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<p>In the AI era, pre-service teachers’ acceptance of generative AI tools like ChatGPT critically shapes future educational quality. Grounded in the Expectation-Confirmation Theory (ECT), the current study integrates the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to propose a hybrid framework that incorporates Social Influence (SI) and Facilitating Conditions (FC) into post-adoption dynamics. Through a mixed method—surveying 337 Chinese pre-service teachers (analyzed via PLS-SEM) and interviewing 15 participants—we reveal three key findings: (1) SI (β = 0.325, <i>p</i> &lt; .001) and FC (β = 0.294, <i>p</i> &lt; .001) are primary drivers of Sustained Usage Intention (SUI), surpassing traditional ECT variables in explanatory power; (2) Expectation confirmation indirectly strengthens SUI through satisfaction (β = 0.288, <i>p</i> &lt; .001), unveiling a dual cognitive-affective pathway. Theoretically, we advance cross-cultural technology acceptance research by unifying ECT, UTAUT, and TAM. Practically, these insights guide AI tool optimization (e.g., localized interfaces) and policy reforms (e.g., compliant access channels) to bridge innovation and regulatory constraints.</p>

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Exploring factors influencing pre-service teachers’ intention to use ChatGPT

  • 期刊论文

  • 发表日期：

    2025年8月12日

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  • Yang Wang,
  • Tianzhen Pi,
  • Bin Xie

摘要

In the AI era, pre-service teachers’ acceptance of generative AI tools like ChatGPT critically shapes future educational quality. Grounded in the Expectation-Confirmation Theory (ECT), the current study integrates the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to propose a hybrid framework that incorporates Social Influence (SI) and Facilitating Conditions (FC) into post-adoption dynamics. Through a mixed method—surveying 337 Chinese pre-service teachers (analyzed via PLS-SEM) and interviewing 15 participants—we reveal three key findings: (1) SI (β = 0.325, p < .001) and FC (β = 0.294, p < .001) are primary drivers of Sustained Usage Intention (SUI), surpassing traditional ECT variables in explanatory power; (2) Expectation confirmation indirectly strengthens SUI through satisfaction (β = 0.288, p < .001), unveiling a dual cognitive-affective pathway. Theoretically, we advance cross-cultural technology acceptance research by unifying ECT, UTAUT, and TAM. Practically, these insights guide AI tool optimization (e.g., localized interfaces) and policy reforms (e.g., compliant access channels) to bridge innovation and regulatory constraints.