Piecing Together Teamwork: A Responsible Approach to an LLM-based Educational Jigsaw Agent

要旨

Conversational agents have been used to support student learning for some time, but the emergence of Large Language Models (LLMs) poses a novel opportunity to enhance their capabilities in collaborative settings. LLM-powered agents can provide timely interventions in collaborative conversations when a teacher is unable to assist the students. However, the use of LLMs in such tools raises many ethical questions and concerns, especially for use with young, impressionable populations. In this work, we present the human-centered design and evaluation of an LLM-based agent aimed to facilitate small group collaboration in middle- and high-school classrooms. Fifty-eight groups of dyads and triads (145 participants), aged 12-17, collaborated in a jigsaw activity and were assigned to be assisted by our agent or not. The results showed decreased self-reported ratings of social loafing and increased use of language related to respectful collaboration in interactions with the agent compared to those without.

著者
Emily Doherty
University of Colorado Boulder, Boulder, Colorado, United States
E. Margaret. Perkoff
University of Colorado Boulder, Boulder, Colorado, United States
Sean von Bayern
University of Colorado Boulder, Boulder, Colorado, United States
Rui Zhang
University of Colorado Boulder, Boulder, Colorado, United States
Indrani Dey
University of Wisconsin-Madison, Madison, Wisconsin, United States
Michal Bodzianowski
University of Colorado Boulder, Boulder, Colorado, United States
Sadhana Puntambekar
University of Wisconsin-Madison, Madison, Wisconsin, United States
Leanne Hirshfield
University of Colorado, Boulder, Colorado, United States
DOI

10.1145/3706598.3713349

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713349

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: AI in the Classroom

G303
6 件の発表
2025-05-01 01:20:00
2025-05-01 02:50:00
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