用知识图谱增强大模型，让在线课程学习更精准可靠。
Leveraging Graph Retrieval-Augmented Generation to Support Learners' Understanding of Knowledge Concepts in MOOCs
- 用学习者个人和教育知识图谱生成个性化问题。
- 结合概念间关系回答问题，减少大模型幻觉。
- 适合想提升自学效率的在线学习者和教育研究者。
大规模开放在线课程（MOOCs）缺乏师生直接互动，学习者理解新知识概念存在困难。近年来，学习者越来越多地使用大语言模型（LLMs）辅助获取新知识，但其容易产生幻觉，影响可靠性。检索增强生成（RAG）通过生成前检索相关文档来缓解此问题。然而，由于学习材料非结构化，RAG在不同MOOC中的应用受限，且现有系统未能主动引导学习者满足其学习需求。为此，我们提出一种基于图谱的RAG流程，利用教育知识图谱（EduKGs）和个人知识图谱（PKGs）在CourseMapper平台中指导学习者理解知识概念。具体实现包括：(1) 基于个人知识图谱的问题生成方法，为学习者提供上下文相关的个性化问题；(2) 基于教育知识图谱的问题回答方法，利用知识概念间的关联解答学习者选择的问题。我们通过3位专家导师在CourseMapper平台上对3个不同MOOC进行了评估，结果表明该图谱增强的RAG方法具有提升学习者个性化理解新知识概念的潜力。
原文摘要 · Abstract (English)
Massive Open Online Courses (MOOCs) lack direct interaction between learners and instructors, making it challenging for learners to understand new knowledge concepts. Recently, learners have increasingly used Large Language Models (LLMs) to support them in acquiring new knowledge. However, LLMs are prone to hallucinations which limits their reliability. Retrieval-Augmented Generation (RAG) addresses this issue by retrieving relevant documents before generating a response. However, the application of RAG across different MOOCs is limited by unstructured learning material. Furthermore, current RAG systems do not actively guide learners toward their learning needs. To address these challenges, we propose a Graph RAG pipeline that leverages Educational Knowledge Graphs (EduKGs) and Personal Knowledge Graphs (PKGs) to guide learners to understand knowledge concepts in the MOOC platform CourseMapper. Specifically, we implement (1) a PKG-based Question Generation method to recommend personalized questions for learners in context, and (2) an EduKG-based Question Answering method that leverages the relationships between knowledge concepts in the EduKG to answer learner selected questions. To evaluate both methods, we conducted a study with 3 expert instructors on 3 different MOOCs in the MOOC platform CourseMapper. The results of the evaluation show the potential of Graph RAG to empower learners to understand new knowledge concepts in a personalized learning experience.
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