '''source'''<br />
<br />
'''''Costs'''''<br />
* Paper on design of a low-cost social robot ‘Philos’: commercial value = $3.000 dollars, associated software = free. Paro costs $6.000 dollars and Nao over $15.000 '''(source: Puehn, Liu, Feng, Hornfeck, & Lee, 2014)'''.<br />
* "Physical robot platforms are typically very expensive to build, alter and maintain." However, software can easily be updated '''(Avramova, Yang, Li, Peters, & Skantze, 2017)'''<br />
* https://www.webfx.com/internet-marketing/ai-pricing.html is an interesting website to see how much it will cost to develop AI software. Comparing e.g. chatbots, large data analysis systems and virtual assistants.<br />
<br />
==References==<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=PRE2020_4_Group4&diff=115199PRE2020 4 Group42021-05-09T17:49:56Z<p>20182838: /* Related Literature */</p>
<hr />
<div><div style="font-family: 'Calibri'; font-size: 15px; line-height: 1.5; max-width: 1100px; word-wrap: break-word; color: #333; font-weight: 400; margin-left: auto; margin-right: auto; padding: 50px; background-color: white; padding-top: 25px;"><br />
<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Coco, The Computer Companion </font><br />
<br />
==Group Description==<br />
<br />
===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
<br />
===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
<br />
<br />
<br />
== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
<br />
<br />
<br />
==Problem Statement and Objectives==<br />
<br />
===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
<br />
Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
<br />
=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
<br />
<br />
<br />
==USE: User, Society and Enterprise==<br />
<br />
<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
<br />
=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
<br />
Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
<br />
Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
<br />
=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
<br />
=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
<br />
==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
<br />
Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref> have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
<br />
Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
<br />
Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
<br />
===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
<br />
''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
<br />
<br />
=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
<br />
''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
<br />
<br />
===Survey===<br />
<br />
To find out more about the group of people that will be most interested in having a virtual companion, a survey will be conducted. This survey acts as a means the specify the target group and to get to know their preferences regarding the functionality of the virtual agent. <br />
<br />
An early draft of the survey can be found [https://docs.google.com/forms/d/e/1FAIpQLScLvw_KTaaE6cRBcPJzjQ5dLDJIcvuLODW9L-skfng-X9q0Gg/viewform?usp=pp_url here].<br />
<br />
<br />
===Planning===<br />
<br />
[[File:0LAUK0 Planning Group 4 (Q4).PNG|1100 px|alt=Planning Group 4]]<br />
<br />
==Research==<br />
<br />
===State of the Art===<br />
<br />
<br />
'''''Productivity agents'''''<br />
<br />
As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
<br />
Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
<br />
With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
<br />
<br />
'''''Companion agents'''''<br />
<br />
When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. <ref name="Ta"/> investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements.<br />
<br />
<br />
'''''Physical health agents'''''<br />
<br />
Cambo, Avrahami, & Lee <ref name = "Cambo2017">Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021</ref> investigated the application “BreakSense” and concluded that the technology should let the user decide for themselves when to take a break. They discovered that in this way, the physical activity became part of their daily routine.<br />
<br />
<br />
'''''Computer assistants'''''<br />
<br />
Although these agents focus on specific tasks, there also exist personal computer assistants that are developed to help, for instance children, more generally with their daily activities. The study by Kessens et al. <ref name = "Kessens2009">Kessens, J. M., Neerincx, M. A., Looije, R., Kroes, M., & Bloothooft, G. (2009). Facial and vocal emotion expression of a personal computer assistant to engage, educate and motivate children. Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009. https://doi.org/10.1109/ACII.2009.5349582</ref> investigated such a computer assistant, namely the Philips iCat. This animated virtual robot can show varying emotional expressions and fulfilled the roles of both companion, educator and motivator.<br />
<br />
===Related Literature===<br />
<br />
'''''General'''''<br />
<br />
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4, general|here]].<br />
<br />
<br />
'''''Design of the Virtual Agent'''''<br />
<br />
In week 3, a separate literature study has been conducted, specifically focused on the design aspect of the virtual agent. As a result of this study, it is planned to form a scientifically grounded recommendation for the design of the virtual agent, based on the direction of all papers taken together.<br />
<br />
An overview of the collected data regarding the design and appearance of the agent can be found [[Related Literature Group 4, design|here]].<br />
<br />
===Motivation for virtual agent===<br />
<br />
This section contains the motivation for the decision to develop a virtual agent instead of a physical robot. Several important topics will be discussed to reach this, namely emotional intelligence, embodiment and costs. '''Disclaimer: this is still a concept and will thus be extended later'''<br />
<br />
'''''Emotional intelligence'''''<br />
* Without emotional intelligence, artificial agents can display behaviours that can be perceived as unexpected and confusing by humans. Moreover, their behaviour can be misinterpreted and can also violate human expectations, which could then possibly cause emotional harm (Fan, Scheutz, Lohani, Mccoy, & Stokes) <ref name = "Fan2017">Fan, L., Scheutz, M., Lohani, M., Mccoy, M., & Stokes, C. (2017). Do We Need Emotionally Intelligent Artificial Agents? First Results of Human Perceptions of Emotional Intelligence in Humans Compared to Robots. Springer International Publishing AG 2017, 129–141. https://doi.org/10.1007/978-3-319-67401-8_15</ref>.<br />
* Artificial agents with high emotional intelligence are being perceived as more trustworthy compared to robots that do not display intelligent behaviour (Fan et al.) <ref name="Fan2017"/>. <br />
<br />
'''''Physical versus simulated embodiment'''''<br />
* The physical embodiment that enhanced social telepresence decreases the smoothness of speech (Tanaka, Nakanishi, & Ishiguro) <ref name = "Tanaka2014">Tanaka, K., Nakanishi, H., Ishiguro, H. (2014). Comparing video, avatar, and robot mediated communication: Pros and cons of embodiment. Communications in Computer and Information Science, 96–110. https://doi.org/10.1007/978-3-662-44651-5_9</ref>.<br />
* According to Milne et al., the most important advantages of virtual agents are that they can be accessed at any time and that they do not need to be repaired (Milne, Luerssen, Lewis, Leibbrandt, & Powers) <ref name = "Milne2010">Milne, M., Luerssen, M. H., Lewis, T. W., Leibbrandt, R. E., Powers, D. M. W. (2010). Development of a virtual agent based social tutor for children with autism spectrum disorders.Proceedings of the International Joint Conference on Neural Networks, 1–9. https://doi.org/10.1109/IJCNN.2010.5596584</ref>.<br />
* The article of Lee, Jung, Kim & Kim researches one of the most fundamental questions about social robots, namely whether or not physical embodiment adds value for good social interaction compared to disembodied social robots. For instance, manufacturing of embodied robots is very expensive, and many technical difficulties can arise because of the many embedded sensors and motors. Moreover, “physical embodied agents may facilitate better social interaction with its users by providing more affordance for proper social interaction”, where affordance means the fundamental properties of a device that determine its ways of use. <br />
* Lee et al. also found that if an embodied robot had anthropomorphic-physical embodiment, expectations of the robot were very high. When it then did not have the ability to react to touch-input, the high expectations of the people dropped and they became frustrated and disappointed in the robot. This might be a general negative effect of physical embodiment (especially for lonely people that might use the robot as a real companion). '''source'''<br />
* Wang & Rau have researched the effect on users’ responses of different types of social robots. They distinghuised on two factors; embodiment and substrates. It has been found that people prefer that the embodiment matches the substrate. This means that they would prefer if a physical robot has the best effect in a physical substrate, but a virtual robot the best in a virtual substrate. '''source'''<br />
<br />
'''''Costs'''''<br />
* Paper on design of a low-cost social robot ‘Philos’: commercial value = $3.000 dollars, associated software = free. Paro costs $6.000 dollars and Nao over $15.000 '''(source: Puehn, Liu, Feng, Hornfeck, & Lee, 2014)'''.<br />
* "Physical robot platforms are typically very expensive to build, alter and maintain." However, software can easily be updated '''(Avramova, Yang, Li, Peters, & Skantze, 2017)'''<br />
* https://www.webfx.com/internet-marketing/ai-pricing.html is an interesting website to see how much it will cost to develop AI software. Comparing e.g. chatbots, large data analysis systems and virtual assistants.<br />
<br />
==References==<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=PRE2020_4_Group4&diff=115198PRE2020 4 Group42021-05-09T17:42:03Z<p>20182838: /* Related Literature */</p>
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<div><div style="font-family: 'Calibri'; font-size: 15px; line-height: 1.5; max-width: 1100px; word-wrap: break-word; color: #333; font-weight: 400; margin-left: auto; margin-right: auto; padding: 50px; background-color: white; padding-top: 25px;"><br />
<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Coco, The Computer Companion </font><br />
<br />
==Group Description==<br />
<br />
===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
<br />
===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
<br />
<br />
<br />
== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
<br />
<br />
<br />
==Problem Statement and Objectives==<br />
<br />
===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
<br />
Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
<br />
=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
<br />
<br />
<br />
==USE: User, Society and Enterprise==<br />
<br />
<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
<br />
=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
<br />
Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
<br />
Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
<br />
=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
<br />
=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
<br />
==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
<br />
Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref> have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
<br />
Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
<br />
Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
<br />
===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
<br />
''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
<br />
<br />
=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
<br />
''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
<br />
<br />
===Survey===<br />
<br />
To find out more about the group of people that will be most interested in having a virtual companion, a survey will be conducted. This survey acts as a means the specify the target group and to get to know their preferences regarding the functionality of the virtual agent. <br />
<br />
An early draft of the survey can be found [https://docs.google.com/forms/d/e/1FAIpQLScLvw_KTaaE6cRBcPJzjQ5dLDJIcvuLODW9L-skfng-X9q0Gg/viewform?usp=pp_url here].<br />
<br />
<br />
===Planning===<br />
<br />
[[File:0LAUK0 Planning Group 4 (Q4).PNG|1100 px|alt=Planning Group 4]]<br />
<br />
==Research==<br />
<br />
===State of the Art===<br />
<br />
<br />
'''''Productivity agents'''''<br />
<br />
As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
<br />
Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
<br />
With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
<br />
<br />
'''''Companion agents'''''<br />
<br />
When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. <ref name="Ta"/> investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements.<br />
<br />
<br />
'''''Physical health agents'''''<br />
<br />
Cambo, Avrahami, & Lee <ref name = "Cambo2017">Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021</ref> investigated the application “BreakSense” and concluded that the technology should let the user decide for themselves when to take a break. They discovered that in this way, the physical activity became part of their daily routine.<br />
<br />
<br />
'''''Computer assistants'''''<br />
<br />
Although these agents focus on specific tasks, there also exist personal computer assistants that are developed to help, for instance children, more generally with their daily activities. The study by Kessens et al. <ref name = "Kessens2009">Kessens, J. M., Neerincx, M. A., Looije, R., Kroes, M., & Bloothooft, G. (2009). Facial and vocal emotion expression of a personal computer assistant to engage, educate and motivate children. Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009. https://doi.org/10.1109/ACII.2009.5349582</ref> investigated such a computer assistant, namely the Philips iCat. This animated virtual robot can show varying emotional expressions and fulfilled the roles of both companion, educator and motivator.<br />
<br />
===Related Literature===<br />
<br />
====General====<br />
<br />
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]].<br />
<br />
<br />
====Design of the Virtual Agent====<br />
<br />
===Motivation for virtual agent===<br />
<br />
This section contains the motivation for the decision to develop a virtual agent instead of a physical robot. Several important topics will be discussed to reach this, namely emotional intelligence, embodiment and costs. '''Disclaimer: this is still a concept and will thus be extended later'''<br />
<br />
'''''Emotional intelligence'''''<br />
* Without emotional intelligence, artificial agents can display behaviours that can be perceived as unexpected and confusing by humans. Moreover, their behaviour can be misinterpreted and can also violate human expectations, which could then possibly cause emotional harm (Fan, Scheutz, Lohani, Mccoy, & Stokes) <ref name = "Fan2017">Fan, L., Scheutz, M., Lohani, M., Mccoy, M., & Stokes, C. (2017). Do We Need Emotionally Intelligent Artificial Agents? First Results of Human Perceptions of Emotional Intelligence in Humans Compared to Robots. Springer International Publishing AG 2017, 129–141. https://doi.org/10.1007/978-3-319-67401-8_15</ref>.<br />
* Artificial agents with high emotional intelligence are being perceived as more trustworthy compared to robots that do not display intelligent behaviour (Fan et al.) <ref name="Fan2017"/>. <br />
<br />
'''''Physical versus simulated embodiment'''''<br />
* The physical embodiment that enhanced social telepresence decreases the smoothness of speech (Tanaka, Nakanishi, & Ishiguro) <ref name = "Tanaka2014">Tanaka, K., Nakanishi, H., Ishiguro, H. (2014). Comparing video, avatar, and robot mediated communication: Pros and cons of embodiment. Communications in Computer and Information Science, 96–110. https://doi.org/10.1007/978-3-662-44651-5_9</ref>.<br />
* According to Milne et al., the most important advantages of virtual agents are that they can be accessed at any time and that they do not need to be repaired (Milne, Luerssen, Lewis, Leibbrandt, & Powers) <ref name = "Milne2010">Milne, M., Luerssen, M. H., Lewis, T. W., Leibbrandt, R. E., Powers, D. M. W. (2010). Development of a virtual agent based social tutor for children with autism spectrum disorders.Proceedings of the International Joint Conference on Neural Networks, 1–9. https://doi.org/10.1109/IJCNN.2010.5596584</ref>.<br />
* The article of Lee, Jung, Kim & Kim researches one of the most fundamental questions about social robots, namely whether or not physical embodiment adds value for good social interaction compared to disembodied social robots. For instance, manufacturing of embodied robots is very expensive, and many technical difficulties can arise because of the many embedded sensors and motors. Moreover, “physical embodied agents may facilitate better social interaction with its users by providing more affordance for proper social interaction”, where affordance means the fundamental properties of a device that determine its ways of use. <br />
