IJIET 2026 Vol.16(6): 1473-1480
doi: 10.18178/ijiet.2026.16.6.2613
doi: 10.18178/ijiet.2026.16.6.2613
Disciplinary Differences in University Students’ AI Adoption: A Technology Acceptance Model Approach
Yong-Jik Lee1 and Hyun-Cheol Choi2,*
1. College of Sarim Honors, Changwon National University, Changwon, South Korea
2. College of General Education, Chung-Ang University, Seoul, South Korea
Email: yongjiklee@changwon.ac.kr (Y.-J.L.); choihc71@cau.ac.kr (H.-C.C.)
*Corresponding author
2. College of General Education, Chung-Ang University, Seoul, South Korea
Email: yongjiklee@changwon.ac.kr (Y.-J.L.); choihc71@cau.ac.kr (H.-C.C.)
*Corresponding author
Manuscript received December 9, 2025; revised December 26, 2025; accepted January 22, 2026; published June 11, 2026
Abstract—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.
Keywords—Artificial Intelligence (AI), AI literacy, Technology Acceptance Model (TAM), university students, disciplinary differences, academic majors
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Keywords—Artificial Intelligence (AI), AI literacy, Technology Acceptance Model (TAM), university students, disciplinary differences, academic majors
Cite: Yong-Jik Lee and Hyun-Cheol Choi, "Disciplinary Differences in University Students’ AI Adoption: A Technology Acceptance Model Approach," International Journal of Information and Education Technology, vol. 16, no. 6, pp. 1473-1480, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).