This thesis tackles the problem of offering dependable and scalable support for automated assessment in education by examining the use of Large Language Models (LLMs) in the validation of BPMN (Business Process Model and Notation) exercises. Designing and testing a methodological framework that can produce BPMN models that are structurally compliant and appropriate for methodical evaluation across several quality characteristics is the goal. To provide BPMN-compliant ground realities, a dataset of 24 heterogeneous exercises was created. With the help of an extended taxonomy and specific modelling principles, the approach developed into a two-step pipeline through iterative improvements: organised identification of BPMN elements followed by XML rendering.