Retrieval-augmented generation for educational application: A systematic survey

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…

• HK
  Hong Kong Metropolitan University

  Award: CP/2022/02
• UG
  University Grants Committee

  Award: UGC/FDS16/E10/23
• RG
  Research Grants Council, University Grants Committee

  Award: 102722
• LU
  Lingnan University

  Award: SDS24A2