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<p>The successful integration of Generative AI (GenAI) tools in K-12 education is ultimately contingent upon teacher acceptance. This study investigates the factors influencing K-12 teachers’ willingness to use GenAI by proposing and testing an integrated theoretical framework that combines the Technology Acceptance Model (TAM) with Perceived Risk Theory. Data were collected using a quantitative survey methodology from 221 in-service teachers in China. The results revealed that while teachers generally hold positive attitudes, their behavioral intention is shaped by a critical trade-off. Tool Performance Evaluation emerged as the strongest positive predictor of usage intention, followed by External Factors. Crucially, and in support of our integrated model, Perceived Risk was a significant negative predictor. The findings also indicate that younger and less experienced teachers exhibit higher acceptance levels. This study concludes that a multi-faceted approach is essential for promoting Generative AI adoption: developers must prioritize tool reliability and ease of use, educational institutions must provide robust training and support, and proactive measures must be taken to mitigate teachers’ legitimate concerns about risk. The research validates a comprehensive theoretical model for understanding technology adoption in education and provides actionable insights for practitioners and policymakers.</p>

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Understanding K-12 teachers’ adoption of generative AI through an integrated technology acceptance and perceived risk framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月18日

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  • Jie Zhao,
  • Ximei Yang

摘要

The successful integration of Generative AI (GenAI) tools in K-12 education is ultimately contingent upon teacher acceptance. This study investigates the factors influencing K-12 teachers’ willingness to use GenAI by proposing and testing an integrated theoretical framework that combines the Technology Acceptance Model (TAM) with Perceived Risk Theory. Data were collected using a quantitative survey methodology from 221 in-service teachers in China. The results revealed that while teachers generally hold positive attitudes, their behavioral intention is shaped by a critical trade-off. Tool Performance Evaluation emerged as the strongest positive predictor of usage intention, followed by External Factors. Crucially, and in support of our integrated model, Perceived Risk was a significant negative predictor. The findings also indicate that younger and less experienced teachers exhibit higher acceptance levels. This study concludes that a multi-faceted approach is essential for promoting Generative AI adoption: developers must prioritize tool reliability and ease of use, educational institutions must provide robust training and support, and proactive measures must be taken to mitigate teachers’ legitimate concerns about risk. The research validates a comprehensive theoretical model for understanding technology adoption in education and provides actionable insights for practitioners and policymakers.