用大模型设计可解释的学习路径，让个性化教学更透明可信。
Multi-Agent Learning Path Planning via LLMs
- 三类智能体协作，通过提示词与规则生成学习路径。
- 在MOOCCubeX数据集上，路径质量、知识连贯性均显著提升。
- 适合教育AI研究者和智能辅导系统开发者参考。
将大语言模型（LLMs）融入智能辅导系统，有望在高等教育中实现个性化学习的变革。然而，现有学习路径规划方法普遍缺乏透明性、适应性和以学习者为中心的可解释性。为此，本文提出一种基于角色与规则协作机制的多智能体学习路径规划（MALPP）框架，包含学习分析、路径规划与反思三个专用智能体，均由LLMs驱动。三者通过结构化提示和预设规则协同工作，分析学习者画像，生成定制化学习路径，并基于可解释反馈迭代优化。该框架基于认知负荷理论与最近发展区理论，确保推荐路径在认知层面契合且具有教学意义。在MOOCCubeX数据集上，使用七种LLMs的实验表明，MALPP在路径质量、知识序列一致性与认知负荷匹配度方面显著优于基线模型。消融实验证实了协作机制与理论约束的有效性。本研究推动了教育领域可信、可解释AI的发展，展示了由LLMs驱动的可扩展学习者中心自适应教学范式。
原文摘要 · Abstract (English)
The integration of large language models (LLMs) into intelligent tutoring systems offers transformative potential for personalized learning in higher education. However, most existing learning path planning approaches lack transparency, adaptability, and learner-centered explainability. To address these challenges, this study proposes a novel Multi-Agent Learning Path Planning (MALPP) framework that leverages a role- and rule-based collaboration mechanism among intelligent agents, each powered by LLMs. The framework includes three task-specific agents: a learner analytics agent, a path planning agent, and a reflection agent. These agents collaborate via structured prompts and predefined rules to analyze learning profiles, generate tailored learning paths, and iteratively refine them with interpretable feedback. Grounded in Cognitive Load Theory and Zone of Proximal Development, the system ensures that recommended paths are cognitively aligned and pedagogically meaningful. Experiments conducted on the MOOCCubeX dataset using seven LLMs show that MALPP significantly outperforms baseline models in path quality, knowledge sequence consistency, and cognitive load alignment. Ablation studies further validate the effectiveness of the collaborative mechanism and theoretical constraints. This research contributes to the development of trustworthy, explainable AI in education and demonstrates a scalable approach to learner-centered adaptive instruction powered by LLMs.
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