Disciplinary Differences in University Students’ AI Adoption: A Technology Acceptance Model Approach

초록

This study investigates university students’ acceptance and use of Artificial Intelligence (AI) technologies, drawing on the Technology Acceptance Model (TAM) as the guiding framework. This study collected pre- and post-semester survey data from 108 university students representing diverse academic majors. Four TAM constructs—Actual Use, Attitudes toward AI, Perceived Usefulness (PU), and Perceived Ease of Use (PEOU)—were measured through a validated survey instrument. Paired-samples t-tests revealed no statistically significant changes in university students’ perceptions across the semester, despite small numerical increases in Actual Use and PEOU. One-way Analysis of Variance (ANOVA) results indicated that disciplinary differences played a notable role in shaping perceptions of AI usefulness. Significant differences emerged in the expectations and usefulness dimensions of PU, with Science, Technology, Engineering, and Mathematics (STEM) students reporting higher PU than humanities and social science majors. These findings suggest that university students’ academic backgrounds influence their expectations and evaluations of AI. The study underscores the importance of designing AI-integrated curricula that account for interdisciplinary differences in AI literacy.

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