Scholarlink

<p>In the rapidly evolving landscape of artificial intelligence (AI), understanding the factors that influence individuals’ intentions to adopt AI technologies is crucial, particularly within educational contexts. This study addresses a critical gap in the literature by examining how AI literacy interacts with the constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT)—Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions—to shape university students’ behavioral intentions to adopt AI technologies. Data from 359 Chinese university students were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that AI Literacy is a significant predictor of Behavioral Intention to adopt AI, with Social Influence, Performance Expectancy, and Effort Expectancy serving as important mediators. Notably, Facilitating Conditions did not have a significant effect on Behavioral Intention in this context. The results underscore the importance of enhancing AI Literacy among university students to foster positive adoption intentions, particularly through social and expectancy-related factors, providing practical implications for educators and policymakers aiming to promote AI integration in higher education.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Bridging AI literacy and UTAUT constructs: structural equation modeling of AI adoption among Chinese university students

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月2日

查看全文
  • Ke Qi,
  • Gong Yunhong,
  • Ke Changping

摘要

In the rapidly evolving landscape of artificial intelligence (AI), understanding the factors that influence individuals’ intentions to adopt AI technologies is crucial, particularly within educational contexts. This study addresses a critical gap in the literature by examining how AI literacy interacts with the constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT)—Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions—to shape university students’ behavioral intentions to adopt AI technologies. Data from 359 Chinese university students were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that AI Literacy is a significant predictor of Behavioral Intention to adopt AI, with Social Influence, Performance Expectancy, and Effort Expectancy serving as important mediators. Notably, Facilitating Conditions did not have a significant effect on Behavioral Intention in this context. The results underscore the importance of enhancing AI Literacy among university students to foster positive adoption intentions, particularly through social and expectancy-related factors, providing practical implications for educators and policymakers aiming to promote AI integration in higher education.