A Dynamic Modeling Framework for Assessing the Impact of Generative AI on Occupational Evolution and Curriculum Adaptation

Abstract

The rapid development of generative artificial intelligence Gen-AI can replace practitioners to complete a large number of repetitive cognitive work, can also help improve decision-making efficiency, and can also build a new model of human and AI collaboration, from multiple dimensions to the existing professional system to bring changes. This article wants to clarify the various effects of this technology, and specially built a dynamic model that reflects the correspondence between career change and higher education adjustment. This article from the O*NET database to collect relevant data, combined with the Dutch occupation code, entropy weight calculation method and K-means clustering algorithm, screened out a number of representative occupations, including bioinformatics, film and video editing, as well as telecommunications line installers and maintenance personnel, just to correspond to the science and technology, art and skills practice of the three different career directions. This paper uses the Bass diffusion model to simulate the penetration process of Gen-AI in different industries, and also uses the Evolution-Vector-Dynamics model to disassemble labor processes into four different types: conventional tasks, cognitive tasks, physical tasks, and sudden-like tasks. From the final calculation results, the market demand for science and technology occupations will follow up, art occupations may face relatively large structural substitution pressure, skills practice occupations will basically remain stable, because a large number of physical operations in this type of occupation can not be replaced by technology for the time being. Combined with the conclusions of these studies, this paper proposes that the existing skills training direction can be adjusted to add artificial intelligence verification, algorithm audit, intellectual property-related training, and AI-assisted infrastructure operations to the training system. This research framework built in this paper has a relatively strong practicality, which can be used to evaluate the career changes brought about by the development of artificial intelligence, and can also provide a clear direction reference for subsequent course adjustments.