Application of Retrieval-Augmented Generation (RAG) Systems in Software Engineering Education: An Approach Based on Generative AI and DevOps

Abstract

This paper presents a systematic literature review of the application of retrieval-augmented generation (RAG) systems in educational settings, with a focus on teaching software engineering and related computing disciplines. Drawing on case studies, academic experiments, and surveys of teachers and students, it provides an overview of the current landscape, highlighting perceptions, reported effectiveness, and the technology’s impact in academia. Based on an analysis of 71 selected scientific papers, the review synthesises evidence on the extent to which RAG systems mitigate hallucinations and improve human–AI interaction. In addition, it suggests that many approaches discussed across studies could be strategically aligned with the integration of DevOps practices and RAG, enhancing their use through automation, continuous improvement, and the agile adoption of technologies within educational processes.