* Lee et al. also found that if an embodied robot had anthropomorphic-physical embodiment, expectations of the robot were very high. When it then did not have the ability to react to touch-input, the high expectations of the people dropped and they became frustrated and disappointed in the robot. This might be a general negative effect of physical embodiment (especially for lonely people that might use the robot as a real companion). '''source'''<br />
* Wang & Rau have researched the effect on users’ responses of different types of social robots. They distinghuised on two factors; embodiment and substrates. It has been found that people prefer that the embodiment matches the substrate. This means that they would prefer if a physical robot has the best effect in a physical substrate, but a virtual robot the best in a virtual substrate. '''source'''<br />
<br />
'''''Costs'''''<br />
* Paper on design of a low-cost social robot ‘Philos’: commercial value = $3.000 dollars, associated software = free. Paro costs $6.000 dollars and Nao over $15.000 '''(source: Puehn, Liu, Feng, Hornfeck, & Lee, 2014)'''.<br />
* "Physical robot platforms are typically very expensive to build, alter and maintain." However, software can easily be updated '''(Avramova, Yang, Li, Peters, & Skantze, 2017)'''<br />
* https://www.webfx.com/internet-marketing/ai-pricing.html is an interesting website to see how much it will cost to develop AI software. Comparing e.g. chatbots, large data analysis systems and virtual assistants.<br />
<br />
==References==<br />
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<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Related_Literature_Group_4&diff=115197Related Literature Group 42021-05-09T17:40:54Z<p>20182838: </p>
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<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Summaries </font><br />
<br />
===Problem Statement===<br />
<br />
* In the paper by Xiao et al., it was researched what kind of impact working from home has on social, behavioural and physical well-being during COVID-19. They distributed a questionnaire in which 988 valid responses were gathered. The sample had an average age of 40.9. They found that working from home, full time can contribute to mental issues for people that live online. These mental issues are for example isolation and depression, because these people do not have face-to-face interactions and do not receive social support from people living in the same home. <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<br />
* The article also found that there can be work-family conflict inside of the house. This means that it is hard for people to separate work and family from each other because the boundaries are very blurred when working from home. Most participants had a hard time balancing work schedules because they could for example be interrupted by their family members. Emotional exhaustion is a possible result of this ongoing work-family conflict. <ref name='Xiao'/><br />
<br />
*The paper wanted to investigate the experience of home office workers before and during COVID-19, focused on the self-perception of vocal fatigue and physical pain. They found that mental fatigue has increased for home office workers. It requires them greater concentration during communication, they spend more time looking at a computer screen, and more use of headphones. This creates visual, auditory, vocal, and mainly mental overload <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>.<br />
<br />
* Additionally, the researchers found that there are physical health problems that can arise from working from home. These problems can for example arise because employees do not have the ability to walk around in the office space, or outside in between meetings. Additionally, the high exposure to computer screens can result in fatigue, tiredness, headaches and eye-related symptoms. <ref name='Xiao'/><br />
<br />
* The goal of the following study was to investigate the prevalence of unhealthy behaviour before and during the COVID-19 quarantine amongst Brazilian adults. In total, data of 38.535 adults was gathered. Participants had to report the frequency of certain feelings, such as sadness, happiness etc. Additionally, they were asked to report the frequency and duration of their physical activities and their TV and computer/tablet use from before and during the COVID-19 pandemic. They found that, because of quarantine, people showed an increase of sedentary behaviours (more than 8 hours per day) and a decrease of physical activities. This can affect cardiovascular, metabolic, and mental health, and this all increases the risk of mortality. Furthermore, they found that the unhealthy behavior was also associated with feelings of loneliness, sadness and anxiety <ref name='Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095</ref><br />
<br />
* The paper researched constraints of mandatory home education due to the COVID-19 pandemic, from the perspective of the parents of first to ninth graders. It has been found that parents especially have time, expertise and technical restrictions. To obtain this, they would suggest more interaction with the teacher, both the children and the parents.<ref name='Brom'>Brom, C., Lukavský, J., Greger, D., Hannemann, T., Straková, J., & Švaříček, R. (2020). Mandatory Home Education During the COVID-19 Lockdown in the Czech Republic: A Rapid Survey of 1st-9th Graders’ Parents. Frontiers in Education, 5. https://doi.org/10.3389/feduc.2020.00103</ref><br />
<br />
===State of the Art===<br />
<br />
* There exists already a platform, called “X5Learn”, that helps its users (both students and teachers) with online education purposes. It can help students, for instance, by providing personal recommendations and adapting to their individual learning preferences. On the other hand, it enables collaboration of sources for teachers. Special about this platform is the fact that it combines both human-centered design, AI, and software tools. In this way, it makes sure that the service is easy, intuitive, and transparent to its users. <ref name='Perez-Ortiz'>Perez-Ortiz, M., Dormann, C., Rogers, Y., Bulathwela, S., Kreitmayer, S., Yilmaz, E., Noss, R., & Shawe-Taylor, J. (2021). X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI. 26th International Conference on Intelligent User Interfaces, 70–74. https://doi.org/10.1145/3397482.3450721</ref><br />
<br />
* The goal of the study by Kessens et al. <ref name='Kessens'>Kessens, J. M., Neerincx, M. A., Looije, R., Kroes, M., & Bloothooft, G. (2009). Facial and vocal emotion expression of a personal computer assistant to engage, educate and motivate children. Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009. https://doi.org/10.1109/ACII.2009.5349582</ref> was to develop a personal computer assistant that helps children to adhere to performing daily activities and living healthy. This personal computer assistant had three roles, namely a companion, educator and motivator role. The companion robot gives emotional support and allows the children to also play with it. If the assistant takes on the educational role, it can teach and explain. When the robot takes on the motivational role it can encourage the children to adhere to a healthy lifestyle and it can learn them that adherence is important. The participants of the experiment with the assistant were children of 8 and 9 years old. In total there were 18 participants, of which 8 participants were female. The study showed that the more human-like the interaction with the computer assistant was (for example using different emotional expressions), the more persuasive, engaging and fun the interaction was between the computer and the child. The computer assistant showed to have the opportunity to increase motivation and self-performance management amongst the children compare to when they did not use the assistant. Also, the assistant was able to reduce the BMI of the users. Both children and adults enjoyed the computer assistant.<br />
<br />
* The following paper by Chou, Chan, & Lin <ref name='Chou'>Chou, C. Y., Chan, T. W., & Lin, C. J. (2003). Redefining the learning companion: The past, present, and future of educational agents. Computers and Education, 40(3), 255–269. https://doi.org/10.1016/S0360-1315(02)00130-6</ref> discusses the history of learning agents and its potential (both positive and negative) for the future. Educational agents could help to improve a social learning environment. However, the complexity of the educational agents makes development expensive and difficult. The paper also addresses the classification of educational agents, which is the larger group of software that helps social learning through a human approach. A learning companion falls within this branch and is defined as a “computer-simulated character, which has human characteristics and plays a non-authoritative role in a social learning environment".<br />
<br />
* In the paper of Cambo, Avrahami and Lee <ref name='Cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021</ref> it has been researched why work breaks are important and how to motivate users to increase physical activity. They want to reach this by wrapping this break in a playful interaction. Therefore, they came up with an application called BreakSense. They have decided that it is not necessary for the device to interrupt the user, since it is better for the user to self-interrupt and initiate a break themselves. This helped the participants in a way that using the device, induced physical activity in their daily routine.<br />
<br />
* In a meta analysis of 83 papers, all the kinds of chatbots that exist are described, and also the history and evaluation of chatbots and how humans perceive and experience their interaction with them. They also mention areas of research that future studies can focus on. <ref name = "Rapp2020">Rapp, A., Curti, L., & Boldi, A. (2021). The human side of human-chatbot interaction: A systematic literature review of ten years of research on text-based chatbots. International Journal of Human Computer Studies, 151, 102630. https://doi.org/10.1016/j.ijhcs.2021.102630 </ref><br />
<br />
===Appearance===<br />
<br />
* It is possible to program a chatbot in a way that it will interact with you via a moving and talking avatar. The paper also suggests that displaying facial expressions are beneficial in the interaction with such an agent, since displaying the avatar’s emotion can enhance perceived emotional intelligence. <ref name='Angga'>Angga, P. A., Fachri, W. E., Elevanita, A., Suryadi, & Agushinta, R. D. (2016). Design of chatbot with 3D avatar, voice interface, and facial expression. Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015, 326–330. https://doi.org/10.1109/ICSITech.2015.7407826</ref><br />
<br />
* Looije et al researched the guidelines that are needed when developing a personal assistant. These guidelines were derived from interviewing, persuasive technologies and from existing guidelines for personal assistants. In their research they found that guidelines were best expressed in iCat (a personal assistant) that was able to show socially intelligent behavior compared to a non-social or text interface based iCat.<ref name='Looije'>Looije, R., Cnossen, F., & Neerincx, M. A. (2006). Incorporating guidelines for health assistance into a socially intelligent robot. Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, 515–520. https://doi.org/10.1109/ROMAN.2006.314441</ref><br />
<br />
* A study by Go and Sundar <ref name='GoSundar'>Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j.chb.2019.01.020</ref> states that revealing the identity of a chatbot as a non-human can have a positive effect: user will have less high expectations about the conversation, and will be impressed when an agent shows human-likebehaviour. Furthermore, they emphasize the importance of the conversational style between a human an a computer. When the dialogue resembles that of an actual human, perceived feelings of social presence and homophily will increase, leading to more positive attitudes towards the agent (and in turn potential desired behaviour consequences).<br />
<br />
* Higher feelings of social presence can be achieved when an agent’s language usage shows a consistent personality, which can be either introvert or extravert.<ref name="Sinatra2021">Sinatra, A. M., Pollard, K. A., Files, B. T., Oiknine, A. H., Ericson, M., & Khooshabeh, P. (2021). Social fidelity in virtual agents: Impacts on presence and learning. Computers in Human Behavior, 114, 106562. https://doi.org/10.1016/j.chb.2020.106562</ref><br />
<br />
* Virtual agents which are communicating in a personalized way (using “I” and “you”) will behave more human-like and it will therefore gain more social fidelity. It will also lead to increased feelings of social presence and better learning performance and motivation.<ref name = "Sinatra2021"/><ref name = "Picciano">Picciano, A. G. (2002). BEYOND STUDENT PERCEPTIONS: ISSUES OF INTERACTION, PRESENCE, AND PERFORMANCE IN AN ONLINE COURSE. In JALN (Vol. 6, Issue 1).</ref><br />
<br />
* The previous statement is supported by Araujo<ref name = "Araujo2018">Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189. https://doi.org/10.1016/j.chb.2018.03.051</ref>. In his paper, he showed that social presence increased when the machine shows a more intelligent interaction style.<br />
<br />
* Social fidelity is related to certain human-like behaviour and cues. This includes for example speech content (personalized language, feedback, politeness, social memory, personality) and visual cues (facial expressions, gestures, gaze, emotions). <ref name = "Sinatra2021"/><br />
<br />
===Functionality===<br />
<br />
* In the study of Niemi and Kousa <ref name = "Niemi2020">Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167</ref>, students (16-18 years old) and teachers of an upper secondary school were asked about their perception considering the distant teaching due to COVID-19. This was done with the use of four questionnaires (56 to 72 students and 9 to 15 teachers. Overall, the distant teaching was considered to be done well. Students did however feel a heavier workload and more fatiqued. Some also indicated a loss of motivation. Teachers did not recognize these problems.<br />
<br />
* Apparently, the mere presence of a lifelike character in an online learning environment can have a strong positive influence on the perceive learning experience of students (around the age of 12). Adding such an interactive agent to the learning process can make it more fun, and the agent is perceived to be helpful and credible. <ref name = "Lester1997">Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366.</ref><br />
<br />
* One has to be aware that there are both a lower and and upper bound to the proactiveness of a virtual agent. Agents that are too present can quickly be perceived as irritating and intrusive. <ref name = "Lester1997">Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366.</ref><br />
<br />
* Sharma et al. <ref name = "Sharma2020">Sharma, K., Giannakos, M., & Dillenbourg, P. (2020). Eye-tracking and artificial intelligence to enhance motivation and learning. Smart Learning Environments, 7(1). https://doi.org/10.1186/s40561-020-00122-x</ref> explains that in combination with eye-tracking, AI can also be used to give individual feedback. Next to this, it can help to predict learning outcomes of the individual students.<br />
<br />
* The following study was aimed at measuring the effectiveness of learning chatbot systems on student performance. In total 72 students of a university in Tandojam participated in the study. These students were divided into 2 groups. One group was able to use the Google search engine, and the other group was able to use the Chatbot system to find solutions to their problems. The study found that learning through the Chatbot had a significant impact on memory retention and learning outcomes <ref name = "Abbasi2014">Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3).</ref><br />
<br />
* A paper by Grover et al. <ref name = "Grover">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> described an experiment in which two chatbots were compared, one of which has a face and appeared to be more emotionally intelligent. Results suggested that the emotionally expressive agent can lead participants (average age 33) to be more productive and focused during working hours. The participants also reported to feel more satisfied with their achievements. Furthermore, the participants suggested valuable improvements, like intelligent task scheduling and a distraction monitoring system.<br />
<br />
* The study of Baethge and Rigotti <ref name = "Baethge2013">Baethge, A., & Rigotti, T. (2013). Interruptions to workflow: Their relationship with irritation and satisfaction with performance, and the mediating roles of time pressure and mental demands. Work & Stress, 27(1), 43–63. https://doi.org/10.1080/02678373.2013.761783</ref> tries to find how interruptions during work influence the perception of performance and irritation. This study shows that interruptions are negatively correlated with the satisfaction of performance and positively with forgetting what you were doing and feeling irritated. The study was done with 133 nurses.<br />
