(376a) Combining Ontologies and Llms to Evaluate and Improve Engineering Curricula

In the contemporary educational landscape, the evaluation and enhancement of engineering curricula are essential for ensuring the alignment with evolving industry demands, educational standards and the rapidly evolving technologies. This research paper proposes a novel approach that integrates ontologies and Large Language Models (LLM) to advance the evaluation and improvement process. Leveraging ontologies facilitates the representation of domain knowledge and curriculum structures, while LLMs provide the capability to analyze textual data comprehensively and leverage the wide context to infer improvements and identify gaps in learning outcomes and obtained skills. By combining these two methodologies, our approach enables a deeper understanding of the relationships between curriculum components, learning outcomes, and industry requirements. We demonstrate the efficacy of our approach in identifying areas for improvement, optimizing course structures, and enhancing the overall quality of engineering education using an ontology for Chemical Engineering curriculum. This research contributes to the advancement of curriculum evaluation methodologies, providing educators and stakeholders with valuable insights for continuous improvement in engineering education.