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

TL;DR

This paper introduces a novel framework that automates the construction of hierarchical knowledge graphs and generates personalized exercises by reasoning over learners' mastery data, enhancing adaptive education systems.

Contribution

It presents a Generative GraphRAG framework with LLM-based automatic knowledge graph construction and adaptive exercise generation, addressing scalability and personalization limitations.

Findings

Automated hierarchical knowledge graphs effectively capture educational content.

Graph-based reasoning enables personalized exercise generation.

Framework receives positive user feedback in real-world educational settings.

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.