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<p>This study focuses on the intention of Chinese college students to use Artificial Intelligence Generated Content (AIGC), aiming to explore the influencing factors and their mechanisms of action and to provide theoretical and practical guidance for the application of AIGC in higher education. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, diffusion of innovations theory, and perceived risk theory, an analytical framework is constructed. A questionnaire survey was conducted, collecting 415 valid samples, and structural equation modeling (SEM) was used for empirical analysis. The study found that performance expectancy, effort expectancy, social influence, facilitating conditions, satisfaction, and relative advantage positively affect the intention to use; relative advantage and satisfaction act as mediators between multiple factors and the intention to use, with the mediating effect of relative advantage being more pronounced; chain mediation effect analysis shows that facilitating conditions play a stronger mediating role in enhancing the intention to use AIGC; perceived risk negatively moderates the relationship between relative advantage and the intention to use. The results of the study provide recommendations for higher education institutions and technology developers.</p>

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Factors influencing Chinese college students’ intention to use AIGC: a study based on the UTAUT model

  • 期刊论文

  • 发表日期：

    2025年4月1日

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  • Kai Guo,
  • Chengyuan Zhan,
  • Xiang Li

摘要

This study focuses on the intention of Chinese college students to use Artificial Intelligence Generated Content (AIGC), aiming to explore the influencing factors and their mechanisms of action and to provide theoretical and practical guidance for the application of AIGC in higher education. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, diffusion of innovations theory, and perceived risk theory, an analytical framework is constructed. A questionnaire survey was conducted, collecting 415 valid samples, and structural equation modeling (SEM) was used for empirical analysis. The study found that performance expectancy, effort expectancy, social influence, facilitating conditions, satisfaction, and relative advantage positively affect the intention to use; relative advantage and satisfaction act as mediators between multiple factors and the intention to use, with the mediating effect of relative advantage being more pronounced; chain mediation effect analysis shows that facilitating conditions play a stronger mediating role in enhancing the intention to use AIGC; perceived risk negatively moderates the relationship between relative advantage and the intention to use. The results of the study provide recommendations for higher education institutions and technology developers.