IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v14y2024i2p21582440241259012.html
   My bibliography  Save this article

Government-University Relationship in China’s AI Talent Development: A Triple Helix Perspective

Author

Listed:
  • Tao Fu
  • Yonghan Ji

Abstract

Using the Triple Helix model, this study explores the government-university relationship in the context of China’s AI talent development, and their outcomes in terms of AI program deployment, enrollment and faculty. Their interaction may best be summarized as a model of government pull and university response, but with more support and autonomy for the Shuang Yiliu groups. Specifically, the state has maintained a dominant role as a policymaker in promoting the production of AI personnel and showed strong mobilizing abilities to integrate universities into the national AI strategy. Government guidelines outlined the roadmap for training top AI talent with a focus on Shuang Yiliu universities, universities with Shuang Yiliu disciplines, and interdisciplinary graduate students. Universities have responded with quick launch of AI programs, large enrollment and faculty with advanced training and overseas experience. A multi-level AI personnel training system has taken shape. With their privilege in financial and policy support and more autonomy, Shuang Yiliu universities, and universities with Shuang Yiliu disciplines will be the main producers of AI-concentrated graduate students. Theoretical contributions are discussed and policy and practice implications for addressing AI program distributions, talent development and retention, faculty and research, and AI as a discipline provided.

Suggested Citation

  • Tao Fu & Yonghan Ji, 2024. "Government-University Relationship in China’s AI Talent Development: A Triple Helix Perspective," SAGE Open, , vol. 14(2), pages 21582440241, June.
    • Handle: RePEc:sae:sagope:v:14:y:2024:i:2:p:21582440241259012
    DOI: 10.1177/21582440241259012
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/21582440241259012
    Download Restriction: no
    File URL: https://libkey.io/10.1177/21582440241259012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:sagope:v:14:y:2024:i:2:p:21582440241259012. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.