# Automated Feedback on Student-Generated UML and ER Diagrams Using Large Language Models

arXiv: https://arxiv.org/abs/2507.23470 · HTML: https://mlsanity.com/paper/2507.23470/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 31 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [Towards Human-Bot Collaborative Software Architecting with ChatGPT](https://mlsanity.com/paper/2302.14600/) (2023)
- [How LLMs Aid in UML Modeling: An Exploratory Study with Novice Analysts](https://mlsanity.com/paper/2404.17739/) (2024)
- [Assessing UML Diagrams by GPT: Implications for Education](https://mlsanity.com/paper/2412.17200/) (2024)

## Built on by (most cited first, 1 of 1)

- [Confidence-Aware Automated Assessment of Student-Drawn Scientific Models](https://mlsanity.com/paper/2606.20264/) (2026)

## Abstract (arXiv)

UML and ER diagrams are foundational in computer science education but come with challenges for learners due to the need for abstract thinking, contextual understanding, and mastery of both syntax and semantics. These complexities are difficult to address through traditional teaching methods, which often struggle to provide scalable, personalized feedback, especially in large classes. We introduce DUET (Diagrammatic UML & ER Tutor), a prototype of an LLM-based tool, which converts a reference diagram and a student-submitted diagram into a textual representation and provides structured feedback based on the differences. It uses a multi-stage LLM pipeline to compare diagrams and generate reflective feedback. Furthermore, the tool enables analytical insights for educators, aiming to foster self-directed learning and inform instructional strategies. We evaluated DUET through semi-structured interviews with six participants, including two educators and four teaching assistants. They identified strengths such as accessibility, scalability, and learning support alongside limitations, including reliability and potential misuse. Participants also suggested potential improvements, such as bulk upload functionality and interactive clarification features. DUET presents a promising direction for integrating LLMs into modeling education and offers a foundation for future classroom integration and empirical evaluation.