<br />
* The study of Borst et al. <ref name = "Borst2015">Borst, J. P., Taatgen, N. A., & Van Rijn, H. (2015). What makes interruptions disruptive? A process-model account of the effects of the problem state bottleneck on task interruption and resumption. Conference on Human Factors in Computing Systems - Proceedings, 2015-April, 2971–2980. https://doi.org/10.1145/2702123.2702156</ref> tries to make a first step towards an integrated theory for task interruptions, for in earlier research several factors have been proven to influence the effect of the interruption. These factors are the duration of the interruption, the complexity of the interrupting task and the moment of interruption. Their study confirmed that problem state requirements influence the disruptiveness. So, interfaces should interrupt at low problem state moments and also maintain the problem state for the user when interrupted by a secondary task.<br />
<br />
* Henning et al.<ref name = "Henning1997">Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref> studied the influence of short breaks on computer operators. At larger work sites no improvements were found. At smaller work sites well-being and productivity improved when exercises were included in the small breaks. Extra 3-minute breaks from computer work were preferred over 30-second breaks in each hour.<br />
<br />
* Artificial agents have been used to provide social support. However, as mentioned by Ta et al.<ref name = "Ta2020">Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235</ref>, most research in this area has not focused on everyday life. Instead, experiments are performed, for instance, under high levels of stress. This might give different results than in an educational or work setting. Therefore investigated the potential of artificial agents in everyday life. Their research seems to indicate that they can also be useful in these situations. These findings can thus be applied to the AI companion robot, which should provide social support and combat loneliness.<br />
<br />
* Odekerkern-Schröder et al. <ref name = "Odekerken2020">Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148</ref> study the role of the companion robot Vector during the COVID-19 pandemic. They found that companion robots can fulfill to reduce feelings of loneliness, by building supportive relationships.<br />
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==References==<br />
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<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Logbook_group04&diff=115196Logbook group042021-05-09T17:39:42Z<p>20182838: </p>
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<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Logbook Group 4 </font><br />
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== Week 1 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4.5 hours), Meeting preparation (0.25 hours), Meeting (1.5 hours), Deliverables (1 hour), Checking text for Wiki (1.25 hours), Mendeley (0.5 hour), State of the Art (1,25 hours)<br />
|-<br />
| Ezra Gerris || 10 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5 hours), Writing the Approach + literature study (2 hours)<br />
|-<br />
| Kari Luijt || 11.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5h), User group (1h) , Subject + User group + Milestones + Checking document (1h 15min), Literatire studies + Problem statement +upload things to wiki (1h 30 min)<br />
|-<br />
| Julie van der Hijde || 12.25 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature Study (4 hours), Making referencing correct (0.5 h), Meeting (1.5h), Objectives&Problem statement + checking entire doc(2 h), Update Wiki (0.75h), Upload everything on wiki (1h)<br />
|-<br />
| Silke Franken || 16 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (1 h), learn more about Wikitext / update Wiki (3h 15), Meeting (1.5h), Literature study (7 h), Planning (1 h)<br />
|-<br />
|}<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 14.25 hours || Meeting (1h), Investigating AI possibilities (4.5h), Survey (1h), (Tutor) meeting (1.75h), Emails to tutor (0.25h), Reading Grover (1.5h), Literature study (1.5h), Writing State-of-the-art (1.75h), Checking text for Wiki (1h)<br />
<br />
|-<br />
| Ezra Gerris || 11 hours || Meeting (1h), Survey questions (0.5h), USE (1.25h), (Tutor) meeting (1.75h), Read Grover (2.5h), (Re)write motivation (2.5h), Checking and updating wiki (1.5h)<br />
<br />
|-<br />
| Kari Luijt || 13.25 hours || Meeting (1h), Preparation survey questions (1h), Making survey questions (6h), (Tutor) meeting (1.75h), Making the survey (1.25h), Making the outro (.25h), Read state of the art + motivation for COCO (.5h), Read Grover (1.5h) <br />
<br />
|-<br />
| Julie van der Hijde || 13.75 hours || Meeting (1h), USE (1.5h), Wiki (0.5h), Survey questions (1h), (Tutor) meeting (1.75h), Read Grover + notes for future (2.5h), search articles motivation (1.5h), motivation COCO (3h), Checking survey, wiki, motivation, state-of- the-art (1h)<br />
<br />
|-<br />
| Silke Franken || 16.5 hours || Meeting (1h), Making survey questions (6.5h), Literature study (1.5h), Read complete plan (0.5h) Design of AI companion (1h), (Tutor) meeting (1.75h), Update planning (15min), writing intro and checking survey (2h), State-of-the-Art (1h), Update Wiki (1h)<br />
<br />
|-<br />
|}<br />
<br />
== Week 3 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 4 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 5 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 6 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 7 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 8 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 9 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=PRE2020_4_Group4&diff=114909PRE2020 4 Group42021-05-03T07:57:30Z<p>20182838: </p>
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<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> Coco, The Computer Companion </font><br />
<br />
==Group Description==<br />
<br />
===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
<br />
<br />
===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
<br />
<br />
<br />
== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
<br />
<br />
<br />
==Problem Statement and Objectives==<br />
<br />
===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
<br />
Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
<br />
=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
<br />
<br />
<br />
==USE: User, Society and Enterprise==<br />
<br />
<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
<br />
=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
<br />
Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
<br />
Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
<br />
=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
<br />
=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
<br />
==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
<br />
Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref> have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
<br />
Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
<br />
Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
<br />
===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
<br />
''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
<br />
<br />
=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
<br />
''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
<br />
<br />
===Survey===<br />
<br />
To find out more about the group of people that will be most interested in having a virtual companion, a survey will be conducted. This survey acts as a means the specify the target group and to get to know their preferences regarding the functionality of the virtual agent. <br />
<br />
An early draft of the survey can be found [https://docs.google.com/forms/d/e/1FAIpQLScLvw_KTaaE6cRBcPJzjQ5dLDJIcvuLODW9L-skfng-X9q0Gg/viewform?usp=pp_url here].<br />
<br />
<br />
===Planning===<br />
<br />
[[File:0LAUK0 Planning Group 4 (Q4).PNG|1100 px|alt=Planning Group 4]]<br />
<br />
==Research==<br />
<br />
===State of the Art===<br />
<br />
<br />
'''''Productivity agents'''''<br />
<br />
As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
<br />
Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
<br />
With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
<br />
<br />
'''''Companion agents'''''<br />
<br />
When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. <ref name="Ta"/> investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements.<br />
<br />
<br />
'''''Physical health agents'''''<br />
<br />
Cambo, Avrahami, & Lee <ref name = "Cambo2017">Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021</ref> investigated the application “BreakSense” and concluded that the technology should let the user decide for themselves when to take a break. They discovered that in this way, the physical activity became part of their daily routine.<br />
<br />
<br />
'''''Computer assistants'''''<br />
<br />
Although these agents focus on specific tasks, there also exist personal computer assistants that are developed to help, for instance children, more generally with their daily activities. The study by Kessens et al. <ref name = "Kessens2009">Kessens, J. M., Neerincx, M. A., Looije, R., Kroes, M., & Bloothooft, G. (2009). Facial and vocal emotion expression of a personal computer assistant to engage, educate and motivate children. Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009. https://doi.org/10.1109/ACII.2009.5349582</ref> investigated such a computer assistant, namely the Philips iCat. This animated virtual robot can show varying emotional expressions and fulfilled the roles of both companion, educator and motivator.<br />
<br />
===Related Literature===<br />
<br />
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]].<br />
<br />
===Motivation for virtual agent===<br />
<br />
This section contains the motivation for the decision to develop a virtual agent instead of a physical robot. Several important topics will be discussed to reach this, namely emotional intelligence, embodiment and costs. '''Disclaimer: this is still a concept and will thus be extended later'''<br />
<br />
'''''Emotional intelligence'''''<br />
* Without emotional intelligence, artificial agents can display behaviours that can be perceived as unexpected and confusing by humans. Moreover, their behaviour can be misinterpreted and can also violate human expectations, which could then possibly cause emotional harm (Fan, Scheutz, Lohani, Mccoy, & Stokes) <ref name = "Fan2017">Fan, L., Scheutz, M., Lohani, M., Mccoy, M., & Stokes, C. (2017). Do We Need Emotionally Intelligent Artificial Agents? First Results of Human Perceptions of Emotional Intelligence in Humans Compared to Robots. Springer International Publishing AG 2017, 129–141. 10.1007/978-3-319-67401-8_15</ref>.<br />
* Artificial agents with high emotional intelligence are being perceived as more trustworthy compared to robots that do not display intelligent behaviour (Fan et al.) <ref name="Fan2017"/>. <br />
<br />
'''''Physical versus simulated embodiment'''''<br />
* The physical embodiment that enhanced social telepresence decreases the smoothness of speech '''(source: Tanaka, Nakanishi, & Ishiguro)'''.<br />
* According to Milne et al., the most important advantages of virtual agents are that they can be accessed at any time and that they do not need to be repaired '''(source: Milne, Luerssen, Lewis, Leibbrandt, & Powers)'''.<br />
* The article of Lee, Jung, Kim & Kim researches one of the most fundamental questions about social robots, namely whether or not physical embodiment adds value for good social interaction compared to disembodied social robots. For instance, manufacturing of embodied robots is very expensive, and many technical difficulties can arise because of the many embedded sensors and motors. Moreover, “physical embodied agents may facilitate better social interaction with its users by providing more affordance for proper social interaction”, where affordance means the fundamental properties of a device that determine its ways of use. <br />
* Lee et al. also found that if an embodied robot had anthropomorphic-physical embodiment, expectations of the robot were very high. When it then did not have the ability to react to touch-input, the high expectations of the people dropped and they became frustrated and disappointed in the robot. This might be a general negative effect of physical embodiment (especially for lonely people that might use the robot as a real companion).<br />
'''source'''<br />
* Wang & Rau have researched the effect on users’ responses of different types of social robots. They distinghuised on two factors; embodiment and substrates. It has been found that people prefer that the embodiment matches the substrate. This means that they would prefer if a physical robot has the best effect in a physical substrate, but a virtual robot the best in a virtual substrate. '''source'''<br />
<br />
'''''Costs'''''<br />
* Paper on design of a low-cost social robot ‘Philos’: commercial value = $3.000 dollars, associated software = free. Paro costs $6.000 dollars and Nao over $15.000 '''(source: Puehn, Liu, Feng, Hornfeck, & Lee, 2014)'''.<br />
* "Physical robot platforms are typically very expensive to build, alter and maintain." However, software can easily be updated '''(Avramova, Yang, Li, Peters, & Skantze, 2017)'''<br />
* https://www.webfx.com/internet-marketing/ai-pricing.html is an interesting website to see how much it will cost to develop AI software. Comparing e.g. chatbots, large data analysis systems and virtual assistants.<br />
<br />
==References==<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Logbook_group04&diff=114766Logbook group042021-05-02T17:56:43Z<p>20182838: /* Week 2 */</p>
<hr />
<div>== Week 1 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4.5 hours), Meeting preparation (0.25 hours), Meeting (1.5 hours), Deliverables (1 hour), Checking text for Wiki (1.25 hours), Mendeley (0.5 hour), State of the Art (1,25 hours)<br />
|-<br />
| Ezra Gerris || 10 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5 hours), Writing the Approach + literature study (2 hours)<br />
|-<br />
| Kari Luijt || 11.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5h), User group (1h) , Subject + User group + Milestones + Checking document (1h 15min), Literatire studies + Problem statement +upload things to wiki (1h 30 min)<br />
|-<br />
| Julie van der Hijde || 12.25 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature Study (4 hours), Making referencing correct (0.5 h), Meeting (1.5h), Objectives&Problem statement + checking entire doc(2 h), Update Wiki (0.75h), Upload everything on wiki (1h)<br />
|-<br />
| Silke Franken || 16 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (1 h), learn more about Wikitext / update Wiki (3h 15), Meeting (1.5h), Literature study (7 h), Planning (1 h)<br />
|-<br />
|}<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 14.25 hours || Meeting (1h), Investigating AI possibilities (4.5h), Survey (1h), (Tutor) meeting (1.75h), Emails to tutor (0.25h), Reading Grover (1.5h), Literature study (1.5h), Writing State-of-the-art (1.75h), Checking text for Wiki (1h)<br />
<br />
|-<br />
| Ezra Gerris || 7 hours || Meeting (1h), Survey questions (0.5h), USE (1.25h), (Tutor) meeting (1.75h), Read Grover (2.5h)<br />
<br />
|-<br />
| Kari Luijt || 13.25 hours || Meeting (1h), Preparation survey questions (1h), Making survey questions (6h), (Tutor) meeting (1.75h), Making the survey (1.25h), Making the outro (.25h), Read state of the art + motivation for COCO (.5h), Read Grover (1.5h) <br />
<br />
|-<br />
| Julie van der Hijde || 11.75 hours || Meeting (1h), USE (1.5h), Wiki (0.5h), Survey questions (1h), (Tutor) meeting (1.75h), Read Grover + notes for future (2.5h), search articles motivation (1.5h), motivation COCO (2h)<br />
<br />
|-<br />
| Silke Franken || 16.5 hours || Meeting (1h), Making survey questions (6.5h), Literature study (1.5h), Read complete plan (0.5h) Design of AI companion (1h), (Tutor) meeting (1.75h), Update planning (15min), writing intro and checking survey (2h), State-of-the-Art (1h), Update Wiki (1h)<br />
<br />
|-<br />
|}<br />
<br />
== Week 3 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 4 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 5 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 6 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 7 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 8 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 9 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=PRE2020_4_Group4&diff=114765PRE2020 4 Group42021-05-02T17:55:48Z<p>20182838: /* Planning */</p>
<hr />
<div><div style="font-family: 'Calibri'; font-size: 15px; line-height: 1.5; max-width: 1100px; word-wrap: break-word; color: #333; font-weight: 400; margin-left: auto; margin-right: auto; padding: 50px; background-color: white; padding-top: 25px;"><br />
<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> COCO, The Computer Companion </font><br />
<br />
==Group Description==<br />
<br />
===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
<br />
<br />
===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
<br />
<br />
