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<p>Generative Artificial Intelligence (AI) is steadily gaining prominence in higher education and brings about huge impact on college students’ daily life. However, limited studies paid attention to college students’ use behavior of generative AI and its influencing factors. The study aimed to explore this issue by adopting an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. Two generative AI related variables, named “novelty value” and “perceived humanness,” were added to the UTAUT model, and the potential moderating effects of gender, grade and major were also considered in the model. The model was validated by collecting data from 1,190 college students at a Chinese university. Results indicated significant positive correlations among performance expectancy, effort expectancy, novelty value, social influence, facilitating conditions, and behavioral intention. Additionally, facilitating conditions and behavioral intention significantly influenced use behavior, while perceived humanness had no significant impact on behavioral intention. Moderating variables like gender and grade significantly affected the acceptance of generative AI, whereas major did not. The findings provided nuanced insights to advance the practical application of generative AI. Considering limited sample in the study, future research may encompass diverse demographics across multiple countries to enable cross-cultural comparisons.</p>

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College students’ use behavior of generative AI and its influencing factors under the unified theory of acceptance and use of technology model

  • 期刊论文

  • 发表日期：

    2025年4月21日

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  • Jie Xu,
  • Yan Li,
  • Rustam Shadiev,
  • Cuixin Li

摘要

Generative Artificial Intelligence (AI) is steadily gaining prominence in higher education and brings about huge impact on college students’ daily life. However, limited studies paid attention to college students’ use behavior of generative AI and its influencing factors. The study aimed to explore this issue by adopting an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. Two generative AI related variables, named “novelty value” and “perceived humanness,” were added to the UTAUT model, and the potential moderating effects of gender, grade and major were also considered in the model. The model was validated by collecting data from 1,190 college students at a Chinese university. Results indicated significant positive correlations among performance expectancy, effort expectancy, novelty value, social influence, facilitating conditions, and behavioral intention. Additionally, facilitating conditions and behavioral intention significantly influenced use behavior, while perceived humanness had no significant impact on behavioral intention. Moderating variables like gender and grade significantly affected the acceptance of generative AI, whereas major did not. The findings provided nuanced insights to advance the practical application of generative AI. Considering limited sample in the study, future research may encompass diverse demographics across multiple countries to enable cross-cultural comparisons.