Factors Influencing IT Students' Willingness to Use Generative AI for Learning: A UTAUT-Based Study
VERSION OF RECORD ONLINE: 15/09/2025
Corressponding author's email:
tnminh.cdktcn@khanhhoa.edu.vnDOI:
https://doi.org/10.54644/jte.2025.1738Keywords:
Generative Artificial Intelligence, UTAUT model, Information Technology, Teaching and Learning Information Technology, AI EducationAbstract
The rapid advancement of Generative Artificial Intelligence (GAI) has greatly influenced its adoption in various fields, particularly education. This study utilizes the Unified Theory of Acceptance and Use of Technology (UTAUT) to explore factors shaping the willingness of vocational school and college-level information technology students to use GAI for learning specialized subjects. Data were collected through an online survey via Google Forms, targeting 115 IT students in Khanh Hoa province, Vietnam. The analysis focuses on four key factors: performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC), with moderating variables such as gender, age, and learning year. The findings reveal that performance expectancy, social influence, and facilitating conditions have significant positive impacts on students’ willingness to use GAI, while effort expectancy was not statistically significant. Among the factors, facilitating conditions had the strongest influence, followed by social influence and performance expectancy. No significant differences were observed across age or learning year, but female students demonstrated a greater reliance on support and resources than their male counterparts. These results underscore the transformative potential of GAI as an educational tool, highlighting the importance of integrating supportive resources and fostering a conducive learning environment. This study provides valuable insights into the factors driving GAI adoption among students, paving the way for future research and practical applications in education.
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