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AI-driven innovation in educational management: A multi-case study of Chinese higher education institutions

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  • Wei Zhang
  • Dzintra. Ilisko

Abstract

This study examines the implementation and impacts of AI-driven innovations in Chinese higher education management, focusing on how technological readiness and organizational learning capacity affect implementation outcomes. A multi-case analysis of 35 higher education institutions in the Yangtze River Delta region utilizes data from 847 survey responses from administrators, faculty, and students. Results reveal an asymmetry between technological readiness (β = 0.341, p < 0.01) and organizational learning capacity (β = 0.254, p < 0.01) on implementation success. Threshold effects were identified for both dimensions, with medium-scale institutions showing optimal implementation performance. Temporal analyses indicate that while technological readiness yields immediate benefits, organizational learning capacity delivers stronger long-term effects. Institutions should adopt a sequenced approach to AI implementation, prioritizing technological infrastructure before organizational capability building, with formal knowledge management systems significantly enhancing success rates. Successful AI implementation in educational management requires balancing technical infrastructure with organizational learning capabilities, recognizing threshold effects and institutional context variations that inform both theoretical frameworks and practical implementation strategies.

Suggested Citation

  • Wei Zhang & Dzintra. Ilisko, 2025. "AI-driven innovation in educational management: A multi-case study of Chinese higher education institutions," Edelweiss Applied Science and Technology, Learning Gate, vol. 9(4), pages 2109-2128.
    • Handle: RePEc:ajp:edwast:v:9:y:2025:i:4:p:2109-2128:id:6498
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