AI-powered learning content generation through retrieval-augmented generation for improved accuracy and personalisation
The integration of artificial intelligence (AI) in education has transformed content delivery and personalisation, with Retrieval-Augmented Generation (RAG) emerging as a promising architecture to enhance the accuracy and contextual relevance of AI-generated educational materials. This study aims to...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2025
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| Online Access: | http://journalarticle.ukm.my/26732/1/58-68%20-.pdf http://journalarticle.ukm.my/26732/ http://ejournal.ukm.my/jpend |
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| Summary: | The integration of artificial intelligence (AI) in education has transformed content delivery and personalisation, with Retrieval-Augmented Generation (RAG) emerging as a promising architecture to enhance the accuracy and contextual relevance of AI-generated educational materials. This study aims to systematically review the application, effectiveness, and challenges of RAG-powered educational content generation systems. The research employed a systematic literature review (SLR) design, guided by the PRISMA 2020 protocols. A total of 26 peer-reviewed articles published between 2020 and 2025 were selected from the Scopus database. No human participants were involved, as the study is based on literature. Data were extracted using a predefined matrix and analysed thematically to identify recurring patterns and contradictions. Findings revealed three major themes: (1) the dominance of retriever-generator pipelines and modular platforms such as OpenRAG and LearnRAG; (2) significant improvements in content factuality, student performance, and explainability; and (3) persistent limitations including infrastructure constraints, data bias, and ethical concerns. The review concludes that while RAG enhances educational AI systems, equitable access and responsible implementation remain critical. Implications include the need for policy frameworks, improved infrastructure, and future research on multilingual and domain-specific RAG applications. |
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