Jie Huang (Author)
School of Package Engineering, Hunan University of Technology, China
Jinhua Tan (Primary Contact)
School of Package Engineering, Hunan University of Technology, China
Yiwu Liu (Author)
School of Package Engineering, Hunan University of Technology, China
Yue Chen (Author)
School of Package Engineering, Hunan University of Technology, China
Fude Lu (Author)
School of Package Engineering, Hunan University of Technology, China
Haiyang Liao (Author)
School of Mechanical Engineering, Hunan University of Technology, China
Artificial intelligence; Full supply chain; Integration of industry and education; Packaging engineering major; Talent training
Hunan Provincial Research Project on Degree and Graduate Education Teaching Reform (2025JGYB326); Hunan Provincial General Higher Education Teaching Reform Research Project (202502002052); Key Project of Education and Teaching Reform Research in General Colleges and Universities in Hunan Province (HNJG-20230724); Hunan University of Technology Degree and Postgraduate Education Teaching Reform Research Project (JGYB25011); Hunan University of Technology Teaching Reform Project (SJG-0125212).
30-11-2025
In the current practice of industry–education integration for talent cultivation, the Packaging Engineering major faces persistent challenges, including limited integration mechanisms, insufficient depth of collaboration, and a narrow scope of engagement. These issues constrain the development of high-quality undergraduate talent. To address these limitations, this study takes the Packaging Engineering program at Hunan University of Technology as a representative case and adopts a comprehensive case study approach. Data were collected through questionnaires, in-depth interviews, document analysis, and learning platform log mining. By employing analytical techniques such as descriptive statistics and thematic clustering, the study systematically examines the present status and underlying challenges of industry–education integration in the program. Building upon these findings, the study proposes an AI-empowered “full-chain” industry–education integration framework that holistically connects the industry chain, education chain, innovation chain, and talent chain. This framework leverages artificial intelligence to achieve precise alignment between educational processes and industrial demands, facilitate deep knowledge reconstruction, and foster the co-evolution of the university–industry ecosystem, thereby realizing comprehensive and synergistic integration. Furthermore, the research delineates the implementation pathway of the AI-enabled “full-chain” model from three dimensions: demand-driven design, resource integration, and process implementation. Empirical results indicate that the proposed model has yielded significant improvements in the Packaging Engineering talent cultivation process—manifested in enhanced quality and frequency of university–enterprise collaboration, improved graduate employment outcomes, and sustained student satisfaction and recognition. This study contributes a replicable and scalable paradigm for AI-supported “full-chain” industry–education integration, offering valuable insights for reforming talent cultivation models in Packaging Engineering and related engineering disciplines.
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