Research on Cultivating the Industrial Ecosystem and Mechanism of Industry Education Integration for AI Plus Lifelong Learning
DOI:
https://doi.org/10.54097/a226k053Keywords:
Artificial Intelligence, Industry Education Integration, Industrial Ecosystem, Educational Digitalization, Lifelong EducationAbstract
Against the backdrop of educational digitalization and the development of new quality productive forces, the in-depth empowerment of lifelong learning by artificial intelligence (AI) has become a core starting point for building a learning society. Nevertheless, the domestic "AI + Lifelong Learning" sector is currently plagued by practical shortcomings including fragmented industrial ecosystems, superficial industry-education integration, and mismatched supply and demand in services for diverse groups. Existing studies fail to deliver an integrated theoretical framework and tiered implementation mechanisms. This study aims to clarify the internal coupling logic between industrial ecosystem cultivation and industry-education integration, and establish a quantifiable and implementable collaborative development system. Supported by industrial ecology, lifelong education theory, the Triple Helix model of collaborative innovation, and the Technology Acceptance Model (TAM), this paper identifies four core dilemmas: isolated industrial participants, homogeneous technologies, shortages of interdisciplinary talents, and the absence of adaptive supporting mechanisms. A four-dimensional path for industrial ecosystem cultivation is constructed, namely "participant collaboration—capacity upgrading—demand matching—environmental optimization". Three differentiated integration mechanisms are designed for senior citizens, working professionals and special groups respectively: university-enterprise-community linkage, enterprise-driven and university-supported cooperation, and public welfare-enabled industry-education synergy. Two sets of quantitative evaluation indicators are also formulated covering ecosystem maturity and industry-education performance. The integrated theoretical model and practical framework developed in this research can serve as a decision-making reference for governments to optimize industrial policies, enterprises to launch intelligent products, and universities to advance reforms in digital-intelligent talent cultivation, thereby facilitating the construction of a lifelong learning system.
Downloads
References
[1] CPC Central Committee. (2024, July 18). Decision of the Central Committee of the Communist Party of China on Further Deepening Reform Comprehensively to Advance Chinese style Modernization [EB/OL]. https://www.gov.cn/ zhengce/ 202407/content_6963770.htm.
[2] CPC Central Committee, & State Council. (2025, February 10). Outline for Building a Powerful Country in Education (2024 2035) [EB/OL]. https://www.gov. cn/gongbao/ 2025/ issue_11846/202502/content_7002799.html.
[3] Zhang, J. X., & Yin, X. S. (2026). The new ecosystem of AI + lifelong learning under human AI collaboration: Operation logic, value framework and evolution path. China Educational Technology, (04), 45 52.
[4] Liu, J., & Zhou, Z. Y. (2025). Evolution and governance path of ubiquitous and accessible lifelong education system driven by intelligent technology. Chinese Vocational and Technical Education, (33), 18 25.
[5] Wang, Z. L. (2024). Construction of new pedagogical system from the perspective of new quality productive forces: Shifting from school centered to lifelong education centered. Open Education Research, 30(03), 15 23.
[6] Shen, Y. R., & Hu, Z. Y. (2024). Two way empowerment between new quality productive forces and in depth industry education integration: Practical dilemmas and practice paths. Chinese University Science & Technology, (05), 78 83.
[7] Liu, J. (2026). Construction of innovative ecosystem for industry education integration in application oriented universities driven by new quality productive forces. Journal of Beijing Union University, 40(02), 92 98.
[8] Ma, L. P., & Zheng, X. R. (2026). Empirical research on user profiling and technology acceptance differences of generative AI based learning. Journal of East China Normal University (Educational Sciences), (01), 78 88.
[9] Wang, C., & Miao, Y. (2025). Driving mechanism and practical bottleneck of industry education integration ecosystem reconstructed by generative AI empowerment. Vocational Education Research, (06), 45 50.
[10] Cai, L. Y. (2024). Development and innovation path of vocational education industry education integration from the perspective of lifelong education. Continuing Education Research, (09), 36 41.
[11] Dede, C. (2026). Learning engineering as a framework for scaling AI enabled lifelong learning. In LERN 2026 Conference Proceedings (pp. 112 128). National AI Institute for Adult Learning and Online Education.
[12] Lee, H., & Kim, J. (2020). The impact of industry academia collaboration on AI and IoT education: A case study from Germany. Journal of Vocational Education & Training, 72.
[13] Zheng, J. H., Shi, C. M., & Cai, X. D. (2025). Lifelong learning of LLM based agents: A roadmap for adaptive adult education systems. IEEE Transactions on Learning Technologies, 18(04), 621 633.
[14] Kim, J., Islam, M., & Goel, A. (2026). Metacognitive correction architecture for LLM agents serving diverse lifelong learners. In AAAI 2026 Workshop on AI for Adult Education (pp. 37 43). AAAI Press.
[15] Etzkowitz, H. (2003). Innovation in innovation: The triple helix of university industry government relations. Social Science Information, 42(3), 293 337. https://doi.org/ 10.1177/ 05390 184030423002.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Education and Educational Research
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
