The DOI matches its Crossref record; 89 of the 96 references it cites were found in a registry or an index.
Retrieval-augmented generation for educational application: A systematic survey
Open accessAbstract
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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Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025
Billion-scale similarity search with GPUs
Johnson, J., Douze, M., & Jegou, H. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921572 · Cited by 4,228
Dense passage retrieval for open-domain question answering
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575
The probabilistic relevance framework: BM25 and beyond
Robertson, S., & Zaragoza, H. (2009) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000019 · Cited by 3,374
Conceptualizing AI literacy: An exploratory review
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100041 · Cited by 2,205
Vision, challenges, roles and research issues of Artificial Intelligence in Education
Hwang, G.-J., Xie, H., Wah, B. W., & Gašević, D. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100001 · Cited by 1,435
Self-citationRetrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · UCL Discovery (University College London) · Cited by 1,187
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Izacard, G., & Grave, E. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.74 · Cited by 1,109
Application and theory gaps during the rise of artificial intelligence in education
Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100002 · Cited by 1,003
Self-citationA Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
Fan, W., Ding, Y., Ning, L., Wang, S., Li, H., Yin, D., Chua, T.-S., & Li, Q. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671470 · Cited by 827
Research works printed but not linked: 2
- 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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Comparing traditional AI, agentic ai and agentic rag for dialogic online education
English, V. (2025) · Asian Journal of Education and Training · doi:10.20448/edu.v11i4.7926 · Cited by 5
Qi, H., Chung-Ho, S., Chan, R. Y.-Y., & Wong, C. M. V. (2025) · IEEE Access · doi:10.1109/access.2025.3600316 · Cited by 5
Metadata
- 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
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2025-06
- Volume
- 8
- Pages
- 100417
- ISSN
- 2666-920X (ISSN-L)
- 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
- 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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