Intelligent Academic Support Systems Based on Retrieval-Augmented Generation

Abstract

Intelligent academic support systems based on Retrieval-Augmented Generation (RAG) represent a significant advancement in educational artificial intelligence by combining the reasoning capabilities of Large Language Models (LLMs) with external knowledge retrieval mechanisms. Traditional academic support tools often rely solely on pre-trained language models, which may produce hallucinations, outdated explanations, or curriculum-inconsistent responses. RAG-based systems address these limitations by retrieving relevant and verified academic content from curriculum repositories, institutional databases, digital libraries, and structured knowledge graphs before generating responses. When integrated with curriculum-aware artificial intelligence, Explainable Artificial Intelligence (XAI), Human-in-the-Loop (HITL) validation, and educational governance frameworks, RAG-based academic support systems significantly enhance instructional accuracy, learner engagement, personalization, and decision-making quality. This study explores intelligent academic support systems based on RAG through a qualitative review of recent scholarly literature. Findings indicate that RAG-driven systems improve academic performance, curriculum alignment, transparency, and learner trust while reducing misinformation and hallucinations. Despite challenges such as infrastructure requirements, knowledge base maintenance, and governance complexity, RAG-based academic support systems provide a scalable and reliable foundation for modern educational environments.