PRE2020 4 Group4: Difference between revisions
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===State of the Art=== | ===State of the Art=== | ||
====Productivity Agents==== | |||
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. | |||
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"/> | |||
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. | |||
====Companion Agents==== | |||
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). | |||
===Related Literature=== | ===Related Literature=== | ||
A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]]. | A list of related scientific papers, including short summaries stating their relevance, can be found [[Related Literature Group 4|here]]. | ||
==References== | ==References== | ||
<references/> | <references/> | ||
Revision as of 18:13, 2 May 2021
Group Description
Members
| Name | Student ID | Department | Email address |
|---|---|---|---|
| Eline Ensinck | 1333941 | Industrial Engineering & Innovation Sciences | e.n.f.ensinck@student.tue.nl |
| Julie van der Hijde | 1251244 | Industrial Engineering & Innovation Sciences | j.v.d.hijde@student.tue.nl |
| Ezra Gerris | 1378910 | Industrial Engineering & Innovation Sciences | e.gerris@student.tue.nl |
| Silke Franken | 1330284 | Industrial Engineering & Innovation Sciences | s.w.franken@student.tue.nl |
| Kari Luijt | 1327119 | Industrial Engineering & Innovation Sciences | k.luijt@student.tue.nl |
Logbook
See the following page: Logbook Group 4
Subject
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.
Problem Statement and Objectives
Problem Statement
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 [1] [2].
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[1]. 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[2].
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[1]. In addition to this, it requires greater concentration for home office workers during communication [3]. Students have also indicated to experience a heavier workload, fatigue and a loss of motivation due to COVID-19 [4].
Objectives
Our objectives are the following:
- Help with concentration and motivation (study-buddy)
- Improve physical health
- Provide social support for the user
USE: User, Society and Enterprise
Target user group
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 [1]. 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 [4]. 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 [1]. This can affect cardiovascular and metabolic health, but even mental health [2]. 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 [1]. Hence, students and employees should become more physically active to improve their physical (and mental) health.
Secondary users
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.
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.
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
Society
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.
Enterprise
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.
Approach
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.
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 [5]. Furthermore, Odekerken-Schröder et al. (2020) have found that companion robots can reduce feelings of loneliness by building supportive relationships[6].
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 [7]. Moreover, Henning et al. (1997) have found that at smaller work sites, users’ well-being improved when exercises were included in the small breaks [8].
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[9]. 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[10]. 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[11].
Method
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.
Table 1: Schematic overview of the deliverables
| Topic | Deliverable |
|---|---|
| Functionality | Literature study |
| Results questionnaire 1: user needs | |
| Design | Results questionnaire 2: design |
| Example companion | |
| Additional | Risk assessment |
Milestones
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.
Table 2: Overview of the milestones
| Topic | Milestone |
|---|---|
| Organization | Complete planning |
| Functionality | Complete literature study |
| Responses questionnaire 1: user needs | |
| Complete analysis questionnaire 1: user needs | |
| Design | Responses questionnaire 2: design |
| Complete analysis questionnaire 2: design | |
| Design of the companion |
Planning
.PNG)
Research
State of the Art
Productivity Agents
As discussed by Grover et al. [12] 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.
Grover et al. mention how Kimani et al. [13] 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. [12]
With their research, Grover and colleagues [12] 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 [14]. 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.
Companion Agents
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).
Related Literature
A list of related scientific papers, including short summaries stating their relevance, can be found here.
References
- ↑ 1.0 1.1 1.2 1.3 1.4 1.5 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 Cite error: Invalid
<ref>tag; name "Xiao" defined multiple times with different content Cite error: Invalid<ref>tag; name "Xiao" defined multiple times with different content Cite error: Invalid<ref>tag; name "Xiao" defined multiple times with different content - ↑ 2.0 2.1 2.2 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 Cite error: Invalid
<ref>tag; name "Werneck" defined multiple times with different content - ↑ 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
- ↑ 4.0 4.1 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
- ↑ 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
- ↑ 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
- ↑ 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
- ↑ 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
- ↑ 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).
- ↑ 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
- ↑ 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.
- ↑ 12.0 12.1 12.2 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
- ↑ 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
- ↑ 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