Proceedings of the 2025 5th International Conference on Culture, Design and Social Development (CDSD 2025)

Barriers and Pathways for Industry-University-Research (IUR) Models in Vocational Education under the AI Era: Toward Effective School–Enterprise Collaborative Training

Authors
Ziyu Lu1, *
1School of Civil and Transportation Engineering, Southeast University Chengxian College, Nanjing, Jiangsu, 210000, China
*Corresponding author. Email: 220423112@cxxy.seu.edu.cn
Corresponding Author
Ziyu Lu
Available Online 26 February 2026.
DOI
10.2991/978-2-38476-541-6_100How to use a DOI?
Keywords
Vocational Education; Industry-University-Research Collaboration; Artificial Intelligence; Talent Development; Competency-Based Training
Abstract

In the context of vocational education reform, the collaborative model of industry-academia-research has gradually become an important pathway for talent cultivation. This model integrates multiple advantages, achieving an organic combination of theory and practice, thereby enhancing the relevance and effectiveness of vocational education talent training. This article focuses on the practical methods and developmental issues related to the synergy mechanism between production, education, and research in vocational education. It first outlines the logic of cooperation among production, education, and research, emphasizing its value in various aspects. By conducting a comparative analysis of three classic models, the article reveals the advantages and disadvantages of different models in curriculum design, practice orientation, and depth of collaboration, particularly highlighting the importance of achieving an integrated approach of “curriculum-teaching-practice” for enhancing synergy. Furthermore, the article discusses the role of deep corporate involvement in curriculum updates, teaching reforms, and diversified evaluations, positing that such engagement can effectively enhance students’ adaptability and improve practical skills. It also identifies challenges posed by rapid technological advancements, inadequate evaluation systems, and insufficient school-enterprise collaboration. Finally, the article proposes several actionable solutions: improving the support system, expanding diversified curricula, and further enhancing teachers’ digital literacy.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 5th International Conference on Culture, Design and Social Development (CDSD 2025)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
26 February 2026
ISBN
978-2-38476-541-6
ISSN
2352-5398
DOI
10.2991/978-2-38476-541-6_100How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Ziyu Lu
PY  - 2026
DA  - 2026/02/26
TI  - Barriers and Pathways for Industry-University-Research (IUR) Models in Vocational Education under the AI Era: Toward Effective School–Enterprise Collaborative Training
BT  - Proceedings of the 2025 5th International Conference on Culture, Design and Social Development (CDSD 2025)
PB  - Atlantis Press
SP  - 906
EP  - 915
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-541-6_100
DO  - 10.2991/978-2-38476-541-6_100
ID  - Lu2026
ER  -