This master’s thesis aims to assess the feasibility of utilizing Large Language Models (LLMs) to generate Unified Modeling Language (UML) Class Diagrams. UML is a standardized visual language widely used in software engineering to depict the structure and design of a software system, enabling clear communication and documentation of system components and their relationships. By comparing the LLM-generated diagrams with those produced by humans, particularly during the crucial initial phase of requirement gathering, the research assesses whether AI can support or enhance traditional software modeling practices. The study utilizes a two-part methodology complemented by a statistical analysis. In the first part, the diagrams are examined for syntactic (adherence to UML rules), semantic (accuracy of meaning and concepts), and pragmatic (usefulness and applicability) quality errors, in the second part the semantic distance between the generated diagrams and the given solutions is calculated by using a specific algorithm.