<br />
== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
<br />
<br />
<br />
==Problem Statement and Objectives==<br />
<br />
===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
<br />
Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
<br />
=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
<br />
<br />
<br />
==USE: User, Society and Enterprise==<br />
<br />
<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
<br />
=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
<br />
Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
<br />
Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
<br />
=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
<br />
=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
<br />
==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
<br />
Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref> have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
<br />
Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
<br />
Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
<br />
===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
<br />
''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
<br />
<br />
=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
<br />
''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
<br />
<br />
===Survey===<br />
<br />
To find out more about the group of people that will be most interested in having a virtual companion, a survey will be conducted. This survey acts as a means the specify the target group and to get to know their preferences regarding the functionality of the virtual agent. <br />
<br />
An early draft of the survey can be found [https://docs.google.com/forms/d/e/1FAIpQLScLvw_KTaaE6cRBcPJzjQ5dLDJIcvuLODW9L-skfng-X9q0Gg/viewform?usp=pp_url here].<br />
<br />
<br />
===Planning===<br />
<br />
[[File:0LAUK0 Planning Group 4 (Q4).PNG|1100 px|alt=Planning Group 4]]<br />
<br />
==Research==<br />
<br />
===State of the Art===<br />
<br />
<br />
'''''Productivity agents'''''<br />
<br />
As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
<br />
Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
<br />
With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
<br />
<br />
'''''Companion agents'''''<br />
<br />
When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. <ref name="Ta"/> investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements.<br />
<br />
<br />
'''''Physical health agents'''''<br />
<br />
Cambo, Avrahami, & Lee <ref name = "Cambo2017">Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021</ref> investigated the application “BreakSense” and concluded that the technology should let the user decide for themselves when to take a break. They discovered that in this way, the physical activity became part of their daily routine.<br />
<br />
<br />
'''''Computer assistants'''''<br />
<br />
Although these agents focus on specific tasks, there also exist personal computer assistants that are developed to help, for instance children, more generally with their daily activities. The study by Kessens et al. <ref name = "Kessens2009">Kessens, J. M., Neerincx, M. A., Looije, R., Kroes, M., & Bloothooft, G. (2009). Facial and vocal emotion expression of a personal computer assistant to engage, educate and motivate children. Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009. https://doi.org/10.1109/ACII.2009.5349582</ref> investigated such a computer assistant, namely the Philips iCat. This animated virtual robot can show varying emotional expressions and fulfilled the roles of both companion, educator and motivator.<br />
<br />
===Related Literature===<br />
<br />
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]].<br />
<br />
==References==<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=PRE2020_4_Group4&diff=114764PRE2020 4 Group42021-05-02T17:54:37Z<p>20182838: /* Approach */</p>
<hr />
<div><div style="font-family: 'Calibri'; font-size: 15px; line-height: 1.5; max-width: 1100px; word-wrap: break-word; color: #333; font-weight: 400; margin-left: auto; margin-right: auto; padding: 50px; background-color: white; padding-top: 25px;"><br />
<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> COCO, The Computer Companion </font><br />
<br />
==Group Description==<br />
<br />
===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
<br />
<br />
===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
<br />
<br />
<br />
== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
<br />
<br />
<br />
==Problem Statement and Objectives==<br />
<br />
===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
<br />
Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
<br />
=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
<br />
<br />
<br />
==USE: User, Society and Enterprise==<br />
<br />
<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
<br />
=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
<br />
Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
<br />
Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
<br />
=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
<br />
=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
<br />
==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
<br />
Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref> have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
<br />
Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
<br />
Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
<br />
===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
<br />
''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
<br />
<br />
=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
<br />
''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
<br />
<br />
===Survey===<br />
<br />
To find out more about the group of people that will be most interested in having a virtual companion, a survey will be conducted. This survey acts as a means the specify the target group and to get to know their preferences regarding the functionality of the virtual agent. <br />
<br />
An early draft of the survey can be found [https://docs.google.com/forms/d/e/1FAIpQLScLvw_KTaaE6cRBcPJzjQ5dLDJIcvuLODW9L-skfng-X9q0Gg/viewform?usp=pp_url here].<br />
<br />
<br />
===Planning===<br />
<br />
[[File:0LAUK0 Planning Group 4 (Q4).PNG|1300 px|alt=Planning Group 4]]<br />
<br />
==Research==<br />
<br />
===State of the Art===<br />
<br />
<br />
'''''Productivity agents'''''<br />
<br />
As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
<br />
Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
<br />
With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
<br />
<br />
'''''Companion agents'''''<br />
<br />
When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. <ref name="Ta"/> investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements.<br />
<br />
<br />
'''''Physical health agents'''''<br />
<br />
Cambo, Avrahami, & Lee <ref name = "Cambo2017">Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021</ref> investigated the application “BreakSense” and concluded that the technology should let the user decide for themselves when to take a break. They discovered that in this way, the physical activity became part of their daily routine.<br />
<br />
<br />
'''''Computer assistants'''''<br />
<br />
Although these agents focus on specific tasks, there also exist personal computer assistants that are developed to help, for instance children, more generally with their daily activities. The study by Kessens et al. <ref name = "Kessens2009">Kessens, J. M., Neerincx, M. A., Looije, R., Kroes, M., & Bloothooft, G. (2009). Facial and vocal emotion expression of a personal computer assistant to engage, educate and motivate children. Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009. https://doi.org/10.1109/ACII.2009.5349582</ref> investigated such a computer assistant, namely the Philips iCat. This animated virtual robot can show varying emotional expressions and fulfilled the roles of both companion, educator and motivator.<br />
<br />
===Related Literature===<br />
<br />
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]].<br />
<br />
==References==<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=PRE2020_4_Group4&diff=114763PRE2020 4 Group42021-05-02T17:36:47Z<p>20182838: </p>
<hr />
<div><div style="font-family: 'Calibri'; font-size: 15px; line-height: 1.5; max-width: 1100px; word-wrap: break-word; color: #333; font-weight: 400; margin-left: auto; margin-right: auto; padding: 50px; background-color: white; padding-top: 25px;"><br />
<br />
<font size='6' style="margin-bottom: 20px; padding-bottom: 10px;display: block;"> COCO, The Computer Companion </font><br />
<br />
==Group Description==<br />
<br />
===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
<br />
<br />
===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
<br />
<br />
<br />
== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
<br />
<br />
<br />
==Problem Statement and Objectives==<br />
<br />
===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
<br />
Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
<br />
=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
<br />
<br />
<br />
==USE: User, Society and Enterprise==<br />
<br />
<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
<br />
=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
<br />
Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
<br />
Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
<br />
=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
<br />
=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
<br />
==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
<br />
Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref> have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
<br />
Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
<br />
Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
<br />
===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
<br />
''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
<br />
<br />
=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
<br />
''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
<br />
<br />
===Planning===<br />
<br />
[[File:0LAUK0 Planning Group 4 (Q4).PNG|1300 px|alt=Planning Group 4]]<br />
<br />
==Research==<br />
<br />
===State of the Art===<br />
<br />
<br />
'''''Productivity agents'''''<br />
<br />
As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
<br />
Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
<br />
With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
<br />
<br />
'''''Companion agents'''''<br />
<br />
When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. <ref name="Ta"/> investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements.<br />
<br />
<br />
'''''Physical health agents'''''<br />
<br />
Cambo, Avrahami, & Lee <ref name = "Cambo2017">Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021</ref> investigated the application “BreakSense” and concluded that the technology should let the user decide for themselves when to take a break. They discovered that in this way, the physical activity became part of their daily routine.<br />
<br />
<br />
'''''Computer assistants'''''<br />
<br />
Although these agents focus on specific tasks, there also exist personal computer assistants that are developed to help, for instance children, more generally with their daily activities. The study by Kessens et al. <ref name = "Kessens2009">Kessens, J. M., Neerincx, M. A., Looije, R., Kroes, M., & Bloothooft, G. (2009). Facial and vocal emotion expression of a personal computer assistant to engage, educate and motivate children. Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009. https://doi.org/10.1109/ACII.2009.5349582</ref> investigated such a computer assistant, namely the Philips iCat. This animated virtual robot can show varying emotional expressions and fulfilled the roles of both companion, educator and motivator.<br />
<br />
===Related Literature===<br />
<br />
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]].<br />
<br />
==References==<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Logbook_group04&diff=114762Logbook group042021-05-02T17:25:39Z<p>20182838: /* Week 2 */</p>
<hr />
<div>== Week 1 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4.5 hours), Meeting preparation (0.25 hours), Meeting (1.5 hours), Deliverables (1 hour), Checking text for Wiki (1.25 hours), Mendeley (0.5 hour), State of the Art (1,25 hours)<br />
|-<br />
| Ezra Gerris || 10 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5 hours), Writing the Approach + literature study (2 hours)<br />
|-<br />
| Kari Luijt || 11.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5h), User group (1h) , Subject + User group + Milestones + Checking document (1h 15min), Literatire studies + Problem statement +upload things to wiki (1h 30 min)<br />
|-<br />
| Julie van der Hijde || 12.25 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature Study (4 hours), Making referencing correct (0.5 h), Meeting (1.5h), Objectives&Problem statement + checking entire doc(2 h), Update Wiki (0.75h), Upload everything on wiki (1h)<br />
|-<br />
| Silke Franken || 16 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (1 h), learn more about Wikitext / update Wiki (3h 15), Meeting (1.5h), Literature study (7 h), Planning (1 h)<br />
|-<br />
|}<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 14.25 hours || Meeting (1h), Investigating AI possibilities (4.5h), Survey (1h), (Tutor) meeting (1.75h), Emails to tutor (0.25h), Reading Grover (1.5h), Literature study (1.5h), Writing State-of-the-art (1.75h), Checking text for Wiki (1h)<br />
<br />
|-<br />
| Ezra Gerris || 7 hours || Meeting (1h), Survey questions (0.5h), USE (1.25h), (Tutor) meeting (1.75h), Read Grover (2.5h)<br />
<br />
|-<br />
| Kari Luijt || 13.25 hours || Meeting (1h), Preparation survey questions (1h), Making survey questions (6h), (Tutor) meeting (1.75h), Making the survey (1.25h), Making the outro (.25h), Read state of the art + motivation for COCO (.5h), Read Grover (1.5h) <br />
<br />
|-<br />
| Julie van der Hijde || 11.75 hours || Meeting (1h), USE (1.5h), Wiki (0.5h), Survey questions (1h), (Tutor) meeting (1.75h), Read Grover + notes for future (2.5h), search articles motivation (1.5h), motivation COCO (2h)<br />
<br />
|-<br />
| Silke Franken || 16 hours || Meeting (1h), Making survey questions (6.5h), Literature study (1.5h), Read complete plan (0.5h) Design of AI companion (1h), (Tutor) meeting (1.75h), Update planning (15min), writing intro and checking survey (2h), State-of-the-Art (1h), Update Wiki (30min)<br />
<br />
|-<br />
|}<br />
<br />
== Week 3 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 4 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 5 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 6 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 7 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 8 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 9 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=PRE2020_4_Group4&diff=114761PRE2020 4 Group42021-05-02T17:24:39Z<p>20182838: /* State of the Art */</p>
<hr />
<div><br />
<br />
<br />
==Group Description==<br />
<br />
===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
<br />
===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
<br />
<br />
<br />
== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
<br />
<br />
<br />
==Problem Statement and Objectives==<br />
<br />
===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
<br />
Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
<br />
=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
<br />
<br />
<br />
==USE: User, Society and Enterprise==<br />
<br />
<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
<br />
=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
<br />
Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
<br />
Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
<br />
=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
<br />
=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
<br />
==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
<br />
Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref> have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
<br />
Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
<br />
Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
<br />
===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
<br />
''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
<br />
<br />
=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
<br />
''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
<br />
<br />
===Planning===<br />
<br />
[[File:0LAUK0 Planning Group 4 (Q4).PNG|1300 px|alt=Planning Group 4]]<br />
<br />
==Research==<br />
<br />
===State of the Art===<br />
<br />
<br />
'''''Productivity agents'''''<br />
<br />
As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
<br />
Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
<br />
With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
<br />
<br />
'''''Companion agents'''''<br />
<br />
When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. <ref name="Ta"/> investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements.<br />
<br />
<br />
'''''Physical health agents'''''<br />
