Integrating Large Language Models (LLMs) into educational practice enables personalized learning by accommodating diverse learner behaviors. This study explored diverse learner profiles within a multi-agent, LLM-empowered learning environment. Data was collected from 312 undergraduate students at a university in China as they participated in a six-module course. Based on hierarchical cluster analyses of system profiles and student-AI interactive dialogues, we found that students exhibit varied behavioral, cognitive, and emotional engagement tendencies. This analysis allowed us to identify two types of dropouts (early dropouts and stagnating interactors) and three completer profiles (active questioners, responsive navigators, and lurkers). The results showed that high levels of interaction do not always equate to productive learning and vice versa. Prior knowledge significantly influenced interaction patterns and short-term learning benefits. Further analysis of the human-AI dialogues revealed that some students actively engaged in knowledge construction, while others displayed a high frequency of regulatory behaviors. Notably, both groups of students achieved comparable learning gains, demonstrating the effectiveness of the multi-agent learning environment in supporting personalized learning. These results underscore the complex and multifaceted nature of engagement in human-AI collaborative learning and provide practical implications for the design of adaptive educational systems.

翻译：将大语言模型（LLMs）整合到教育实践中，通过适应多样化的学习者行为，实现了个性化学习。本研究探索了在一个多智能体、LLM赋能的学习环境中的多样化学习者特征。数据收集自中国一所大学的312名本科生，他们参与了一门包含六个模块的课程。基于对系统日志和学生-AI交互对话的层次聚类分析，我们发现学生表现出不同的行为、认知和情感参与倾向。该分析使我们能够识别出两类辍学者（早期辍学者和停滞交互者）以及三类完成者特征（主动提问者、响应式导航者和潜学者）。结果表明，高水平的交互并不总是等同于高效的学习，反之亦然。先验知识显著影响了交互模式和短期学习收益。对人机对话的进一步分析显示，一些学生积极参与知识建构，而另一些学生则表现出高频的调节行为。值得注意的是，两组学生取得了相当的学习收益，这证明了多智能体学习环境在支持个性化学习方面的有效性。这些结果突显了人机协作学习中参与度的复杂性和多面性，并为适应性教育系统的设计提供了实践启示。