Generative AI for Education: A Retrieval-Augmented System for Effective Feedback in Self-Assessment
Juan Martínez-Romo, Lourdes Araujo, Laura Plaza, Fernando López-Ostenero · 2025
The application of generative AI in education has shown significant potential to enhance learning outcomes by providing personalized, adaptive feedback to students. In this work, we present a novel Retrieval-Augmented Generation (RAG) system designed to improve the explanations and feedback provided to students during self-assessment activities. The system we developed is grounded in the course's reference material, ensuring that the feedback remains accurate, consistent, and contextually relevant to the student's curriculum. The system retrieves information directly from the textbook, reducing ambiguity and interpretation errors, and generates responses tailored to the specific needs of each student. The feedback is not only designed to correct misconceptions but also to reinforce key concepts, making the system a valuable tool for self-guided learning. In this study, we also explore the importance of prompt engineering in creating effective AI-generated feedback. We detail the iterative process used to optimize the prompts and the strategies employed to ensure high-quality, interpretable responses. The findings from this work suggest that generative AI, when integrated with subject-specific textbooks and careful prompt engineering, can significantly enhance the educational experience by providing dynamic, and contextually accurate feedback. This approach opens new possibilities for AI-driven education tools, contributing to more personalized and effective learning experiences.