中国人工智能教育政策扩散的时空特征及影响因素——基于省级数据的事件史分析
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燕山大学 公共管理学院

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河北省高等学校科学研究专项任务(基础研究重点培育)项目“中国高校办学自主权政策变迁的典型特征、制度逻辑与优化策略研究”(JCZX2024019)。


The spatio-temporal characteristics and influencing factors of the Diffusion of artificial intelligence education policies in China -- Event history analysis based on provincial data
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School of Public Administration,Yanshan University

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    摘要:

    近年来中国政府出台多项政策推动人工教育发展,研究中国人工智能教育政策扩散规律,能够为各级政府优化政策提供理论依据。基于省级政府样本数据并采用事件史分析方法,对中国人工智能教育政策扩散时空特征及影响因素进行实证研究。研究发现:中国人工智能教育政策扩散在时间上呈现出“先快速,后缓慢”的非渐进性特征,在空间上呈现出“整体推进,跟进扩散”特征,且邻近效应显著。在影响因素方面,外部影响因素中上级政府压力和相邻政府压力是各省级政府采纳政策的关键因素。内部影响因素中省委书记年龄得到部分支持。基于此,提出应该充分发挥央地政策协同效应、形成良性省际竞合关系、强化领导干部引领作用、加强研究明晰发展思路四方面优化对策,为省域优化人工智能教育政策提供参考。

    Abstract:

    China has issued many policies to promote artificial intelligence education in recent years, studying the rules of the diffusion of artificial intelligence education policies among provincial-level governments can provide valuable references for governments at all levels to advance the implementation of these policies. Based on provincial-level government sample data and using event history analysis, this study empirically investigates the spatio-temporal characteristics and influencing factors of the diffusion of AI education policies in China. The findings reveal that the diffusion of AI education policies in China exhibits a "fast initially and then slow" non-gradual characteristic over time, and a pattern of "overall advancement and follow-up diffusion" in space, with a significant proximity effect. In terms of influencing factors, external factors pressure from higher-level governments and pressure from neighboring governments are key drivers of policy adoption by provincial governments. Among internal factors, the age of provincial party secretaries received partial support, indicating that it also influences policy adoption. Based on these findings, four optimization strategies are proposed: giving full play to the synergistic effect of central and local policies, fostering healthy inter-provincial competition and cooperation, strengthening the leadership and support of leading officials, and enhancing research to clarify development pathways. These recommendations aim to provide insights for provincial-level governments in optimizing AI education policies.

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  • 收稿日期:2025-11-29
  • 最后修改日期:2026-01-25
  • 录用日期:2026-03-30
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