A Systematic Literature Review of Retrieval-Augmented Generation Implementation for Enhancing Large Language Models in Education

The rapid advancement of Large Language Models (LLM) has led to the creation of increasingly adaptive intelligent learning systems. However, many educational implementations of LLMs still rely on internal model knowledge without sufficient grounding in reliable external sources, which may reduce response accuracy, contextual relevance, and trustworthiness. One approach proposed to address this limitation is Retrieval-Augmented Generation (RAG), which combines the LLM’s generative capabilities with external information retrieval systems. Nevertheless, evidence regarding how RAG has been implemented, optimized, and evaluated in educational contexts remains fragmented. This study aimed to evaluate the RAG implementation in supporting LLM performance in learning environments by examining (1) the most frequent types of learning activities involving RAG, (2) the RAG implementation effectiveness in improving response quality, (3) the optimization techniques used to improve RAG results, and (4) the challenges and opportunities faced in its integration in education. The study was conducted systematically using the PICO framework, drawing on articles from the Scopus and IEEE databases, spanning the period from 2021 to 2025. The analysis of 50 studies revealed that RAG is most applied in the contexts of question answering, personalized learning, and tutoring, with significant improvements in the aspects of accuracy, relevance, and personalization of responses. However, not all studies explicitly reported the implementation of optimization techniques to RAG. These techniques comprised knowledge injection, prompt engineering, and query expansion. Challenges remain in reasoning, retrieval accuracy, and system integration, but there is strong potential for developing more adaptive and contextualized learning systems. This review recommends stronger retrieval optimization, better alignment with pedagogical objectives, and broader evaluation using educational outcome measures to maximize the impact of RAG-enhanced LLMs in digital education.