Analysis of the Promotion Effect of Vocational Undergraduate Innovative Practice Teaching on New Quality Productivity under Artificial Intelligence-Enabled Industry-Teaching Integration Model

Authors

  • Cheng’en Pan Business & Tourism Institute, Hangzhou Polytechnic University, Hangzhou, Zhejiang, 310018, China

DOI:

https://doi.org/10.70917/ijcisim-2025-0238

Keywords:

new quality productivity; kernel density estimation; regional differences; entropy weight method

Abstract

Based on the background of AI-enabled industry-education integration, this paper investigates the promotion impact between vocational undergraduate innovative practice teaching and new quality productivity. Firstly, the evaluation index system of undergraduate innovative practice teaching and new quality productivity is constructed, the entropy value method is used to comprehensively evaluate the development level of new quality productivity in 30 provinces in China from 2014 to 2024, and the kernel density estimation is used to measure the regional differences in the level of undergraduate innovative practice teaching. Finally, the quantile regression model was used to regress the three dimensions of undergraduate innovative practice teaching and new quality productivity. The results show that the regional balance of undergraduate innovative practice teaching development is enhanced, the mean value of undergraduate innovative practice teaching in central region is 0.2671 in 2014-2024, the lowest level of development, and the differences in western provinces tend to expand and polarization phenomenon occurs; the development of new-quality productivity in high-level counties has the most significant spatial spillover effect; under quantile regression , the promotion effect of undergraduate innovative practice teaching on the new quality productivity shows a U-shaped change.

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Published

2025-12-22

How to Cite

Cheng’en Pan. (2025). Analysis of the Promotion Effect of Vocational Undergraduate Innovative Practice Teaching on New Quality Productivity under Artificial Intelligence-Enabled Industry-Teaching Integration Model. International Journal of Computer Information Systems and Industrial Management Applications, 17, 16. https://doi.org/10.70917/ijcisim-2025-0238

Issue

Section

Original Articles