<br />
Cambo, Avrahami, & Lee <ref name = "Cambo2017">Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021</ref> investigated the application “BreakSense” and concluded that the technology should let the user decide for themselves when to take a break. They discovered that in this way, the physical activity became part of their daily routine.<br />
<br />
<br />
'''''Computer assistants'''''<br />
<br />
Although these agents focus on specific tasks, there also exist personal computer assistants that are developed to help, for instance children, more generally with their daily activities. The study by Kessens et al. <ref name = "Kessens2009">Kessens, J. M., Neerincx, M. A., Looije, R., Kroes, M., & Bloothooft, G. (2009). Facial and vocal emotion expression of a personal computer assistant to engage, educate and motivate children. Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009. https://doi.org/10.1109/ACII.2009.5349582</ref> investigated such a computer assistant, namely the Philips iCat. This animated virtual robot can show varying emotional expressions and fulfilled the roles of both companion, educator and motivator.<br />
<br />
===Related Literature===<br />
<br />
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]].<br />
<br />
==References==<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=PRE2020_4_Group4&diff=114760PRE2020 4 Group42021-05-02T17:24:30Z<p>20182838: /* State of the Art */</p>
<hr />
<div><br />
<br />
<br />
==Group Description==<br />
<br />
===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
<br />
===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
<br />
<br />
<br />
== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
<br />
<br />
<br />
==Problem Statement and Objectives==<br />
<br />
===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
<br />
Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
<br />
=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
<br />
<br />
<br />
==USE: User, Society and Enterprise==<br />
<br />
<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
<br />
=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
<br />
Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
<br />
Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
<br />
=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
<br />
=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
<br />
==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
<br />
Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref> have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
<br />
Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
<br />
Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
<br />
===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
<br />
''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
<br />
<br />
=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
<br />
''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
<br />
<br />
===Planning===<br />
<br />
[[File:0LAUK0 Planning Group 4 (Q4).PNG|1300 px|alt=Planning Group 4]]<br />
<br />
==Research==<br />
<br />
===State of the Art===<br />
<br />
<br />
''''Productivity agents''''<br />
<br />
As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
<br />
Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
<br />
With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
<br />
<br />
'''''Companion agents'''''<br />
<br />
When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. <ref name="Ta"/> investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements.<br />
<br />
<br />
'''''Physical health agents'''''<br />
<br />
Cambo, Avrahami, & Lee <ref name = "Cambo2017">Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021</ref> investigated the application “BreakSense” and concluded that the technology should let the user decide for themselves when to take a break. They discovered that in this way, the physical activity became part of their daily routine.<br />
<br />
<br />
'''''Computer assistants'''''<br />
<br />
Although these agents focus on specific tasks, there also exist personal computer assistants that are developed to help, for instance children, more generally with their daily activities. The study by Kessens et al. <ref name = "Kessens2009">Kessens, J. M., Neerincx, M. A., Looije, R., Kroes, M., & Bloothooft, G. (2009). Facial and vocal emotion expression of a personal computer assistant to engage, educate and motivate children. Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009. https://doi.org/10.1109/ACII.2009.5349582</ref> investigated such a computer assistant, namely the Philips iCat. This animated virtual robot can show varying emotional expressions and fulfilled the roles of both companion, educator and motivator.<br />
<br />
===Related Literature===<br />
<br />
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]].<br />
<br />
==References==<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=PRE2020_4_Group4&diff=114759PRE2020 4 Group42021-05-02T17:23:25Z<p>20182838: /* State of the Art */</p>
<hr />
<div><br />
<br />
<br />
==Group Description==<br />
<br />
===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
<br />
===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
<br />
<br />
<br />
== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
<br />
<br />
<br />
==Problem Statement and Objectives==<br />
<br />
===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
<br />
Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
<br />
=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
<br />
<br />
<br />
==USE: User, Society and Enterprise==<br />
<br />
<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
<br />
=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
<br />
Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
<br />
Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
<br />
=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
<br />
=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
<br />
==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
<br />
Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref> have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
<br />
Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
<br />
Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
<br />
===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
<br />
''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
<br />
<br />
=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
<br />
''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
<br />
<br />
===Planning===<br />
<br />
[[File:0LAUK0 Planning Group 4 (Q4).PNG|1300 px|alt=Planning Group 4]]<br />
<br />
==Research==<br />
<br />
===State of the Art===<br />
<br />
<br />
'''Productivity agents'''<br />
<br />
As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
<br />
Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
<br />
With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
<br />
<br />
'''Companion agents'''<br />
<br />
When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. <ref name="Ta"/> investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements.<br />
<br />
<br />
'''Physical health agents'''<br />
<br />
Cambo, Avrahami, & Lee <ref name = "Cambo2017">Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021</ref> investigated the application “BreakSense” and concluded that the technology should let the user decide for themselves when to take a break. They discovered that in this way, the physical activity became part of their daily routine.<br />
<br />
<br />
'''Computer assistants'''<br />
<br />
Although these agents focus on specific tasks, there also exist personal computer assistants that are developed to help, for instance children, more generally with their daily activities. The study by Kessens et al. <ref name = "Kessens2009">Kessens, J. M., Neerincx, M. A., Looije, R., Kroes, M., & Bloothooft, G. (2009). Facial and vocal emotion expression of a personal computer assistant to engage, educate and motivate children. Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009. https://doi.org/10.1109/ACII.2009.5349582</ref> investigated such a computer assistant, namely the Philips iCat. This animated virtual robot can show varying emotional expressions and fulfilled the roles of both companion, educator and motivator.<br />
<br />
===Related Literature===<br />
<br />
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]].<br />
<br />
==References==<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=PRE2020_4_Group4&diff=114758PRE2020 4 Group42021-05-02T17:20:59Z<p>20182838: /* State of the Art */</p>
<hr />
<div><br />
<br />
<br />
==Group Description==<br />
<br />
===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
<br />
===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
<br />
<br />
<br />
== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
<br />
<br />
<br />
==Problem Statement and Objectives==<br />
<br />
===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
<br />
Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
<br />
=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
<br />
<br />
<br />
==USE: User, Society and Enterprise==<br />
<br />
<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
<br />
=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
<br />
Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
<br />
Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
<br />
=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
<br />
=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
<br />
==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
<br />
Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref> have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
<br />
Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
<br />
Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
<br />
===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
<br />
''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
<br />
<br />
=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
<br />
''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
<br />
<br />
===Planning===<br />
<br />
[[File:0LAUK0 Planning Group 4 (Q4).PNG|1300 px|alt=Planning Group 4]]<br />
<br />
==Research==<br />
<br />
===State of the Art===<br />
<br />
====Productivity agents====<br />
<br />
As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
<br />
Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
<br />
With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
<br />
====Companion agents====<br />
<br />
When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. <ref name="Ta"/> investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements.<br />
<br />
====Physical health agents====<br />
<br />
Cambo, Avrahami, & Lee <ref name = "Cambo2017">Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021</ref> investigated the application “BreakSense” and concluded that the technology should let the user decide for themselves when to take a break. They discovered that in this way, the physical activity became part of their daily routine.<br />
<br />
====Computer assistants====<br />
<br />
Although these agents focus on specific tasks, there also exist personal computer assistants that are developed to help, for instance children, more generally with their daily activities. The study by Kessens et al. <ref name = "Kessens2009">Kessens, J. M., Neerincx, M. A., Looije, R., Kroes, M., & Bloothooft, G. (2009). Facial and vocal emotion expression of a personal computer assistant to engage, educate and motivate children. Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009. https://doi.org/10.1109/ACII.2009.5349582</ref> investigated such a computer assistant, namely the Philips iCat. This animated virtual robot can show varying emotional expressions and fulfilled the roles of both companion, educator and motivator.<br />
<br />
===Related Literature===<br />
<br />
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]].<br />
<br />
==References==<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=PRE2020_4_Group4&diff=114757PRE2020 4 Group42021-05-02T17:14:17Z<p>20182838: /* Approach */</p>
<hr />
<div><br />
<br />
<br />
==Group Description==<br />
<br />
===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
<br />
===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
<br />
<br />
<br />
== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
<br />
<br />
<br />
==Problem Statement and Objectives==<br />
<br />
===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
<br />
Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
<br />
=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
<br />
<br />
<br />
==USE: User, Society and Enterprise==<br />
<br />
<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
<br />
=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
<br />
Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
<br />
Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
<br />
=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
<br />
=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
<br />
==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
<br />
Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref> have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
<br />
Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
<br />
Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
<br />
===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
<br />
''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
<br />
<br />
=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
<br />
''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
<br />
<br />
===Planning===<br />
<br />
[[File:0LAUK0 Planning Group 4 (Q4).PNG|1300 px|alt=Planning Group 4]]<br />
<br />
==Research==<br />
<br />
===State of the Art===<br />
<br />
====Productivity Agents====<br />
<br />
As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
<br />
Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
<br />
With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
<br />
====Companion Agents====<br />
<br />
When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements (Ta et al., 2020a).<br />
<br />
===Related Literature===<br />
<br />
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]].<br />
<br />
==References==<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=PRE2020_4_Group4&diff=114756PRE2020 4 Group42021-05-02T17:13:25Z<p>20182838: /* State of the Art */</p>
<hr />
<div><br />
<br />
<br />
==Group Description==<br />
<br />
===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
<br />
===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
<br />
<br />
<br />
== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
<br />
<br />
<br />
==Problem Statement and Objectives==<br />
<br />
===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
<br />
Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
<br />
=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
<br />
<br />
<br />
==USE: User, Society and Enterprise==<br />
<br />
<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
<br />
=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
<br />
Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
<br />
Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
<br />
=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
<br />
=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
<br />
==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
<br />
Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al (2020) have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref>. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
<br />
Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
<br />
Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
<br />
===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
<br />
''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
<br />
<br />
=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
<br />
''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
<br />
<br />
===Planning===<br />
<br />
[[File:0LAUK0 Planning Group 4 (Q4).PNG|1300 px|alt=Planning Group 4]]<br />
<br />
==Research==<br />
<br />
===State of the Art===<br />
<br />
====Productivity Agents====<br />
<br />
As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
<br />
Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
<br />
With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
<br />
====Companion Agents====<br />
<br />
When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements (Ta et al., 2020a).<br />
<br />
===Related Literature===<br />
<br />
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]].<br />
<br />
==References==<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=PRE2020_4_Group4&diff=114755PRE2020 4 Group42021-05-02T17:13:15Z<p>20182838: /* Research */</p>
<hr />
<div><br />
<br />
<br />
==Group Description==<br />
<br />
===Members===<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Name !! Student ID !! Department !! Email address<br />
|-<br />
| Eline Ensinck || 1333941 || Industrial Engineering & Innovation Sciences || e.n.f.ensinck@student.tue.nl<br />
|-<br />
| Julie van der Hijde || 1251244 || Industrial Engineering & Innovation Sciences || j.v.d.hijde@student.tue.nl<br />
|-<br />
