Retrieval-augmented generation for educational application: A systematic survey

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Advancements in large language models (LLMs) have transformed AI-driven education, enabling innovative applications across various learning and teaching domains. However, LLMs still face several challenges, including hallucination and static internal knowledge, which hinder their reliability in educational settings. Retrieval-Augmented Generation (RAG) enhances LLMs by retrieving relevant information from an external knowledge base and incorporating it into the LLM's generation process. This approach improves factual accuracy and enables dynamic knowledge updates, making LLMs particularly suitable for educational applications. In this paper, we comprehensively review existing research that integrates RAG into educational scenarios. We first clarify the definition and workflow of RAG, and following the indexing mechanism of RAG, we introduce different types of retrievers and generation optimization methods. As the main focus of this work, we explore the practical applications of RAG in education, covering interactive learning systems, generation and assessment of educational content, and large-scale deployment in educational ecosystems. Based on our comprehensive review, this paper discusses existing challenges and future directions, including mitigating hallucinations, ensuring the completeness and timeliness of retrieved knowledge, reducing computational costs, and enhancing multimodal support for RAG-based educational applications.