Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2026

Abstract

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.

Keywords

Conversational agents, Learning by explaining, Agent role design, User experience, Interaction patterns

Discipline

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Research Areas

Software and Cyber-Physical Systems

Areas of Excellence

Digital transformation

Publication

CHI '26: Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, Barcelona, Spain, April 13-17

First Page

1

Last Page

24

Identifier

10.1145/3772318.3790298

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3772318.3790298

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