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.

翻译：在在线学习环境中，学生往往缺乏个性化的同伴互动，而这种互动对于认知发展和学习投入至关重要。尽管先前的研究已利用大语言模型（LLMs）来模拟交互式学习环境，但这些互动仅限于对话交流，未能适应学习者个性化的认知和心理状态。因此，学生的参与度较低，且难以获得启发。为应对这一挑战，我们提出了OnlineMate——一个融合心理理论（ToM）、由大语言模型驱动的多智能体学习伴侣系统。OnlineMate模拟类似同伴的角色，在协作讨论中推断学习者的误解、困惑等心理状态，并动态调整互动策略以支持高阶思维。包括模拟实验、人工评估和真实课堂试验在内的综合评估表明，OnlineMate通过提升学生的平均认知水平，同时显著改善情感投入分数，有效促进了深度学习和认知参与。