Enhancing ESCO with Generative AI: A Dynamic Approach to Supporting 21^st Century Education

In the rapidly evolving landscape of engineering education, upskilling and lifelong learning have become critical to maintaining competitiveness and fostering innovation. The use of ontologies, such as the European Skills, Competences, Qualifications, and Occupations (ESCO), plays a crucial role in organizing and managing the skills required for modern engineering roles. However, the slow pace of ontology updates and the lack of contextual adaptability present significant challenges, leading to outdated and irrelevant information for educators, learners, and industry professionals. This paper explores the potential of integrating Large Language Models (LLMs) with knowledge engineering to accelerate the process of updating ontologies like ESCO. By dynamically analyzing data and incor-porating contextual information, LLMs offer promising avenues for enhancing the evolution and precision of these ontologies. We discuss the potential impact of this approach in engineering education, particularly in aligning ups killing and reskilling efforts with the demands of emerging technologies such as AI -driven automation and digital engineering. This paper aims to highlight how LLMs can support the creation of more responsive, context-aware learning frameworks, ultimately sustaining educational ex-cellence and fostering critical thinking in engineering education.