An Educator-Oriented Retrieval-Augmented Generation Agent for Evidence-Based XR Adoption in Robotics Education

Extended Reality (XR) technologies have demonstrated considerable potential for enhancing robotics education by enabling immersive visualization, safe experimentation and improved access to practical laboratory experiences. Despite these advantages, the adoption of XR in robotics education remains limited because educators must navigate an increasingly fragmented body of research spanning robotics, educational technology, human–robot interaction, artificial intelligence and instructional design. Existing scientific databases provide access to publications but offer limited support for transforming dispersed research findings into actionable, evidence-based educational guidance. This paper presents an educator-oriented Retrieval-Augmented Generation (RAG) approach designed to support XR adoption in robotics education. The proposed approach combines a curated corpus of 19 scientific publications with a document qualification framework that assesses relevance, RAG readiness, completeness, authority and accuracy. The resulting knowledge base is integrated into an RAG prototype that uses semantic retrieval and large language model generation to produce context-sensitive responses to educator queries. Four illustrative educator-persona scenarios demonstrate how the prototype can organize retrieved information into structured recommendations for instructional planning and technology implementation. These examples illustrate the intended functionality of the approach but do not establish its retrieval accuracy, factual grounding, citation reliability or educational effectiveness. Future work will focus on reproducible corpus selection, validation of the qualification framework, quantitative evaluation of retrieval and generation quality and studies involving educators in authentic teaching contexts. This work provides a methodological and technical foundation for investigating domain-specific RAG assistants as decision-support tools for the use of immersive technologies in robotics education.