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<p>Generative Artificial Intelligence (GAI) has gained substantial traction in recent times and demonstrates a wide range of applications across multiple sectors. GAI has triggered technological disruptions in various domains. This study aims to explore the classroom adoption of Generative Artificial Intelligence (GAI) among educators and students. The aim is to identify the determinants of the adoption of GAI tools in learning systems. The study integrates different factors—such as perceived enjoyment, social influence, and perceived risk—into the well-established Technology Acceptance Model (TAM), comprising perceived ease of use, perceived usefulness, and behavioural intention. A structured survey measure was constructed, and data gathered among 254 participants were assessed. The hypothesized model was confirmed based on Partial Least Squares Structural Equation Modelling (PLS-SEM). The results offer useful insights into the pros and cons related to the adoption of GAI tools in educational design, where all hypothesized relationships were empirically confirmed. In addition, the proposed acceptance model offers a foundation for future research and has the potential to make significant contributions towards the development and application of more accessible and efficient GAI technologies in education.</p>

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The acceptance of generative artificial intelligence technology: an empirical study by applying structural equation modelling

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Anil Singh Parihar,
  • Harendra Singh,
  • Vikrant Vikram Singh,
  • Aditya Kumar Gupta,
  • P. K. Kapur,
  • Anoop Kumar

摘要

Generative Artificial Intelligence (GAI) has gained substantial traction in recent times and demonstrates a wide range of applications across multiple sectors. GAI has triggered technological disruptions in various domains. This study aims to explore the classroom adoption of Generative Artificial Intelligence (GAI) among educators and students. The aim is to identify the determinants of the adoption of GAI tools in learning systems. The study integrates different factors—such as perceived enjoyment, social influence, and perceived risk—into the well-established Technology Acceptance Model (TAM), comprising perceived ease of use, perceived usefulness, and behavioural intention. A structured survey measure was constructed, and data gathered among 254 participants were assessed. The hypothesized model was confirmed based on Partial Least Squares Structural Equation Modelling (PLS-SEM). The results offer useful insights into the pros and cons related to the adoption of GAI tools in educational design, where all hypothesized relationships were empirically confirmed. In addition, the proposed acceptance model offers a foundation for future research and has the potential to make significant contributions towards the development and application of more accessible and efficient GAI technologies in education.