用大模型模拟有共情的学伴，帮在线学习者提升思维深度和参与感。
OnlineMate: An LLM-Based Multi-Agent Companion System for Cognitive Support in Online Learning
- 引入心智理论，让多个智能体感知学生心理状态并动态调整互动策略。
- 实测显示学生平均认知水平提升，情绪参与度显著提高。
- 适合需要个性化陪伴与思维激发的在线学习场景。
在线学习中，学生常缺乏个性化的同伴互动，而这对于认知发展和学习投入至关重要。尽管先前研究已利用大语言模型（LLM）模拟互动学习环境，但交互仅限于对话层面，未能根据学习者的个体认知与心理状态进行自适应调整，导致参与度低且难获启发。为此，我们提出 OnlineMate——一个基于大语言模型、融合心智理论（ToM）的多智能体学习伴侣系统。该系统模拟类同伴角色，通过协作讨论推断学生的误解与困惑等心理状态，并动态调整交互策略以支持高阶思维。综合评估包括模拟实验、人工评测及真实课堂试验，结果表明 OnlineMate 显著促进深度学习与认知投入，使学生平均认知水平提升，情感参与度大幅改善。
原文摘要 · Abstract (English)
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.
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