LLM-Assisted Semantic Assessment of Entity-Relationship Diagrams in Conceptual Modeling Education

Providing timely and meaningful feedback on student-created conceptual models requires assessing syntactic and semantic correctness, the latter indicating whether a model adequately represents the intended domain. Existing rule- and label-based approaches provide limited support for alternative valid terminology and modeling decisions, while direct large language model (LLM) assessments may produce incomplete or inconsistent feedback. This paper presents an LLM-assisted semantic assessment approach for Entity-Relationship diagrams that combines explicit diagram mapping and deterministic difference detection with LLM-based semantic interpretation. The approach generates element-specific feedback and makes identified differences between the task description, the sample solution, and the submitted model transparent. It supports configurable assessment instructions and generates detailed formative feedback. Evaluation on student submissions shows that the assessment approach with explicit diagram mapping and deterministic difference detection produces substantially more complete and correct feedback than direct LLM assessment.