The impact of coercive, normative, and mimetic Stress on Chinese teachers' continuance intention to use generative AI: An integrated perspective of the Expectation-Confirmation Model and Institutional Theory
Kunjie Jia, Kai Cui, Huimin He, Yiran Du
TLDR
This study reveals how individual satisfaction and institutional pressures influence Chinese teachers' continued use of generative AI in education.
Key contributions
- Integrates Expectation-Confirmation Model and Institutional Theory to study AI adoption.
- Identifies individual factors (satisfaction, usefulness) and institutional pressures as key drivers.
- Reveals Chinese teachers use generative AI pragmatically for tasks but critically evaluate content.
- Employs a mixed-methods design with surveys (437 teachers) and interviews (15 teachers).
Why it matters
This paper provides a comprehensive understanding of factors influencing teachers' sustained engagement with generative AI, combining individual and institutional perspectives. It offers valuable insights for policymakers and educators developing AI integration strategies in educational settings, particularly in China.
Original Abstract
This study investigates Chinese teachers' continuance intention to use generative artificial intelligence (AI) by integrating the Expectation-Confirmation Model with Institutional Theory. A sequential explanatory mixed-methods design was employed. Questionnaire data from 437 teachers were analysed using structural equation modelling, followed by semi-structured interviews with 15 teachers to further interpret the findings. The results indicate that confirmation, perceived usefulness, and satisfaction play important roles in shaping teachers' continuance intention, while institutional pressures, including coercive, normative, and mimetic influences, also contribute to continued use. Qualitative findings further reveal that teachers often use generative AI pragmatically to support tasks such as lesson preparation and idea generation, while simultaneously exercising caution and critically evaluating the reliability of AI-generated content. These findings highlight the combined influence of individual evaluations and institutional contexts on teachers' sustained engagement with generative AI in education.
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