This paper reflects an experimental approach to employ LLM technology through sophisticated crafting of machine instructions in order to evaluate data from machine learning algorithms and work out intersections suitable for automated grading frameworks. It can be seen as a demonstration of LLM capability in identifying cross-disciplinary data and bridging suitable datasets for further implementation in a different scientific field. The author aims to demonstrate how, from the perspective of educational science, Artificial Intelligence can be harnessed to create frameworks for grading automation, as an exemplary showcase for the overall usability of advanced machines in educational science.