Towards a Retrieval-Augmented Generation Framework for Originality Evaluation in Projects-Based Learning Classrooms

Abstract

Project-Based Learning is central to modern engineering education, but assessing the originality of student work poses significant challenges, particularly when previous project repositories are accessible. This paper addresses the issue by proposing a framework based on Retrieval-Augmented Generation and Large Language Models to evaluate the originality of project proposals in the context of a master’s course in Electronic Engineering. The system processes and summarizes prior work—including reports, code, and presentations—creating a semantically indexed knowledge base. Upon receiving a new proposal, the RAG system compares it to this base, identifies the most similar previous works, and generates an originality assessment. Results show the effectiveness of the approach, with a strong correlation (coefficient of 0.87) between the originality scores assigned by the system and those given by expert instructors in a blind evaluation. The proposed RAG system demonstrates its ability to systematically detect similarities and potential lack of originality that might be overlooked by human evaluators due to the volume of prior work. This framework provides an objective and efficient tool to support originality assessment, benefiting both instructors and students by promoting genuine innovation in PBL settings.