Personalized education systems increasingly rely on structured knowledge representations to support adaptive learning and question generation. However, existing approaches face two fundamental limitations. First, constructing and maintaining knowledge graphs for educational content largely depends on manual curation, resulting in high cost and poor scalability. Second, most personalized education systems lack effective support for state-aware and systematic reasoning over learners' knowledge, and therefore rely on static question banks with limited adaptability. To address these challenges, this paper proposes a Generative GraphRAG framework for automated knowledge modeling and personalized exercise generation. It consists of two core modules. The first module, Automated Hierarchical Knowledge Graph Constructor (Auto-HKG), leverages LLMs to automatically construct hierarchical knowledge graphs that capture structured concepts and their semantic relations from educational resources. The second module, Cognitive GraphRAG (CG-RAG), performs graph-based reasoning over a learner mastery graph and combines it with retrieval-augmented generation to produce personalized exercises that adapt to individual learning states. The proposed framework has been deployed in real-world educational scenarios, where it receives favorable user feedback, suggesting its potential to support practical personalized education systems.

翻译：个性化教育系统日益依赖结构化知识表示来支持自适应学习与习题生成。然而，现有方法面临两个根本性局限。首先，针对教育内容的知识图谱构建与维护主要依赖人工标注，导致成本高昂且可扩展性差。其次，大多数个性化教育系统缺乏对学习者知识状态感知与系统化推理的有效支持，因而依赖适应性有限的静态题库。为应对这些挑战，本文提出一种用于自动化知识建模与个性化习题生成的生成式GraphRAG框架。该框架包含两个核心模块：第一模块为自动化分层知识图谱构建器（Auto-HKG），利用大语言模型从教育资源中自动构建捕获结构化概念及其语义关系的分层知识图谱；第二模块为认知图检索增强生成器（CG-RAG），通过对学习者掌握状态图进行基于图的推理，并结合检索增强生成技术，生成适应个体学习状态的个性化习题。所提框架已在真实教育场景中部署，并获得积极的用户反馈，表明其具备支持实用个性化教育系统的潜力。