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[2602.00020] Beyond Static Question Banks: Dynamic Knowledge Expansion via LLM-Automated Graph Construction and Adaptive Generation
Summary
This paper presents a framework for dynamic knowledge expansion in personalized education, utilizing LLMs for automated graph construction and adaptive exercise generation.
Why It Matters
As education increasingly shifts towards personalized learning, this research addresses critical limitations in existing systems, such as the reliance on static question banks and manual knowledge graph curation. By automating these processes, the proposed framework could enhance scalability and adaptability in educational tools, potentially improving learning outcomes.
Key Takeaways
- The Generative GraphRAG framework automates knowledge modeling and exercise generation.
- It features two main modules: Auto-HKG for knowledge graph construction and CG-RAG for personalized exercise generation.
- The framework addresses scalability issues in educational content management.
- Real-world deployment shows positive user feedback, indicating practical applicability.
- The approach enhances adaptive learning by leveraging structured knowledge representations.
Computer Science > Computers and Society arXiv:2602.00020 (cs) [Submitted on 16 Jan 2026 (v1), last revised 13 Feb 2026 (this version, v2)] 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 View a PDF of the paper titled Beyond Static Question Banks: Dynamic Knowledge Expansion via LLM-Automated Graph Construction and Adaptive Generation, by Yingquan Wang and 3 other authors View PDF HTML (experimental) 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 sem...
