Bot in the Bunch: Facilitating Group Chat Discussion by Improving Efficiency and Participation with a Chatbot
Abstract
Although group chat discussions are prevalent in daily life, they have a number of limitations. When discussing in a group chat, reaching a consensus often takes time, members contribute unevenly to the discussion, and messages are unorganized. Hence, we aimed to explore the feasibility of a facilitator chatbot agent to improve group chat discussions. We conducted a needfinding survey to identify key features for a facilitator chatbot. We then implemented GroupfeedBot, a chatbot agent that could facilitate group discussions by managing the discussion time, encouraging members to participate evenly, and organizing members' opinions. To evaluate GroupfeedBot, we performed preliminary user studies that varied for diverse tasks and different group sizes. We found that the group with GroupfeedBot appeared to exhibit more diversity in opinions even though there were no differences in output quality and message quantity. On the other hand, GroupfeedBot promoted members' even participation and effective communication for the medium-sized group.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Prior CHI studies have investigated potential scenarios of interacting with multi-party based chatbots as well as diverse roles of chatbots and their positive impact on familiarization among teammates, generating more ideas and diverse views, and nudging silent people to speak up.
“prior CHI studies [43, 50, 77, 79, 93] have investigated potential scenarios of interacting with multi-party based chatbots [77] as well as diverse roles of chatbots and their positive impact on familiarization among teammates [79], generating more ideas and diverse views [43, 50, 79], and nudging silent people to speak up [43].”
From The Promise and Peril of ChatGPT in Higher Education: Opportunities, Challenges, and Design Implications (2024) · cites this paper as [43]
Cites
Email overload at work: an analysis of factors associated with email strain
Dabbish, L. A., & Kraut, R. E. (2006) · Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work · doi:10.1145/1180875.1180941 · Cited by 238 · see citing works
Understanding Chatbot-mediated Task Management
Toxtli, C., Monroy-Hernández, A., & Cranshaw, J. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173632 · Cited by 127 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Wang, Q., Saha, K., Gregori, E., Joyner, D., & Goel, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445645 · Cited by 120 · see citing works
Park, H., & Ahn, D. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642785 · Cited by 84 · see citing works
IntroBot: Exploring the Use of Chatbot-assisted Familiarization in Online Collaborative Groups
Shin, D., Kim, S., Shang, R., Lee, J., & Hsieh, G. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580930 · Cited by 37 · see citing works
Self-citationDo, H. J., Kong, H.-K., Tetali, P., Lee, J., & Bailey, B. P. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579532 · Cited by 32 · see citing works
Self-citationComparing Moderation Strategies in Group Chats with Multi-User Chatbots
Wagner, N., Kraus, M., Tonn, T., & Minker, W. (2022) · Proceedings of the 4th Conference on Conversational User Interfaces · doi:10.1145/3543829.3544527 · Cited by 15 · see citing works
Examining Teenagers’ Perceptions of Conversational Agents in Learning Settings
Nguyen, H. (2022) · Interaction Design and Children · doi:10.1145/3501712.3529740 · Cited by 15 · see citing works
- Authors
- Kim, S., Eun, J., Oh, C., Suh, B., & Lee, J.
- Affiliations
- Seoul National University (KR)
- Carnegie Mellon University (US)
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-13
- OpenAlex
- W3030120704
- Cited by
- 133
- References
- 2
- Citations by year
- 2026: 22 · 2025: 34 · 2024: 21 · 2023: 19 · 2022: 20 · 2021: 16 · 2020: 1
- FWCI
- 10.23
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- National Research Foundation of Korea (NRF-2018M3E2A1081492)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- chatbots, discussion facilitation, group participation, consensus building, opinion diversity, message organization, group size effects
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
