Abstract Teaching the unified modeling language (UML) is a critical task that can benefit from emerging artificial intelligence-based solutions. This paper presents UML Miner, a visual paradigm plugin designed to support the teaching and learning of UML by capturing and analyzing students’ modeling activities to deliver personalized, context-aware feedback. The tool records fine-grained software modeling actions, storing them in an event log. For each diagram, a natural language description and its XML representation are stored separately. Modeling behavior is reconstructed through process mining techniques and compared with a reference model created by the instructor using conformance checking. This analysis adopts a declarative approach, leveraging the declare language to specify behavioral constraints without enforcing a rigid action sequence, thereby accommodating multiple valid modeling strategies while preserving semantic rigor. The detected violations, together with the diagram’s natural language and XML representations, are integrated into a retrieval-augmented generation-enabled large language model, which combines accumulated knowledge with the discovered process constraints to generate enriched, pedagogically meaningful feedback in real time. An exploratory case study conducted in a real-world learning scenario evaluates the tool’s performance from both qualitative and quantitative perspectives, demonstrating its potential to improve learning outcomes and student engagement.
Ardimento et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: