Go to ACL ARR 2026 January homepageOnlineMate: An LLM-Based Multi-Agent Companion System for Cognitive Support in Online Learning
Keywords: Theory of Mind, Online Learning, LLM for Education, Role Play, LLM-based Agent, Multi-agent System
Abstract: In online learning environments, students often lack personalized peer interactions, which are crucial for cognitive development and learning engagement. Although previous studies have employed large language models (LLMs) to simulate interactive learning environments, these interactions are limited to conversational exchanges, failing to adapt to learners’ individualized cognitive and psychological states. As a result, students’ engagement is low and they struggle to gain inspiration. To address this challenge, we propose **OnlineMate**, a multi-agent learning companion system driven by LLMs integrated with Theory of Mind (ToM). OnlineMate simulates peer-like roles, infers learners’ psychological states such as misunderstandings and confusion during collaborative discussions, and dynamically adjusts interaction strategies to support higher-order thinking. Comprehensive evaluations, including simulation-based experiments, human assessments, and real classroom trials, demonstrate that OnlineMate significantly promotes deep learning and cognitive engagement by elevating students’ average cognitive level while substantially improving emotional engagement scores.
Paper Type: Long
Research Area: NLP Applications
Research Area Keywords: educational applications
Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models
Languages Studied: English
Submission Number: 10586
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