From Large Language Models to Retrieval-Augmented Generation: A Systematic Review of AI-Powered Educational Systems From Large Language Models to Retrieval-Augmented Generation: A Systematic Review of AI-Powered Educational Systems
Abstract
Artificial Intelligence (AI) is rapidly restructuring education by enabling personalized learning, automated assessment, and intelligent tutoring systems.Recent developments in Large Language Models (LLMs) are demonstrating strong capabilities in natural language understanding and content generation, enabling applications such as conversational tutoring, question generation, and feedback delivery.However, LLM-based educational systems suffer from critical limitations such as hallucinations, outdated knowledge, lack of curriculum alignment, and limited transparency.To address these challenges, Retrieval-Augmented Generation (RAG) is a promising paradigm that integrates external knowledge retrieval with generative models, enabling context-aware, accurate, and domainspecific responses.This paper presents an organized and inclusive review of AI-powered educational systems, tracking the evolution from traditional AI and standalone LLMs to RAG-enhanced architectures.The review analyzes recent research on RAG-based educational applications, including intelligent tutoring systems, virtual assistants, content generation, assessment, and feedback mechanisms.Finally, the study identifies critical research gaps and outlines future directions for developing curriculum-aware, adaptive, and trustworthy RAG-based educational systems.
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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