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

2026-01-23

AI Summary

This study addresses the limitation of existing educational dialogue agents, which typically adopt a single role and thus fail to elucidate how distinct pedagogical roles influence learners’ interaction patterns and learning outcomes. For the first time, it systematically compares three agent roles—tutee, peer, and challenger—within the “explain-to-agent” paradigm. Through a human-agent dialogue experiment (N=96) integrating behavioral analysis and subjective measures, the research examines their differential effects on cognitive engagement, metacognitive behaviors, and learning experience. Findings indicate that the tutee role significantly enhances cognitive engagement but also increases perceived stress; the peer role fosters greater immersion and interest; and the challenger role effectively promotes critical thinking and metacognitive activity. These results provide both theoretical grounding and practical guidance for tailoring dialogue agents to specific instructional objectives.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent Communication
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• conversational agents
• learning by explaining
• pedagogical roles
• interaction patterns
• learning outcomes

Innovation

• conversational agents
• learning by explaining
• pedagogical roles
• cognitive investment
• collaborative dialogue