| Ezra Gerris || 1378910 || Industrial Engineering & Innovation Sciences || e.gerris@student.tue.nl<br />
|-<br />
| Silke Franken || 1330284 || Industrial Engineering & Innovation Sciences || s.w.franken@student.tue.nl<br />
|-<br />
| Kari Luijt || 1327119 || Industrial Engineering & Innovation Sciences || k.luijt@student.tue.nl<br />
|-<br />
|}<br />
<br />
===Logbook===<br />
<br />
See the following page: [[logbook_group04|Logbook Group 4]]<br />
<br />
<br />
<br />
== Subject == <br />
We want to analyze and design an AI robot componanion to improve online learning and working from home problems like diminished motivation, loneliness and physical health problems. In order to address these problems we will introduce you to Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC.<br />
<br />
<br />
<br />
==Problem Statement and Objectives==<br />
<br />
===Problem Statement ===<br />
Due to the COVID-19 pandemic that emerged at the beginning of 2020 everyone's lives have been turned upside down. Working from home as much as possible was (and still is) the norm in many places all around the world and it applies to office workers, but also to college-, university-, and high school students. Even though there might be benefits from working in a home office, there are also many disadvantages that are critical to everyone's health, motivation and concentration. Multiple studies have found such effects, both mental and physical, because of the work from home situation <ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref><br />
<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
<br />
Mental issues that might arise are emotional exhaustion, but also feelings of loneliness, isolation and depression. Moreover, because people have a high exposure to computer screens, they can experience fatigue, tiredness, headaches and eye-related symptoms<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Additionally, people exercise less while working from home during the pandemic. This can have effects on metabolic, cardiovascular, and mental health, and all this might result in higher chances of mortality<ref name='Werneck'> Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>.<br />
<br />
Other issues are related to the concentration and motivation of the people that are working from home. Office workers that work at home while also taking care of their families have lots of problems with staying on one task, because they want to run errands for their families<ref name='Xiao'> Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. In addition to this, it requires greater concentration for home office workers during communication <ref name = "Siqueira"> Siqueira, L. T. D., Santos, A. P. dos, Silva, R. L. F., Moreira, P. A. M., Vitor, J. da S., & Ribeiro, V. V. (2020). Vocal Self-Perception of Home Office Workers During the COVID-19 Pandemic. Journal of Voice. https://doi.org/10.1016/j.jvoice.2020.10.016 </ref>. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>.<br />
<br />
=== Objectives ===<br />
Our objectives are the following:<br />
# Help with concentration and motivation (study-buddy)<br />
# Improve physical health<br />
# Provide social support for the user<br />
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<br />
<br />
==USE: User, Society and Enterprise==<br />
<br />
<br />
=== Target user group ===<br />
The user groups for this project will be office workers, college-, university-, and high school students, since these groups experience the most negative effects of the restrictions to work from home. There are several requirements for each group, most of them are related to COVID-19. First of all, there are some requirements relating to mental health. It is important for people to have social interactions from time to time. Individuals living alone could get mental health issues such as depression and loneliness due to the lack of these social interactions, caused by the restrictions<br />
<ref name='Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. <br />
It is also important for people to be able to concentrate well when they are working and that they can maintain their motivation and focus. Studies show that due to COVID-19 students experience a heavier workload, fatigue and a loss of motivation <ref name='Niemi'>Niemi, H. M., & Kousa, P. (2020). A Case Study of Students’ and Teachers’ Perceptions in a Finnish High School during the COVID Pandemic. International Journal of Technology in Education and Science, 4(4), 352–369. https://doi.org/10.46328/ijtes.v4i4.167 </ref>. <br />
Considering the physical health, it is important that students and office workers are physically active and healthy. Some problems for the physical health of students and employees can arise from working from home. People that have an office job often do not get a lot of physical exercise during their workhours, but quarantine measures have reduced this even more <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. This can affect cardiovascular and metabolic health, but even mental health <ref name = 'Werneck'>Werneck, A. O., Silva, D. R., Malta, D. C., Souza-Júnior, P. R. B., Azevedo, L. O., Barros, M. B. A., & Szwarcwald, C. L. (2021). Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults. Translational Behavioral Medicine, 11(2), 323–331. https://doi.org/10.1093/tbm/ibaa095 </ref>. In addition to this, the increased exposure to computer screens since the outbreak of COVID-19, especially applicable to high school students, can result in tiredness, headache and eye-related symptoms <ref name = 'Xiao'>Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users. Journal of Occupational and Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/JOM.0000000000002097 </ref>. Hence, students and employees should become more physically active to improve their physical (and mental) health.<br />
<br />
=== Secondary users === <br />
When people use Coco, they should gain better concentration and motivation and better physical health than without the computer assistant. Moreover, people that might feel lonely can find social support in Coco. Parents of the students will also profit from these aforementioned benefits of Coco, because they need to worry less about their children and their education, as Coco will assist them while studying. <br />
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Besides parents, teachers will profit from Coco too. Since Coco will help the students with studying, the teachers can focus on their actual educational tasks. <br />
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Moreover, co-workers and managers will profit from their colleagues using Coco. Coco can help the workers maintain physical and mental health which in turn leads to a better work mentality and environment<br />
<br />
=== Society ===<br />
When people use Coco the computer companion they will have better, concentration, motivation and better mental and physical health. This means that a lot of people in the society will have a higher well-being which in turn results in a healtier society. Moreover, because people work and study better both companies and the schools will have better results.<br />
<br />
=== Enterprise ===<br />
There are two main stakeholders for enterprises. Coco needs to be developed and this is where a software company comes in. Such a company will develop the virtual agent and will sell licenses to other companies. These companies are the other stakeholders and are interested in buying Coco for their employees or students. This could be small enterprises that want to buy a license for a small group of employees, but also large universities that want to provide the virtual agent for all their students. The effects for the software development company will be economic, since they will earn money with selling the Coco software licenses. For the interested companies, buying the license will mean that their employees’ physical and mental health will increase i.e., the primary users’ benefits.<br />
<br />
==Approach==<br />
In order to address the consequences and improve health and motivation in home-office workers, we will introduce to you Coco, the computer companion. Coco will be an artificially intelligent and interactive agent that users can easily install on their laptop or PC. <br />
<br />
Concerning the mental health of users, a main problem is loneliness. It has been researched before what the impact of robotic technologies is on social support. Ta et al (2020) have found that artificial agents do not only provide social support in laboratory experiments but also in daily life situations <ref name = 'Ta'>Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020). User experiences of social support from companion chatbots in everyday contexts: Thematic analysis. Journal of Medical Internet Research, 22(3). https://doi.org/10.2196/16235 </ref>. <br />
Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships<ref name='Odekerken'>Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: an integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148 </ref>. <br />
<br />
Regarding the physical well-being of users, the use of technology could be useful to improve physical activity. As stated by Cambo et al. (2017), using a mobile application or wearable that tracks self-interruption and initiates a playful break, could induce physical activity in the daily routine of users <ref name='cambo'>Cambo, S. A., Avrahami, D., & Lee, M. L. (2017). BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 3595–3607. https://doi.org/10.1145/3025453.3026021 </ref>. <br />
Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks <ref name='Henning'> Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396 </ref>. <br />
<br />
Finally considering the productivity of users, a paper by Abbasi and Kazi (2014) shows that a learning chatbot systems can enhance the performance of students<ref name = 'abbasi'>Abbasi, S., & Kazi, H. (2014). Measuring effectiveness of learning chatbot systems on Student’s learning outcome and memory retention. In Asian Journal of Applied Science and Engineering (Vol. 3). </ref>. <br />
In an experiment where one group used Google and another group used a chatbot to solve problems, the chatbot had impact on memory retention and learning outcomes of the students. The same research as mentioned before from Henning et al also showed that not only the users’ well-being, but also the users’ productivity would increase in the presence of a chatbot<ref name='henning'>Henning, R. A., Jacques, P., Kissel, G. V., Sullivan, A. B., & Alteras-Webb, S. M. (1997). Frequent short rest breaks from computer work: Effects on productivity and well-being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396</ref>. <br />
Moreover, as has been researched in an experiment of Lester et al. (1997), the presence of a lifelike character in an online learning environment can have a strong influence on the perceived learning experience of students around the age of 12. Adding such an interactive agent to the learning process can make it more fun, next to the fact that the agent is perceived to be helpful and credible<ref name='Lester'>Lester, J. C., Barlow, S. T., Converse, S. A., Stone, B. A., Kahler, S. E., & Bhogal, R. S. (1997). Persona effect: Affective impact of animated pedagogical agents. Conference on Human Factors in Computing Systems - Proceedings, 359–366. </ref>. <br />
<br />
===Method===<br />
At the end of the project, we will present our complete concept of the AI companion. This will include its '''design''' and '''functionality''', which are based on both '''literature research''' and '''statistical analysis''' of send-out questionnaires. The questionnaires will be completed by the user group to make sure the actual users of the technology have their input in the development and analysis of the companion. Moreover, the '''user needs''' and perceptions will be described. The larger societal and entrepreneurial effects will also be taken into account. In this way, all USE-aspects will be addressed. Finally, a '''risk assessment''' will be included, as limitations related to the costs and privacy of the product are also important for the realization of the technology. These deliverables will be presented both in a '''Wiki-page and final presentation'''. A schematic overview of the deliverables can be found in Table 1. <br />
<br />
''Table 1: Schematic overview of the deliverables''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Deliverable<br />
|-<br />
| rowspan="2" | Functionality || Literature study <br />
|- <br />
| Results questionnaire 1: user needs <br />
|-<br />
| rowspan="2" | Design || Results questionnaire 2: design <br />
|-<br />
| Example companion <br />
|-<br />
| Additional || Risk assessment <br />
|-<br />
|}<br />
<br />
<br />
=== Milestones ===<br />
During the project, several milestones are planned to be reached. These milestones correspond to the deliverables mentioned in the section above and can be found in table 2.<br />
<br />
''Table 2: Overview of the milestones''<br />
{| border=1 style="border-collapse: collapse;" cellpadding = 5<br />
! Topic !! Milestone<br />
|-<br />
| Organization || Complete planning<br />
|-<br />
| rowspan="3" | Functionality || Complete literature study<br />
|-<br />
| Responses questionnaire 1: user needs <br />
|-<br />
| Complete analysis questionnaire 1: user needs <br />
|-<br />
| rowspan="3" | Design || Responses questionnaire 2: design <br />
|-<br />
| Complete analysis questionnaire 2: design <br />
|-<br />
| Design of the companion <br />
|-<br />
|}<br />
<br />
<br />
===Planning===<br />
<br />
[[File:0LAUK0 Planning Group 4 (Q4).PNG|1300 px|alt=Planning Group 4]]<br />
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==Research==<br />
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===State of the Art===<br />
<br />
====Productivity Agents====<br />
<br />
As discussed by Grover et al. <ref name="Grover2020">Grover, T., Rowan, K., Suh, J., McDuff, D., & Czerwinski, M. (2020). Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. International Conference on Intelligent User Interfaces, Proceedings IUI, 20, 390–400. https://doi.org/10.1145/3377325.3377507</ref> multiple applications exist that focus on task and time management. They all try to assist their users but do so in different ways. “MeTime”, for example, tries to make its users aware of their distractions by showing which apps they use (and for how long). “Calendar.help”, on the other hand, is connected to its user's email and can schedule meetings accordingly. Other examples include “RADAR” that tackles the problem of “email overload” and “TaskBot” that focuses on teamwork. <br />
<br />
Grover et al. mention how Kimani et al. <ref name = "Kimani2019">Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A Conversational Agent in Support of Productivity and Wellbeing at Work. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, 332–338. https://doi.org/10.1109/ACII.2019.8925488</ref> designed a so-called productivity agent, in an attempt to incorporate all the beforementioned applications with different functions into one artificially intelligent system. The conversational agent that they described focused on improving productivity and well-being in the workspace. By means of a survey and a field study, they investigated the optimal functionality of a productivity agent. Findings suggests four tasks that are most important for such agents to possess. These tasks include distraction monitoring, task scheduling, task management and goal reflection. <ref name = "Grover2020"/><br />
<br />
With their research, Grover and colleagues <ref name = "Grover2020"/> wanted to get more insight on the influence of anthropomorphic appearance in agents versus a simple text-based bot which lower perceived emotional intelligence. Even though productivity was increased with the presence of a chatbot, outcomes suggest that there was no significant performance difference between the virtual agent and the text-based agent. Interaction with the virtual agent was however perceived to be more pleasant, supporting the idea that higher emotional intelligence in agents can reduce negative emotions like frustration <ref name = "Klein1999">Klein, J., Moon, Y., & Pieard, R. W. (1999). This computer responds to user frustration. Conference on Human Factors in Computing Systems - Proceedings, 242–243. https://doi.org/10.1145/632716.632866</ref>. The researchers also found that it is important that the appearance of the agent matches their capabilities, meaning that agents should only have anthropomorphistic looks if it can also act human-like. Other suggestions for improvement were focused on the agent’s inflexible task management skills and inappropriately timed distraction monitoring messages. Those last points especially will act as a guidance in designing an improved Agent System Architecture during this research. Grover et al. suggest including an additional dialog model into the agent architecture, which could be initiated by the user when they want to reschedule or change the duration of a task. They also suggest extending the distraction detection functionality and let users personalize their list of distracting websites and applications.<br />
<br />
<br />
====Companion Agents====<br />
<br />
