Against the backdrop of disruptive technologies such as artificial intelligence, the Internet of Things, and big data reshaping the global industrial landscape, applied universities, as the main providers of regional talent, have seen their dynamic adaptability of their training systems to local economic needs become a core variable affecting regional competitiveness. This study takes Weifang University of Science and Technology in Shandong Province as a typical case, systematically deconstructing the matching mechanism between the employment trends of its graduates from 2020 to 2024 and the talent needs of Weifang's three leading industries: "high-end equipment manufacturing, modern agriculture, and new-generation information technology" through a mixed research method. The empirical analysis reveals that the professional settings lag behind the technology iteration cycle by approximately 2.3 years, and there is a significant gap between the skill structure of graduates and the requirements of industrial upgrading. University-enterprise cooperation exhibits a characteristic of "shallow internship-led" cooperation. Based on this, an optimization path for constructing a "technology-responsive" talent training system is proposed: establishing a dynamic professional adjustment mechanism driven by industrial technology early warning, creating a "technology co-creation" platform for deep industry-education integration, reconstructing an "OBE-real problem-oriented" curriculum system, and supporting policy guarantee measures. The research findings provide a theoretical model and practical paradigm for the transformation of regional applied universities in the context of technological change, offering significant reference value for promoting high-quality development of the local economy.
Shing-Ru Yang (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: