The increasing use of large language models (LLMs) in software engineering has opened up new avenues for automating complex modeling tasks, particularly in transforming natural language specifications into formal representations. This thesis studies the adaptive control of UML Use Case Diagram (UCD) exercises using LLMs, such as ChatGpt and DeepSeek, to generate analogous solutions and evaluate student tasks. A set of 23 manually selected exercises, each with human-written reference solutions in visual and JSON formats, serves as the basis for the empirical investigation. Using prompt engineering methodologies (role-based and iterative prompts), LLMs were prompted to generate UCDs, which were evaluated using a multifaceted framework in relation to syntactic correctness, semantic equivalence and pragmatic usefulness.