How to Build an AI Tutor That Can Adapt to Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG)

Abstract

This paper introduces KG-RAG (Knowledge Graph-enhanced Retrieval-Augmented Generation), a novel framework that addresses two critical challenges in LLM-based tutoring systems: information hallucination and limited course-specific adaptation. By integrating knowledge graphs with retrieval-augmented generation, KG-RAG provides a structured representation of course concepts and their relationships, enabling contextually grounded and pedagogically sound responses. We implement the framework using Qwen2.5, demonstrating its cost-effectiveness while maintaining high performance. The KG-RAG system outperformed standard RAG-based tutoring in a controlled study with 76 university students (mean scores: 6.37 vs. 4.71, p<0.001, Cohen's d=0.86). User feedback showed strong satisfaction with answer relevance (84% positive) and user experience (59% positive). Our framework offers a scalable approach to personalized AI tutoring, ensuring response accuracy and pedagogical coherence.

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Reproductions