Teaching UML using a RAG-based LLM

Teaching the Unified Modelling Language (UML) is a critical task in the frame of Software Engineering courses. Teachers need to understand the students' behavior along with their modeling activities to provide suggestions and feedback to avoid more frequent mistakes and improve their capabilities. This paper presents a novel approach for teaching the UML in Software Engineering courses, focusing on understanding and improving student behavior and capabilities during modeling activities. It introduces a cloud-based tool that captures and analyzes UML diagrams created by students during their interactions with a UML modeling tool. The key aspect of the proposal is the integration of a Retrieval Augmented Generation Large Language Model (RAG-based LLM), which generates insightful feedback for students by leveraging knowledge acquired during the modeling process.The effectiveness of this method is demonstrated through an experiment involving a substantial dataset comprising 5,120 labeled UML models. The validation process confirms the performance of the UML RAG-based LLM in providing relevant feedback related to entities and relationships in the students' models. Additionally, a qualitative analysis highlights the user satisfaction, underscoring its potential as a valuable tool in enhancing the learning experience in software modeling education.