When going to the Play Store or App Store on your mobile phone, you can download “Replika: My AI Friend". This is a companion chatbot, that imitates human-like conversations. The more you use the app, the more it also learns about you. Ta et al. investigated the effects of this advanced chatbot. They found out that it is successful in reducing loneliness as it resembles some form of companionship. Some other benefits were found as well. These include its ability to positively affect its users by sending positive and caring messages, to give advice, and to enable a conversation without fear of judgements (Ta et al., 2020a). <br />
<br />
<br />
===Related Literature===<br />
<br />
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]].<br />
<br />
==References==<br />
<br />
<references/></div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Logbook_group04&diff=114753Logbook group042021-05-02T17:00:25Z<p>20182838: /* Week 9 */</p>
<hr />
<div>== Week 1 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4.5 hours), Meeting preparation (0.25 hours), Meeting (1.5 hours), Deliverables (1 hour), Checking text for Wiki (1.25 hours), Mendeley (0.5 hour), State of the Art (1,25 hours)<br />
|-<br />
| Ezra Gerris || 10 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5 hours), Writing the Approach + literature study (2 hours)<br />
|-<br />
| Kari Luijt || 11.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5h), User group (1h) , Subject + User group + Milestones + Checking document (1h 15min), Literatire studies + Problem statement +upload things to wiki (1h 30 min)<br />
|-<br />
| Julie van der Hijde || 12.25 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature Study (4 hours), Making referencing correct (0.5 h), Meeting (1.5h), Objectives&Problem statement + checking entire doc(2 h), Update Wiki (0.75h), Upload everything on wiki (1h)<br />
|-<br />
| Silke Franken || 16 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (1 h), learn more about Wikitext / update Wiki (3h 15), Meeting (1.5h), Literature study (7 h), Planning (1 h)<br />
|-<br />
|}<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 14.25 hours || Meeting (1h), Investigating AI possibilities (4.5h), Survey (1h), (Tutor) meeting (1.75h), Emails to tutor (0.25h), Reading Grover (1.5h), Literature study (1.5h), Writing State-of-the-art (1.75h), Checking text for Wiki (1h)<br />
<br />
|-<br />
| Ezra Gerris || 7 hours || Meeting (1h), Survey questions (0.5h), USE (1.25h), (Tutor) meeting (1.75h), Read Grover (2.5h)<br />
<br />
|-<br />
| Kari Luijt || 13.25 hours || Meeting (1h), Preparation survey questions (1h), Making survey questions (6h), (Tutor) meeting (1.75h), Making the survey (1.25h), Making the outro (.25h), Read state of the art + motivation for COCO (.5h), Read Grover (1.5h) <br />
<br />
|-<br />
| Julie van der Hijde || 11.75 hours || Meeting (1h), USE (1.5h), Wiki (0.5h), Survey questions (1h), (Tutor) meeting (1.75h), Read Grover + notes for future (2.5h), search articles motivation (1.5h), motivation COCO (2h)<br />
<br />
|-<br />
| Silke Franken || 15.5 hours || Meeting (1h), Making survey questions (6.5h), Literature study (1.5h), Read complete plan (0.5h) Design of AI companion (1h), (Tutor) meeting (1.75h), Update planning (15min), writing intro and checking survey (2h), State-of-the-Art (1h)<br />
<br />
|-<br />
|}<br />
<br />
== Week 3 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 4 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 5 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 6 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 7 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 8 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 9 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Logbook_group04&diff=114752Logbook group042021-05-02T17:00:20Z<p>20182838: /* Week 8 */</p>
<hr />
<div>== Week 1 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4.5 hours), Meeting preparation (0.25 hours), Meeting (1.5 hours), Deliverables (1 hour), Checking text for Wiki (1.25 hours), Mendeley (0.5 hour), State of the Art (1,25 hours)<br />
|-<br />
| Ezra Gerris || 10 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5 hours), Writing the Approach + literature study (2 hours)<br />
|-<br />
| Kari Luijt || 11.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5h), User group (1h) , Subject + User group + Milestones + Checking document (1h 15min), Literatire studies + Problem statement +upload things to wiki (1h 30 min)<br />
|-<br />
| Julie van der Hijde || 12.25 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature Study (4 hours), Making referencing correct (0.5 h), Meeting (1.5h), Objectives&Problem statement + checking entire doc(2 h), Update Wiki (0.75h), Upload everything on wiki (1h)<br />
|-<br />
| Silke Franken || 16 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (1 h), learn more about Wikitext / update Wiki (3h 15), Meeting (1.5h), Literature study (7 h), Planning (1 h)<br />
|-<br />
|}<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 14.25 hours || Meeting (1h), Investigating AI possibilities (4.5h), Survey (1h), (Tutor) meeting (1.75h), Emails to tutor (0.25h), Reading Grover (1.5h), Literature study (1.5h), Writing State-of-the-art (1.75h), Checking text for Wiki (1h)<br />
<br />
|-<br />
| Ezra Gerris || 7 hours || Meeting (1h), Survey questions (0.5h), USE (1.25h), (Tutor) meeting (1.75h), Read Grover (2.5h)<br />
<br />
|-<br />
| Kari Luijt || 13.25 hours || Meeting (1h), Preparation survey questions (1h), Making survey questions (6h), (Tutor) meeting (1.75h), Making the survey (1.25h), Making the outro (.25h), Read state of the art + motivation for COCO (.5h), Read Grover (1.5h) <br />
<br />
|-<br />
| Julie van der Hijde || 11.75 hours || Meeting (1h), USE (1.5h), Wiki (0.5h), Survey questions (1h), (Tutor) meeting (1.75h), Read Grover + notes for future (2.5h), search articles motivation (1.5h), motivation COCO (2h)<br />
<br />
|-<br />
| Silke Franken || 15.5 hours || Meeting (1h), Making survey questions (6.5h), Literature study (1.5h), Read complete plan (0.5h) Design of AI companion (1h), (Tutor) meeting (1.75h), Update planning (15min), writing intro and checking survey (2h), State-of-the-Art (1h)<br />
<br />
|-<br />
|}<br />
<br />
== Week 3 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 4 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 5 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 6 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 7 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 8 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 9 ==<br />
<br />
[[File:logbook week 9 group 4.jpg]]</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Logbook_group04&diff=114751Logbook group042021-05-02T17:00:15Z<p>20182838: /* Week 7 */</p>
<hr />
<div>== Week 1 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4.5 hours), Meeting preparation (0.25 hours), Meeting (1.5 hours), Deliverables (1 hour), Checking text for Wiki (1.25 hours), Mendeley (0.5 hour), State of the Art (1,25 hours)<br />
|-<br />
| Ezra Gerris || 10 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5 hours), Writing the Approach + literature study (2 hours)<br />
|-<br />
| Kari Luijt || 11.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5h), User group (1h) , Subject + User group + Milestones + Checking document (1h 15min), Literatire studies + Problem statement +upload things to wiki (1h 30 min)<br />
|-<br />
| Julie van der Hijde || 12.25 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature Study (4 hours), Making referencing correct (0.5 h), Meeting (1.5h), Objectives&Problem statement + checking entire doc(2 h), Update Wiki (0.75h), Upload everything on wiki (1h)<br />
|-<br />
| Silke Franken || 16 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (1 h), learn more about Wikitext / update Wiki (3h 15), Meeting (1.5h), Literature study (7 h), Planning (1 h)<br />
|-<br />
|}<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 14.25 hours || Meeting (1h), Investigating AI possibilities (4.5h), Survey (1h), (Tutor) meeting (1.75h), Emails to tutor (0.25h), Reading Grover (1.5h), Literature study (1.5h), Writing State-of-the-art (1.75h), Checking text for Wiki (1h)<br />
<br />
|-<br />
| Ezra Gerris || 7 hours || Meeting (1h), Survey questions (0.5h), USE (1.25h), (Tutor) meeting (1.75h), Read Grover (2.5h)<br />
<br />
|-<br />
| Kari Luijt || 13.25 hours || Meeting (1h), Preparation survey questions (1h), Making survey questions (6h), (Tutor) meeting (1.75h), Making the survey (1.25h), Making the outro (.25h), Read state of the art + motivation for COCO (.5h), Read Grover (1.5h) <br />
<br />
|-<br />
| Julie van der Hijde || 11.75 hours || Meeting (1h), USE (1.5h), Wiki (0.5h), Survey questions (1h), (Tutor) meeting (1.75h), Read Grover + notes for future (2.5h), search articles motivation (1.5h), motivation COCO (2h)<br />
<br />
|-<br />
| Silke Franken || 15.5 hours || Meeting (1h), Making survey questions (6.5h), Literature study (1.5h), Read complete plan (0.5h) Design of AI companion (1h), (Tutor) meeting (1.75h), Update planning (15min), writing intro and checking survey (2h), State-of-the-Art (1h)<br />
<br />
|-<br />
|}<br />
<br />
== Week 3 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 4 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 5 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 6 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 7 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 8 ==<br />
<br />
[[File:logbook week 8 group 4.jpg]]<br />
<br />
== Week 9 ==<br />
<br />
[[File:logbook week 9 group 4.jpg]]</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Logbook_group04&diff=114750Logbook group042021-05-02T17:00:09Z<p>20182838: /* Week 5 */</p>
<hr />
<div>== Week 1 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4.5 hours), Meeting preparation (0.25 hours), Meeting (1.5 hours), Deliverables (1 hour), Checking text for Wiki (1.25 hours), Mendeley (0.5 hour), State of the Art (1,25 hours)<br />
|-<br />
| Ezra Gerris || 10 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5 hours), Writing the Approach + literature study (2 hours)<br />
|-<br />
| Kari Luijt || 11.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5h), User group (1h) , Subject + User group + Milestones + Checking document (1h 15min), Literatire studies + Problem statement +upload things to wiki (1h 30 min)<br />
|-<br />
| Julie van der Hijde || 12.25 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature Study (4 hours), Making referencing correct (0.5 h), Meeting (1.5h), Objectives&Problem statement + checking entire doc(2 h), Update Wiki (0.75h), Upload everything on wiki (1h)<br />
|-<br />
| Silke Franken || 16 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (1 h), learn more about Wikitext / update Wiki (3h 15), Meeting (1.5h), Literature study (7 h), Planning (1 h)<br />
|-<br />
|}<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 14.25 hours || Meeting (1h), Investigating AI possibilities (4.5h), Survey (1h), (Tutor) meeting (1.75h), Emails to tutor (0.25h), Reading Grover (1.5h), Literature study (1.5h), Writing State-of-the-art (1.75h), Checking text for Wiki (1h)<br />
<br />
|-<br />
| Ezra Gerris || 7 hours || Meeting (1h), Survey questions (0.5h), USE (1.25h), (Tutor) meeting (1.75h), Read Grover (2.5h)<br />
<br />
|-<br />
| Kari Luijt || 13.25 hours || Meeting (1h), Preparation survey questions (1h), Making survey questions (6h), (Tutor) meeting (1.75h), Making the survey (1.25h), Making the outro (.25h), Read state of the art + motivation for COCO (.5h), Read Grover (1.5h) <br />
<br />
|-<br />
| Julie van der Hijde || 11.75 hours || Meeting (1h), USE (1.5h), Wiki (0.5h), Survey questions (1h), (Tutor) meeting (1.75h), Read Grover + notes for future (2.5h), search articles motivation (1.5h), motivation COCO (2h)<br />
<br />
|-<br />
| Silke Franken || 15.5 hours || Meeting (1h), Making survey questions (6.5h), Literature study (1.5h), Read complete plan (0.5h) Design of AI companion (1h), (Tutor) meeting (1.75h), Update planning (15min), writing intro and checking survey (2h), State-of-the-Art (1h)<br />
<br />
|-<br />
|}<br />
<br />
== Week 3 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 4 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 5 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 6 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 7 ==<br />
<br />
[[File:logbook week 7 group 4.jpg]]<br />
<br />
== Week 8 ==<br />
<br />
[[File:logbook week 8 group 4.jpg]]<br />
<br />
== Week 9 ==<br />
<br />
[[File:logbook week 9 group 4.jpg]]</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Logbook_group04&diff=114749Logbook group042021-05-02T17:00:04Z<p>20182838: /* Week 6 */</p>
<hr />
<div>== Week 1 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4.5 hours), Meeting preparation (0.25 hours), Meeting (1.5 hours), Deliverables (1 hour), Checking text for Wiki (1.25 hours), Mendeley (0.5 hour), State of the Art (1,25 hours)<br />
|-<br />
| Ezra Gerris || 10 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5 hours), Writing the Approach + literature study (2 hours)<br />
|-<br />
| Kari Luijt || 11.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5h), User group (1h) , Subject + User group + Milestones + Checking document (1h 15min), Literatire studies + Problem statement +upload things to wiki (1h 30 min)<br />
|-<br />
| Julie van der Hijde || 12.25 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature Study (4 hours), Making referencing correct (0.5 h), Meeting (1.5h), Objectives&Problem statement + checking entire doc(2 h), Update Wiki (0.75h), Upload everything on wiki (1h)<br />
|-<br />
| Silke Franken || 16 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (1 h), learn more about Wikitext / update Wiki (3h 15), Meeting (1.5h), Literature study (7 h), Planning (1 h)<br />
|-<br />
|}<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 14.25 hours || Meeting (1h), Investigating AI possibilities (4.5h), Survey (1h), (Tutor) meeting (1.75h), Emails to tutor (0.25h), Reading Grover (1.5h), Literature study (1.5h), Writing State-of-the-art (1.75h), Checking text for Wiki (1h)<br />
<br />
|-<br />
| Ezra Gerris || 7 hours || Meeting (1h), Survey questions (0.5h), USE (1.25h), (Tutor) meeting (1.75h), Read Grover (2.5h)<br />
<br />
|-<br />
| Kari Luijt || 13.25 hours || Meeting (1h), Preparation survey questions (1h), Making survey questions (6h), (Tutor) meeting (1.75h), Making the survey (1.25h), Making the outro (.25h), Read state of the art + motivation for COCO (.5h), Read Grover (1.5h) <br />
<br />
|-<br />
| Julie van der Hijde || 11.75 hours || Meeting (1h), USE (1.5h), Wiki (0.5h), Survey questions (1h), (Tutor) meeting (1.75h), Read Grover + notes for future (2.5h), search articles motivation (1.5h), motivation COCO (2h)<br />
<br />
|-<br />
| Silke Franken || 15.5 hours || Meeting (1h), Making survey questions (6.5h), Literature study (1.5h), Read complete plan (0.5h) Design of AI companion (1h), (Tutor) meeting (1.75h), Update planning (15min), writing intro and checking survey (2h), State-of-the-Art (1h)<br />
<br />
|-<br />
|}<br />
<br />
== Week 3 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 4 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 5 ==<br />
<br />
[[File:logbook week 5 group 4.jpg]]<br />
<br />
== Week 6 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 7 ==<br />
<br />
[[File:logbook week 7 group 4.jpg]]<br />
<br />
== Week 8 ==<br />
<br />
[[File:logbook week 8 group 4.jpg]]<br />
<br />
== Week 9 ==<br />
<br />
[[File:logbook week 9 group 4.jpg]]</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Logbook_group04&diff=114748Logbook group042021-05-02T16:59:58Z<p>20182838: /* Week 4 */</p>
<hr />
<div>== Week 1 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4.5 hours), Meeting preparation (0.25 hours), Meeting (1.5 hours), Deliverables (1 hour), Checking text for Wiki (1.25 hours), Mendeley (0.5 hour), State of the Art (1,25 hours)<br />
|-<br />
| Ezra Gerris || 10 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5 hours), Writing the Approach + literature study (2 hours)<br />
|-<br />
| Kari Luijt || 11.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5h), User group (1h) , Subject + User group + Milestones + Checking document (1h 15min), Literatire studies + Problem statement +upload things to wiki (1h 30 min)<br />
|-<br />
| Julie van der Hijde || 12.25 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature Study (4 hours), Making referencing correct (0.5 h), Meeting (1.5h), Objectives&Problem statement + checking entire doc(2 h), Update Wiki (0.75h), Upload everything on wiki (1h)<br />
|-<br />
| Silke Franken || 16 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (1 h), learn more about Wikitext / update Wiki (3h 15), Meeting (1.5h), Literature study (7 h), Planning (1 h)<br />
|-<br />
|}<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 14.25 hours || Meeting (1h), Investigating AI possibilities (4.5h), Survey (1h), (Tutor) meeting (1.75h), Emails to tutor (0.25h), Reading Grover (1.5h), Literature study (1.5h), Writing State-of-the-art (1.75h), Checking text for Wiki (1h)<br />
<br />
|-<br />
| Ezra Gerris || 7 hours || Meeting (1h), Survey questions (0.5h), USE (1.25h), (Tutor) meeting (1.75h), Read Grover (2.5h)<br />
<br />
|-<br />
