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

• URL: http://arxiv.org/abs/2311.17696v7
• Date: Wed, 12 Feb 2025 10:45:02 GMT
• Title: How to Build an Adaptive AI Tutor for Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG)
• Authors: Chenxi Dong, Yimin Yuan, Kan Chen, Shupei Cheng, Chujie Wen,
• Abstract summary: Large Language Models (LLMs) in Intelligent Tutoring Systems (ITS) presents transformative opportunities for personalized education.<n>Current implementations face two critical challenges: maintaining factual accuracy and delivering coherent, context-aware instruction.<n>This paper introduces Knowledge Graph-enhanced Retrieval-Augmented Generation (RAG), a novel framework that integrates structured knowledge representation with context-aware retrieval.
• Score: 5.305156933641317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Integrating Large Language Models (LLMs) in Intelligent Tutoring Systems (ITS) presents transformative opportunities for personalized education. However, current implementations face two critical challenges: maintaining factual accuracy and delivering coherent, context-aware instruction. While Retrieval-Augmented Generation (RAG) partially addresses these issues, its reliance on pure semantic similarity limits its effectiveness in educational contexts where conceptual relationships are crucial. This paper introduces Knowledge Graph-enhanced Retrieval-Augmented Generation (KG-RAG), a novel framework that integrates structured knowledge representation with context-aware retrieval to enable more effective AI tutoring. We present three key contributions: (1) a novel architecture that grounds AI responses in structured domain knowledge, (2) empirical validation through controlled experiments (n=76) demonstrating significant learning improvements (35% increase in assessment scores, p<0.001), and (3) a comprehensive implementation framework addressing practical deployment considerations. These results establish KG-RAG as a robust solution for developing adaptable AI tutoring systems across diverse educational contexts.

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