用大模型驱动的多智能体系统优化知识图谱结构，提升学习路径识别准确率。
MAS-KCL: Knowledge component graph structure learning with large language model-based agentic workflow
- 基于大模型的多智能体协同修正知识图谱结构
- 双向反馈机制动态调整边权重，加速结构学习
- 在5个模拟和4个真实数据集上验证有效性，适合教育AI研究者
知识组件（KCs）是教育领域的基本知识单元，其关系图谱可揭示知识点间的依赖关系。精准的KC图谱有助于教师定位学生在特定知识点上的薄弱环节，从而实施针对性教学干预。本文提出一种名为MAS-KCL的KC图谱结构学习算法，采用大语言模型驱动的多智能体系统，对图谱进行自适应修改与优化，并引入双向反馈机制：智能体利用该机制评估图中边的价值，动态调整各边生成概率分布，显著提升结构学习效率。我们在5个合成数据集和4个真实教育数据集上进行了实验，结果表明该方法在学习路径识别方面表现优异。通过准确捕捉学习路径，教师可设计更全面的学习方案，帮助学生更有效地达成学习目标，推动教育可持续发展。
原文摘要 · Abstract (English)
Knowledge components (KCs) are the fundamental units of knowledge in the field of education. A KC graph illustrates the relationships and dependencies between KCs. An accurate KC graph can assist educators in identifying the root causes of learners' poor performance on specific KCs, thereby enabling targeted instructional interventions. To achieve this, we have developed a KC graph structure learning algorithm, named MAS-KCL, which employs a multi-agent system driven by large language models for adaptive modification and optimization of the KC graph. Additionally, a bidirectional feedback mechanism is integrated into the algorithm, where AI agents leverage this mechanism to assess the value of edges within the KC graph and adjust the distribution of generation probabilities for different edges, thereby accelerating the efficiency of structure learning. We applied the proposed algorithm to 5 synthetic datasets and 4 real-world educational datasets, and experimental results validate its effectiveness in learning path recognition. By accurately identifying learners' learning paths, teachers are able to design more comprehensive learning plans, enabling learners to achieve their educational goals more effectively, thus promoting the sustainable development of education.
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