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The adoption of technology involves embracing, integrating, and utilizing the latest technological innovations in society. As Artificial Intelligence (AI) increasingly transforms education, this study focuses on examining how AI compatibility affects Kuwaiti students’ willingness to accept AI in the educational context. The Stimulus-Organism-Response (S-O-R) framework and the Artificially Intelligent Device Use Acceptance (AIDUA) framework were integrated and extended with compatibility. A cross-sectional survey was conducted among 465 students from Kuwaiti universities. The study utilized Structural Equation Modeling (SEM) to thoroughly validate both its measurement and structural models. The results indicated that the compatibility of AI applications significantly impacts students’ performance expectancy, effort expectancy, and emotional responses toward accepting these applications. Based on these findings, a set of recommendations was formulated.

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The Influence of Compatibility on the Acceptance of Artificial Intelligence in Kuwaiti Universities

  • 章节

  • 发表日期：

    2025年

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  • Seyed Ghasem Saatchi,
  • Mutaz Khaled Yousef Abdel Wahed,
  • Muhyeeddin Kamel Salman Alqaraleh,
  • Hussam Mohd Al-Shorman,
  • Tawfeeq Alanazi,
  • Sabha Maria Nawaf Alka’awneh,
  • Mowafaq Salem Alzboon,
  • Ala’a M. Al-Momani,
  • Sulieman Ibraheem Shelash,
  • Mazen Alzyoud

摘要

The adoption of technology involves embracing, integrating, and utilizing the latest technological innovations in society. As Artificial Intelligence (AI) increasingly transforms education, this study focuses on examining how AI compatibility affects Kuwaiti students’ willingness to accept AI in the educational context. The Stimulus-Organism-Response (S-O-R) framework and the Artificially Intelligent Device Use Acceptance (AIDUA) framework were integrated and extended with compatibility. A cross-sectional survey was conducted among 465 students from Kuwaiti universities. The study utilized Structural Equation Modeling (SEM) to thoroughly validate both its measurement and structural models. The results indicated that the compatibility of AI applications significantly impacts students’ performance expectancy, effort expectancy, and emotional responses toward accepting these applications. Based on these findings, a set of recommendations was formulated.