LPITutor: An LLM based personalized intelligent tutoring system using RAG and prompt engineering

Abstract

Development of large language models (LLMs) has transformed the landscape of personalized education through intelligent tutoring systems (ITS) which responds to diverse learning requirements. This article proposed a model named LLM based Personalized Intelligent Tutoring System (LPITutor) that is based on LLM for personalized ITS that leverages retrieval-augmented generation (RAG) and advanced prompt engineering techniques to generate customized responses aligned with students’ requirements. The aim of LPITutor is to provide customized learning content that adapts to different levels of learners skills and question complexity. The performance of proposed model was evaluated on accuracy, completeness, clarity, difficulty alignment, coherence, and relevance. The finding of LPITutor indicates that it effectively balances the response accuracy and clarity with significant alignment to the difficulty level of student queries. The proposed work also emphasises the broader implications of artificial intelligence (AI)-driven ITS in education and presents future directions for improving the adaptation and optimization of LPITutor.