Cited by 196Artificial Intelligence in EducationTopic ModelingIntelligent Tutoring Systems and Adaptive Learning

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Retrieval-augmented generation for educational application: A systematic survey

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Li, Zongxi, Wang, Zijian, Wang, Weiming, Hung, Kevin, Xie, Haoran, Wang, Fu Lee·2025·Computers and Education: Artificial Intelligence

DOI 10.1016/j.caeai.2025.100417

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Abstract

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.

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Ram, 2023, In-context retrieval-augmented language models, Transactions of the Association for Computational Linguistics, 11, 1316, 10.1162/tacl_a_00605 · DOI · Paper page
Roemer, 2015, Altmetrics and the role of librarians, Library Technology Reports, 51, 31

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Authors: Li, Z., Wang, Z., Wang, W., Hung, K., Xie, H., & Wang, F. L.
Affiliations: Lingnan University (HK)
Hong Kong Metropolitan University (HK)
Corresponding author: Li, Zongxi
ORCID: Li, Zongxi
Wang, Zijian
Hung, Kevin
Xie, Haoran
Wang, Fu Lee
Venue: Computers and Education: Artificial Intelligence
Publisher: Elsevier BV
Published: 2025-06
Volume: 8
Pages: 100417
ISSN: 2666-920X (ISSN-L)
DOI: 10.1016/j.caeai.2025.100417
OpenAlex: W4410362031
Cited by: 151
References: 96
Citations by year: 2026: 122 · 2025: 29
FWCI: 155.31
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: University Grants Committee (UGC/FDS16/E10/23)
Research Grants Council, University Grants Committee (102722)
Hong Kong Metropolitan University (CP/2022/02)
Lingnan University (SDS24A2)
Topics: Social Sciences › Education › Artificial Intelligence in Education
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords: retrieval-augmented generation, large language models, artificial intelligence in education, hallucination mitigation, knowledge base retrieval, interactive learning system, educational content development, automated assessment, multimodal retrieval

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Retrieval-augmented generation for educational application: A systematic survey

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