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International Journal of New Developments in Education, 2025, 7(11); doi: 10.25236/IJNDE.2025.071120.

High-Quality Development Strategies for New Productivity under the Background of Industry-Education Integration

Author(s)

Xin Sun, Peng Chen, Qi Wu, Dan Zhao

Corresponding Author:
Xin Sun
Affiliation(s)

Electrical Engineering, Shenyang Polytechnic College, Shenyang, Liaoning, China

Abstract

Concerns have been expressed about graduates' inadequate skills in the workplace and the gap between talent development and enterprise technology deployment. This article suggests an integrated industry-academia-research strategy based on "new high-quality productivity" to address these problems by closely integrating high technologies with vocational education, including artificial intelligence, big data, cloud computing, and RPA. Its objectives are to improve graduates' professional skills, increase corporate efficiency and technological innovation capabilities, and optimize the system for cultivating talent in higher education. This strategy accomplishes this purpose by fostering integrated education initiatives and industry-academia-research joint training projects, as well as by building a multi-level collaborative education system among universities, businesses, and research organizations. Students' learning outcomes, skills, and capacities have all improved, according to experimental studies. By enhancing students' professional skills and businesses' technology application capabilities, this new high-quality productivity model that integrates industry, academia, and research may support effective and superior industrial development.

Keywords

New Quality Productivity; Industry-Education Integration; Higher Vocational Education; Artificial Intelligence; Big Data

Cite This Paper

Xin Sun, Peng Chen, Qi Wu, Dan Zhao. High-Quality Development Strategies for New Productivity under the Background of Industry-Education Integration. International Journal of New Developments in Education (2025), Vol. 7, Issue 11: 129-135. https://doi.org/10.25236/IJNDE.2025.071120.

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