China’s Policy on AI Talent Cultivation and Its Impact Analysis: Applying Smith’s Policy Implementation Model

[Purpose] The purpose of this study was to analyze how the Chinese government's artificial intelligence (AI) talent development policies are being implemented in actual educational and research settings, and to identify the structural limitations and outcomes that emerge in the process. To better understand the context of policy implementation, Thomas B. Smith’s policy implementation model was adopted as the theoretical framework. [Methods] To achieve this purpose, the study conducted a document analysis focusing on three key Chinese policy documents related to AI talent development: the Next Generation Artificial Intelligence Development Plan (2017), the AI Innovation Action Plan for Universities (2018), and the Guiding Plan for the Training of Graduate Students in the Field of Artificial Intelligence (Trial) (2022). The analysis examined structural problems in policy implementation based on the interactions among policy goals, implementers, target groups, and the external environment. [Results] The main findings are as follows. First, while China’s AI talent policies have achieved a certain degree of quantitative expansion, they lack qualitative depth and show significant regional disparities in implementation capacity. Second, the diversity of policy recipients has led to inconsistent levels of policy acceptance, and there exists a gap between policy goals and the actual needs of industry and education sectors. Third, a lack of coordination among implementers, a mismatch between the expectations of target groups, and delayed responses to changes in the external environment have been identified as key factors hindering effective policy implementation. [Conclusion] From the perspective of Korean education, this study suggests that to transition China’s AI talent policies toward a quality-centered implementation system, it is essential to ensure policy coherence, strengthen the capacity of implementing organizations, institutionalize stakeholder participation, and improve responsiveness to environmental changes. This study provides both theoretical and practical foundations for the successful integration of AI talent development as a national strategic policy into the education system.