A Retrieval-Augmented Large Language Model for Dynamic Personalization in Intelligent Tutoring Systems

Abstract

This article presents a novel approach to Intelligent Tutoring Systems (ITS) by integrating Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) to enable dynamic personalization in educational contexts. The system addresses limitations in traditional ITS that rely on static, rule-based approaches by implementing a three-layered architecture combining semantic retrieval mechanisms with generative AI capabilities. Using GPT-4 as the core LLM enhanced with a custom RAG framework, the system demonstrates improvements in response accuracy (93%), inference speed (2.1 seconds per prompt), and computational efficiency compared to a standard GPT-4 baseline, a traditional rule-based ITS, and an LLM with keyword-based retrieval. The research employs both ASSISTments (fine-grained interaction data) and EdNet (large-scale longitudinal data) datasets for evaluation. Results show that the RAG-enhanced system achieves 40% better contextual relevance compared to standard LLM implementations. The framework incorporates adaptive prompting strategies, real-time knowledge base updates, and multi-level personalization algorithms to create a dynamic educational environment.