Design and Evaluation of a RAG-Based Educational Assistant Grounded in Course Materials: A Case Study in Vocational Training

Abstract

This study presents the design, implementation, and exploratory classroom evaluation of a web-based educational assistant built on Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) for Vocational Education and Training (VET). The platform was designed to generate responses based on teacher-provided course materials, preserve source traceability, and return an abstention message when the retrieved evidence is insufficient. The assistant was deployed in an authentic classroom setting within the Higher Vocational Training programme in Network and Information Systems Administration (ASIR). Nineteen students and one instructor used the system during a practical session and completed a post-session questionnaire combining Likert-scale items with open-ended questions. The findings indicate positive student perceptions of usability, response clarity, perceived reliability, and learning support. Participants particularly valued the ability to obtain focused answers aligned with the instructional materials. The evaluation also revealed a relevant trade-off: restricting the assistant to a controlled corpus reinforced curricular consistency and perceived trustworthiness but limited its capacity to address questions insufficiently covered by the available resources. The absence of conversational memory emerged as the most frequently requested improvement. These preliminary findings suggest that course-constrained RAG assistants may constitute valuable complementary tools for transparent and pedagogically supervised AI-supported learning in technical VET contexts.