| Kari Luijt || 13.25 hours || Meeting (1h), Preparation survey questions (1h), Making survey questions (6h), (Tutor) meeting (1.75h), Making the survey (1.25h), Making the outro (.25h), Read state of the art + motivation for COCO (.5h), Read Grover (1.5h) <br />
<br />
|-<br />
| Julie van der Hijde || 11.75 hours || Meeting (1h), USE (1.5h), Wiki (0.5h), Survey questions (1h), (Tutor) meeting (1.75h), Read Grover + notes for future (2.5h), search articles motivation (1.5h), motivation COCO (2h)<br />
<br />
|-<br />
| Silke Franken || 15.5 hours || Meeting (1h), Making survey questions (6.5h), Literature study (1.5h), Read complete plan (0.5h) Design of AI companion (1h), (Tutor) meeting (1.75h), Update planning (15min), writing intro and checking survey (2h), State-of-the-Art (1h)<br />
<br />
|-<br />
|}<br />
<br />
== Week 3 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 4 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 5 ==<br />
<br />
[[File:logbook week 5 group 4.jpg]]<br />
<br />
== Week 6 ==<br />
<br />
[[File:logbook week 6 group 4.jpg]]<br />
<br />
== Week 7 ==<br />
<br />
[[File:logbook week 7 group 4.jpg]]<br />
<br />
== Week 8 ==<br />
<br />
[[File:logbook week 8 group 4.jpg]]<br />
<br />
== Week 9 ==<br />
<br />
[[File:logbook week 9 group 4.jpg]]</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Logbook_group04&diff=114747Logbook group042021-05-02T16:59:28Z<p>20182838: /* Week 1 */</p>
<hr />
<div>== Week 1 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4.5 hours), Meeting preparation (0.25 hours), Meeting (1.5 hours), Deliverables (1 hour), Checking text for Wiki (1.25 hours), Mendeley (0.5 hour), State of the Art (1,25 hours)<br />
|-<br />
| Ezra Gerris || 10 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5 hours), Writing the Approach + literature study (2 hours)<br />
|-<br />
| Kari Luijt || 11.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5h), User group (1h) , Subject + User group + Milestones + Checking document (1h 15min), Literatire studies + Problem statement +upload things to wiki (1h 30 min)<br />
|-<br />
| Julie van der Hijde || 12.25 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature Study (4 hours), Making referencing correct (0.5 h), Meeting (1.5h), Objectives&Problem statement + checking entire doc(2 h), Update Wiki (0.75h), Upload everything on wiki (1h)<br />
|-<br />
| Silke Franken || 16 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (1 h), learn more about Wikitext / update Wiki (3h 15), Meeting (1.5h), Literature study (7 h), Planning (1 h)<br />
|-<br />
|}<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 14.25 hours || Meeting (1h), Investigating AI possibilities (4.5h), Survey (1h), (Tutor) meeting (1.75h), Emails to tutor (0.25h), Reading Grover (1.5h), Literature study (1.5h), Writing State-of-the-art (1.75h), Checking text for Wiki (1h)<br />
<br />
|-<br />
| Ezra Gerris || 7 hours || Meeting (1h), Survey questions (0.5h), USE (1.25h), (Tutor) meeting (1.75h), Read Grover (2.5h)<br />
<br />
|-<br />
| Kari Luijt || 13.25 hours || Meeting (1h), Preparation survey questions (1h), Making survey questions (6h), (Tutor) meeting (1.75h), Making the survey (1.25h), Making the outro (.25h), Read state of the art + motivation for COCO (.5h), Read Grover (1.5h) <br />
<br />
|-<br />
| Julie van der Hijde || 11.75 hours || Meeting (1h), USE (1.5h), Wiki (0.5h), Survey questions (1h), (Tutor) meeting (1.75h), Read Grover + notes for future (2.5h), search articles motivation (1.5h), motivation COCO (2h)<br />
<br />
|-<br />
| Silke Franken || 15.5 hours || Meeting (1h), Making survey questions (6.5h), Literature study (1.5h), Read complete plan (0.5h) Design of AI companion (1h), (Tutor) meeting (1.75h), Update planning (15min), writing intro and checking survey (2h), State-of-the-Art (1h)<br />
<br />
|-<br />
|}<br />
<br />
== Week 3 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 4 ==<br />
<br />
[[File:logbook week 4 group 4.jpg]]<br />
<br />
== Week 5 ==<br />
<br />
[[File:logbook week 5 group 4.jpg]]<br />
<br />
== Week 6 ==<br />
<br />
[[File:logbook week 6 group 4.jpg]]<br />
<br />
== Week 7 ==<br />
<br />
[[File:logbook week 7 group 4.jpg]]<br />
<br />
== Week 8 ==<br />
<br />
[[File:logbook week 8 group 4.jpg]]<br />
<br />
== Week 9 ==<br />
<br />
[[File:logbook week 9 group 4.jpg]]</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Logbook_group04&diff=114746Logbook group042021-05-02T16:59:18Z<p>20182838: /* Week 2 */</p>
<hr />
<div>== Week 1 ==<br />
<br />
== Week 1 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4.5 hours), Meeting preparation (0.25 hours), Meeting (1.5 hours), Deliverables (1 hour), Checking text for Wiki (1.25 hours), Mendeley (0.5 hour), State of the Art (1,25 hours)<br />
|-<br />
| Ezra Gerris || 10 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5 hours), Writing the Approach + literature study (2 hours)<br />
|-<br />
| Kari Luijt || 11.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5h), User group (1h) , Subject + User group + Milestones + Checking document (1h 15min), Literatire studies + Problem statement +upload things to wiki (1h 30 min)<br />
|-<br />
| Julie van der Hijde || 12.25 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature Study (4 hours), Making referencing correct (0.5 h), Meeting (1.5h), Objectives&Problem statement + checking entire doc(2 h), Update Wiki (0.75h), Upload everything on wiki (1h)<br />
|-<br />
| Silke Franken || 16 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (1 h), learn more about Wikitext / update Wiki (3h 15), Meeting (1.5h), Literature study (7 h), Planning (1 h)<br />
|-<br />
|}<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 14.25 hours || Meeting (1h), Investigating AI possibilities (4.5h), Survey (1h), (Tutor) meeting (1.75h), Emails to tutor (0.25h), Reading Grover (1.5h), Literature study (1.5h), Writing State-of-the-art (1.75h), Checking text for Wiki (1h)<br />
<br />
|-<br />
| Ezra Gerris || 7 hours || Meeting (1h), Survey questions (0.5h), USE (1.25h), (Tutor) meeting (1.75h), Read Grover (2.5h)<br />
<br />
|-<br />
| Kari Luijt || 13.25 hours || Meeting (1h), Preparation survey questions (1h), Making survey questions (6h), (Tutor) meeting (1.75h), Making the survey (1.25h), Making the outro (.25h), Read state of the art + motivation for COCO (.5h), Read Grover (1.5h) <br />
<br />
|-<br />
| Julie van der Hijde || 11.75 hours || Meeting (1h), USE (1.5h), Wiki (0.5h), Survey questions (1h), (Tutor) meeting (1.75h), Read Grover + notes for future (2.5h), search articles motivation (1.5h), motivation COCO (2h)<br />
<br />
|-<br />
| Silke Franken || 15.5 hours || Meeting (1h), Making survey questions (6.5h), Literature study (1.5h), Read complete plan (0.5h) Design of AI companion (1h), (Tutor) meeting (1.75h), Update planning (15min), writing intro and checking survey (2h), State-of-the-Art (1h)<br />
<br />
|-<br />
|}<br />
<br />
== Week 3 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 4 ==<br />
<br />
[[File:logbook week 4 group 4.jpg]]<br />
<br />
== Week 5 ==<br />
<br />
[[File:logbook week 5 group 4.jpg]]<br />
<br />
== Week 6 ==<br />
<br />
[[File:logbook week 6 group 4.jpg]]<br />
<br />
== Week 7 ==<br />
<br />
[[File:logbook week 7 group 4.jpg]]<br />
<br />
== Week 8 ==<br />
<br />
[[File:logbook week 8 group 4.jpg]]<br />
<br />
== Week 9 ==<br />
<br />
[[File:logbook week 9 group 4.jpg]]</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Logbook_group04&diff=114745Logbook group042021-05-02T16:58:49Z<p>20182838: /* Week 2 */</p>
<hr />
<div>== Week 1 ==<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4.5 hours), Meeting preparation (0.25 hours), Meeting (1.5 hours), Deliverables (1 hour), Checking text for Wiki (1.25 hours), Mendeley (0.5 hour), State of the Art (1,25 hours)<br />
|-<br />
| Ezra Gerris || 10 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5 hours), Writing the Approach + literature study (2 hours)<br />
|-<br />
| Kari Luijt || 11.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5h), User group (1h) , Subject + User group + Milestones + Checking document (1h 15min), Literatire studies + Problem statement +upload things to wiki (1h 30 min)<br />
|-<br />
| Julie van der Hijde || 12.25 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature Study (4 hours), Making referencing correct (0.5 h), Meeting (1.5h), Objectives&Problem statement + checking entire doc(2 h), Update Wiki (0.75h), Upload everything on wiki (1h)<br />
|-<br />
| Silke Franken || 16 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (1 h), learn more about Wikitext / update Wiki (3h 15), Meeting (1.5h), Literature study (7 h), Planning (1 h)<br />
|-<br />
|}<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 14.25 hours || Meeting (1h), Investigating AI possibilities (4.5h), Survey (1h), (Tutor) meeting (1.75h), Emails to tutor (0.25h), Reading Grover (1.5h), Literature study (1.5h), Writing State-of-the-art (1.75h), Checking text for Wiki (1h)<br />
<br />
|-<br />
| Ezra Gerris || 7 hours || Meeting (1h), Survey questions (0.5h), USE (1.25h), (Tutor) meeting (1.75h), Read Grover (2.5h)<br />
<br />
|-<br />
| Kari Luijt || 13.25 hours || Meeting (1h), Preparation survey questions (1h), Making survey questions (6h), (Tutor) meeting (1.75h), Making the survey (1.25h), Making the outro (.25h), Read state of the art + motivation for COCO (.5h), Read Grover (1.5h) <br />
<br />
|-<br />
| Julie van der Hijde || 11.75 hours || Meeting (1h), USE (1.5h), Wiki (0.5h), Survey questions (1h), (Tutor) meeting (1.75h), Read Grover + notes for future (2.5h), search articles motivation (1.5h), motivation COCO (2h)<br />
<br />
|-<br />
| Silke Franken || 15.5 hours || Meeting (1h), Making survey questions (6.5h), Literature study (1.5h), Read complete plan (0.5h) Design of AI companion (1h), (Tutor) meeting (1.75h), Update planning (15min), writing intro and checking survey (2h), State-of-the-Art (1h)<br />
<br />
|-<br />
|}<br />
<br />
== Week 3 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 4 ==<br />
<br />
[[File:logbook week 4 group 4.jpg]]<br />
<br />
== Week 5 ==<br />
<br />
[[File:logbook week 5 group 4.jpg]]<br />
<br />
== Week 6 ==<br />
<br />
[[File:logbook week 6 group 4.jpg]]<br />
<br />
== Week 7 ==<br />
<br />
[[File:logbook week 7 group 4.jpg]]<br />
<br />
== Week 8 ==<br />
<br />
[[File:logbook week 8 group 4.jpg]]<br />
<br />
== Week 9 ==<br />
<br />
[[File:logbook week 9 group 4.jpg]]</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Logbook_group04&diff=114744Logbook group042021-05-02T16:58:43Z<p>20182838: /* Week 2 */</p>
<hr />
<div>== Week 1 ==<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! Name !! Time spent !! Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4.5 hours), Meeting preparation (0.25 hours), Meeting (1.5 hours), Deliverables (1 hour), Checking text for Wiki (1.25 hours), Mendeley (0.5 hour), State of the Art (1,25 hours)<br />
|-<br />
| Ezra Gerris || 10 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5 hours), Writing the Approach + literature study (2 hours)<br />
|-<br />
| Kari Luijt || 11.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5h), User group (1h) , Subject + User group + Milestones + Checking document (1h 15min), Literatire studies + Problem statement +upload things to wiki (1h 30 min)<br />
|-<br />
| Julie van der Hijde || 12.25 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature Study (4 hours), Making referencing correct (0.5 h), Meeting (1.5h), Objectives&Problem statement + checking entire doc(2 h), Update Wiki (0.75h), Upload everything on wiki (1h)<br />
|-<br />
| Silke Franken || 16 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (1 h), learn more about Wikitext / update Wiki (3h 15), Meeting (1.5h), Literature study (7 h), Planning (1 h)<br />
|-<br />
|}<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || 14.25 hours || Meeting (1h), Investigating AI possibilities (4.5h), Survey (1h), (Tutor) meeting (1.75h), Emails to tutor (0.25h), Reading Grover (1.5h), Literature study (1.5h), Writing State-of-the-art (1.75h), Checking text for Wiki (1h)<br />
<br />
|-<br />
| Ezra Gerris || 7 hours || Meeting (1h), Survey questions (0.5h), USE (1.25h), (Tutor) meeting (1.75h), Read Grover (2.5h)<br />
<br />
|-<br />
| Kari Luijt || 13.25 hours || Meeting (1h), Preparation survey questions (1h), Making survey questions (6h), (Tutor) meeting (1.75h), Making the survey (1.25h), Making the outro (.25h), Read state of the art + motivation for COCO (.5h), Read Grover (1.5h) <br />
<br />
|-<br />
| Julie van der Hijde || 11.75 hours || Meeting (1h), USE (1.5h), Wiki (0.5h), Survey questions (1h), (Tutor) meeting (1.75h), Read Grover + notes for future (2.5h), search articles motivation (1.5h), motivation COCO (2h)<br />
<br />
|-<br />
| Silke Franken || 15.5 hours || Meeting (1h), Making survey questions (6.5h), Literature study (1.5h), Read complete plan (0.5h) Design of AI companion (1h), (Tutor) meeting (1.75h), Update planning (15min), writing intro and checking survey (2h), State-of-the-Art (1h)<br />
<br />
|-<br />
|}<br />
<br />
== Week 3 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 4 ==<br />
<br />
[[File:logbook week 4 group 4.jpg]]<br />
<br />
== Week 5 ==<br />
<br />
[[File:logbook week 5 group 4.jpg]]<br />
<br />
== Week 6 ==<br />
<br />
[[File:logbook week 6 group 4.jpg]]<br />
<br />
== Week 7 ==<br />
<br />
[[File:logbook week 7 group 4.jpg]]<br />
<br />
== Week 8 ==<br />
<br />
[[File:logbook week 8 group 4.jpg]]<br />
<br />
== Week 9 ==<br />
<br />
[[File:logbook week 9 group 4.jpg]]</div>20182838https://cstwiki.wtb.tue.nl/index.php?title=Logbook_group04&diff=114743Logbook group042021-05-02T16:58:21Z<p>20182838: /* Week 3 */</p>
<hr />
<div>== Week 1 ==<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! Name !! Time spent !! Breakdown<br />
|-<br />
| Eline Ensinck || 12.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4.5 hours), Meeting preparation (0.25 hours), Meeting (1.5 hours), Deliverables (1 hour), Checking text for Wiki (1.25 hours), Mendeley (0.5 hour), State of the Art (1,25 hours)<br />
|-<br />
| Ezra Gerris || 10 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5 hours), Writing the Approach + literature study (2 hours)<br />
|-<br />
| Kari Luijt || 11.75 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (4 hours), Meeting (1.5h), User group (1h) , Subject + User group + Milestones + Checking document (1h 15min), Literatire studies + Problem statement +upload things to wiki (1h 30 min)<br />
|-<br />
| Julie van der Hijde || 12.25 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature Study (4 hours), Making referencing correct (0.5 h), Meeting (1.5h), Objectives&Problem statement + checking entire doc(2 h), Update Wiki (0.75h), Upload everything on wiki (1h)<br />
|-<br />
| Silke Franken || 16 hours || Watching pre-recorded lectures (0.5 hours), Q&A session (0.5 hours), Brainstorm session (1.5 hours), Literature studies (1 h), learn more about Wikitext / update Wiki (3h 15), Meeting (1.5h), Literature study (7 h), Planning (1 h)<br />
|-<br />
|}<br />
<br />
== Week 2 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! Name !! Time spent !! Breakdown<br />
|-<br />
| Eline Ensinck || 14.25 hours || Meeting (1h), Investigating AI possibilities (4.5h), Survey (1h), (Tutor) meeting (1.75h), Emails to tutor (0.25h), Reading Grover (1.5h), Literature study (1.5h), Writing State-of-the-art (1.75h), Checking text for Wiki (1h)<br />
<br />
|-<br />
| Ezra Gerris || 7 hours || Meeting (1h), Survey questions (0.5h), USE (1.25h), (Tutor) meeting (1.75h), Read Grover (2.5h)<br />
<br />
|-<br />
| Kari Luijt || 13.25 hours || Meeting (1h), Preparation survey questions (1h), Making survey questions (6h), (Tutor) meeting (1.75h), Making the survey (1.25h), Making the outro (.25h), Read state of the art + motivation for COCO (.5h), Read Grover (1.5h) <br />
<br />
|-<br />
| Julie van der Hijde || 11.75 hours || Meeting (1h), USE (1.5h), Wiki (0.5h), Survey questions (1h), (Tutor) meeting (1.75h), Read Grover + notes for future (2.5h), search articles motivation (1.5h), motivation COCO (2h)<br />
<br />
|-<br />
| Silke Franken || 15.5 hours || Meeting (1h), Making survey questions (6.5h), Literature study (1.5h), Read complete plan (0.5h) Design of AI companion (1h), (Tutor) meeting (1.75h), Update planning (15min), writing intro and checking survey (2h), State-of-the-Art (1h)<br />
<br />
|-<br />
|}<br />
<br />
== Week 3 ==<br />
<br />
{| border=1 style="border-collapse: collapse; width: 1200px;" cellpadding = 5<br />
! scope="col" style="width: 140px;" | Name<br />
! scope="col" style="width: 100px;" | Time spent<br />
! scope="col" style="width: 960px;" | Breakdown<br />
|-<br />
| Eline Ensinck || || <br />
|-<br />
| Ezra Gerris || || <br />
|-<br />
| Kari Luijt || || <br />
|-<br />
| Julie van der Hijde || || <br />
|-<br />
| Silke Franken || || <br />
<br />
|-<br />
|}<br />
<br />
== Week 4 ==<br />
<br />
[[File:logbook week 4 group 4.jpg]]<br />
<br />
== Week 5 ==<br />
<br />
[[File:logbook week 5 group 4.jpg]]<br />
<br />
== Week 6 ==<br />
<br />
[[File:logbook week 6 group 4.jpg]]<br />
<br />
== Week 7 ==<br />
<br />
[[File:logbook week 7 group 4.jpg]]<br />
<br />
== Week 8 ==<br />
<br />
[[File:logbook week 8 group 4.jpg]]<br />
<br />
== Week 9 ==<br />
<br />
[[File:logbook week 9 group 4.jpg]]</div>20182838