School leaders’ acceptance and use of artificial intelligence in China

The rapid development of generative AI (GenAI) is transforming learning and teaching. Yet empirical research on school leaders’ acceptance and use of GenAI remains limited. Addressing this gap, this study examines the factors influencing school leaders’ acceptance and use of GenAI applications in Chinese schools using the Unified Theory of Acceptance and Use of Technology (UTAUT2). Drawing on survey data from 636 school leaders in China, the data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings indicate that performance expectancy, effort expectancy, hedonic motivation and habit significantly predict behavioural intention. However, facilitating conditions, habit and behavioural intention did not significantly predict actual use behaviour, revealing an intention–behaviour gap among school leaders in adopting GenAI in school leadership and management. This study contributes to the growing literature on AI and educational leadership by providing empirical evidence on school leaders’ use and acceptance of GenAI applications in school management processes.

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Publication Details

Journal
Educational Studies
Published
2026-07-22
DOI
https://doi.org/10.1080/03055698.2026.2708320
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
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article

School leaders’ acceptance and use of artificial intelligence in China

Donnie Adams, Xueling Feng
Educational Studies
AI in Service Interactions
article

School leaders’ acceptance and use of artificial intelligence in China

Donnie Adams, Xueling Feng
article en

Abstract

The rapid development of generative AI (GenAI) is transforming learning and teaching. Yet empirical research on school leaders’ acceptance and use of GenAI remains limited. Addressing this gap, this study examines the factors influencing school leaders’ acceptance and use of GenAI applications in Chinese schools using the Unified Theory of Acceptance and Use of Technology (UTAUT2). Drawing on survey data from 636 school leaders in China, the data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings indicate that performance expectancy, effort expectancy, hedonic motivation and habit significantly predict behavioural intention. However, facilitating conditions, habit and behavioural intention did not significantly predict actual use behaviour, revealing an intention–behaviour gap among school leaders in adopting GenAI in school leadership and management. This study contributes to the growing literature on AI and educational leadership by providing empirical evidence on school leaders’ use and acceptance of GenAI applications in school management processes.

Educational Studies
The University of Melbourne (AU), University of Malaya (MY), Taylor's University (MY)
Quality Education
Openalex Percentile: Top 6%
AI in Service Interactions
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