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Integrating Large Language Models (LLMs) in Intelligent Tutoring Systems(ITS) presents transformative opportunities for personalized education.However, current implementations face two critical challenges: maintainingfactual accuracy and delivering coherent, context-aware instruction. WhileRetrieval-Augmented Generation (RAG) partially addresses these issues, itsreliance on pure semantic similarity limits its effectiveness in educationalcontexts where conceptual relationships are crucial. This paper introducesKnowledge Graph-enhanced Retrieval-Augmented Generation (KG-RAG), a novelframework that integrates structured knowledge representation withcontext-aware retrieval to enable more effective AI tutoring. We present threekey contributions: (1) a novel architecture that grounds AI responses instructured domain knowledge, (2) empirical validation through controlledexperiments (n=76) demonstrating significant learning improvements (35%increase in assessment scores, p<0.001), and (3) a comprehensive implementationframework addressing practical deployment considerations. These resultsestablish KG-RAG as a robust solution for developing adaptable AI tutoringsystems across diverse educational contexts.

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