Dynamic Policy Evolution of Artificial Intelligence Education in Vocational Colleges Using a Cooper-Based System Dynamics Modeling Framework

Abstract

The speedy development of AI technology is changing the needs of employment; hence AI education is a strategic choice for vocational colleges. Nevertheless, the current educational policies are drafted in silos and lack a dynamic tracking mechanism which is to track the impact of curriculum reform, faculty capability, infrastructure investment, industry collaboration and student employability. The above limitations hamper the sustainable development of AI education and obstruct the efficient implementation of long-term policies. In response to the above-mentioned challenge, this paper proposes a dynamic policy evolution framework for AI education in vocational colleges based on the Cooper system dynamics model. The framework combine Cooper’s policy cycle with system dynamics to build causal loop diagrams and stock-and-flow models embedded with the complexity and feedbacks of AI educational ecosystems. Using long-lived data taken from the vocational institutions, we model five interconnected subsystems: educational resources, faculty development, curriculum innovation, industry partnerships, and graduate employment. A variety of policy scenarios, exploring funding allocation, teacher training, curriculum updating, and enterprise collaboration, are simulated over a 10-year planning horizon to evaluate policy impact and sustainability. Based on the simulation results, the coordinated policy measures are found to be significantly better than the isolated measures. The coordinated policy measures would lead to a significant improvement in AI curriculum adopting, formation of competent faculty, industry engagement, graduate employment, and the innovation capacity of the institution. Sensitivity analysis also shows that faculty development and industry collaboration are the most important drivers of long-term success of the policy. Through the use of the proposed framework, policymakers and educational administrators will be able to optimize AI education strategies and create robust decision-support systems. Ultimately leading to evidence-based policy formulation, resource allocation, and sustainable talent development for intelligent industries.