Beyond Static Question Banks: Dynamic Knowledge Expansion via LLM-Automated Graph Construction and Adaptive Generation

• URL: http://arxiv.org/abs/2602.00020v1
• Date: Fri, 16 Jan 2026 16:40:30 GMT
• Title: Beyond Static Question Banks: Dynamic Knowledge Expansion via LLM-Automated Graph Construction and Adaptive Generation
• Authors: Yingquan Wang, Tianyu Wei, Qinsi Li, Li Zeng,
• Abstract summary: This paper proposes a Generative GraphRAG framework for automated knowledge modeling and personalized exercise generation.<n>The proposed framework has been deployed in real-world educational scenarios, where it receives favorable user feedback.
• Score: 6.083691376526491
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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