Research on the Alignment between the Cultivation of Application-oriented Talents and Local Economic Needs in the Age of Technological Disruption

Shengxiu Yang

doi:10.62051/ijgem.v8n1.01

Authors

Shengxiu Yang

DOI:

https://doi.org/10.62051/ijgem.v8n1.01

Keywords:

Technological disruption, Application-oriented talents, Talent fit, Regional economy

Abstract

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.

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References

[1] Huang Bin, Chen Xiaofei. Research on the Half-Life of Applied Talent Capabilities under the Compression of Technological Iteration Cycles [J]. Educational Research, 2023(5): 112-125.

[2] Zhang Wei, Li Liguo. Analysis of the Evolution of Technology Integration Capability Requirements Based on Recruitment Text Mining [J]. Journal of Higher Engineering Education Research, 2024(2): 45-58.

[3] Gu Yong'an, et al. Innovation of dynamic adjustment mechanism for professional clusters from the perspective of industry-education integration [J]. Higher Education Research in China, 2021(8): 78-84.

[4] Chen Jiefang, Wang Boqing. Empirical Test of the OBE Model in Shortening the Job Adaptation Period [J]. Journal of Higher Engineering Education Research, 2022(3): 93-102.

[5] Liu Zhentian. Reconstruction of the Employment Quality Evaluation System in Colleges and Universities in the Era of Technological Disruption [J]. Educational Research, 2023(11): 56-69.

[6] Wang Shibin, et al. Spatial and temporal characteristics of regional industrial upgrading and the matching degree between universities and majors [J]. Tsinghua University Education Research, 2020(4): 34-47.

[7] Zhou Haitao, et al. Research on the Influencing Factors of the Technology Achievement Transformation Rate in Modern Industrial Colleges [J]. Journal of Education Development Research, 2023(18): 23-35.

[8] Li Zhimin, et al. The Enlightenment of Germany's "Dual System 4.0" to Chinese Application-oriented Universities [J]. Comparative Education Research, 2024(1): 88-97.

[9] TEECE D J. Dynamic Capabilities for Education 4.0 [J]. Research Policy, 2020, 49(7): 103-115.

[10] BRYNJOLFSSON E, MCAFEE A. The New Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies [M]. MIT Press, 2023.

[11] SCHWAB K. The Fourth Industrial Revolution: What It Means and How to Respond [J]. Foreign Affairs, 2022, 101(3): 28-40.

[12] FREEMAN C, HUANG Y. Collaboration in the Age of AI: University-Industry Linkages in Emerging Technologies [J]. Technological Forecasting & Social Change, 2024, 198: 1-12.

[13] CARNEVALE A P, et al. The Growing Demand for Hybrid Jobs in the Digital Economy [R]. Georgetown University, 2023.

[14] OECD. Micro-credentials in Higher Education: Bridging Skills Gaps [Z]. OECD Education Policy Perspectives, 2024.

[15] UNESCO. AI and Education: Guidance for Policy-makers [R]. Paris: UNESCO Publishing, 2023.

[16] Etzkowitz H., Leydesdorff L. The Triple Helix: University-Industry-Government Innovation in Action [M]. Routledge, 2000.

[17] Gu Yong'an. Construction of application-oriented undergraduate professional clusters: logic, mechanism, and path [J]. Higher Education Research in China, 2020(11): 78-84.

[18] World Economic Forum. The Future of Jobs Report 2023 [R]. Geneva, 2023.

[19] Li Shi, et al. Research on the Impact of Artificial Intelligence on China's Labor Market [J]. Economic Research, 2022(5): 45-60.

[20] Shandong Provincial Department of Education. Shandong Provincial Regulations on Promoting Industry-Education Integration [Z]. 2023.

[21] Weifang Bureau of Statistics. Statistical Communique on the National Economic and Social Development of Weifang City in 2023 [Z]. 2024.