This chapter presents a Retrieval-Augmented Generation (RAG) framework to evaluate the originality of student projects in a Project-Based Learning (PBL) context within an advanced digital systems course of a Master’s degree in Electronic Engineering at the University of Seville . While PBL fosters creativity, technical competence, and active learning, assessing originality becomes increasingly complex as repositories of past projects grow and distinctions between inspiration, reuse, and plagiarism blur. Traditional plagiarism-detection tools focus on textual similarity and fail to capture conceptual novelty, especially in engineering contexts where code reuse is common.

The proposed system combines structured summarization using an open-weight large language model (DeepSeek-R1) with semantic retrieval over an indexed knowledge base of previous projects. Each historical project is automatically preprocessed and summarized into a structured format, including objectives, modules, hardware, and contextual keywords. These summaries are embedded and indexed using an RAG pipeline, enabling semantic comparison between new proposals and prior work. The system generates originality scores, identifies similar projects, and provides explanatory feedback.

Validation was conducted using 91 historical projects and 10 controlled test cases spanning the full originality spectrum. The system’s scores showed a strong correlation with expert evaluations (Pearson r = 0.87, p = 0.006), though it tended to be slightly more conservative. Qualitative feedback from instructors and students highlighted its usefulness as a cognitive support tool that enhances transparency, reduces memory bias, and promotes reflective refinement of project ideas. Rather than replacing human judgment, the framework functions as an explainable co-evaluation assistant, offering a scalable, locally deployable, and ethically grounded approach to originality assessment in engineering education.