Who You Explain To Matters: Learning by Explaining to Conversational Agents with Different Pedagogical Roles

要旨

Conversational agents are increasingly used in education for learning support. An application is "learning by explaining", where learners explain their understanding to an agent. However, existing research focuses on single roles, leaving it unclear how different pedagogical roles influence learners' interaction patterns, learning outcomes and experiences. We conducted a between-subjects study (N=96) comparing agents with three pedagogical roles (Tutee, Peer, Challenger) and a control condition while learning an economics concept. We found that different pedagogical roles shaped learning dynamics, including interaction patterns and experiences. Specifically, the Tutee agent elicited the most cognitive investment but led to high pressure. The Peer agent fostered high absorption and interest through collaborative dialogue. The Challenger agent promoted cognitive and metacognitive acts, enhancing critical thinking with moderate pressure. The findings highlight how agent roles shape different learning dynamics, guiding the design of educational agents tailored to specific pedagogical goals and learning phases.

受賞
Honorable Mention
著者
Zhengtao Xu
National University of Singapore, Singapore, Singapore
Junti Zhang
National University of Singapore, Singapore, Singapore
Anthony Tang
Singapore Management University, Singapore, Singapore
YI-CHIEH LEE
National University of Singapore, Singapore, Singapore

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Design Methods and Frameworks

P1 - Room 112
7 件の発表
2026-04-15 18:00:00
2026-04-15 19:30:00