Page 651 - ISC PROCEEDINGS 21.4

related knowledge, and digital skills, can more easily grasp the practical academic
significance of generative AI technologies. This result is in line with the recent
publications that emphasize AI literacy as a key facilitator of technology adoption among
higher education students (Zhou and Chen, 2023; Sergeeva and Ivanov, 2025).
Lastly, predictive validity of the extended TAM framework is also validated by the
significant association between behavioral intention and actual use. This finding is very in
line with the initial assumptions of the TAM, and new generative AI adoption research
papers in the field of higher education (Sousa and Gomes, 2025; Moggelvang and
Pedersen, 2025).
6. Conclusion
The rapid integration of generative artificial intelligence (GenAI) into higher
education has fundamentally transformed digital learning environments, reshaping how
students access information, complete academic tasks, and engage with knowledge
creation processes. In response to this technological shift, the present study examined
the determinants of students’ adoption of generative AI tools by extending the
Technology Acceptance Model (TAM) within the context of higher education.
The findings confirm that the core TAM constructs—perceived usefulness and
perceived ease of use—remain central predictors of technology adoption. Students are
more likely to develop positive attitudes toward generative AI tools when they perceive
them as beneficial for improving academic performance and easy to operate. These
perceptions significantly influence behavioral intention, which ultimately translates into
actual usage behavior.
There are a number of limitations in the current research. To begin with, the
convenience sampling method used, which is a non-probability one, can restrict the
extrapolation of the results to other contexts of higher education because the study
sample does not necessarily reflect the whole population of university students. Second,
the study is cross-sectional in nature, which limits capturing variations in the perceptions
and actual usage behavior of students towards the change with time. Because the
implementation of generative technologies of artificial intelligence is fast-changing, future
research should be conducted using longitudinal research designs in order to more
effectively investigate the shifts in both behavioral intention and actual use. Third, the
research was limited to student groups and did not pay much attention to the attitudes of
institutional policies, administrators, and faculty, as these factors might also determine
the future adoption of generative AI in online learning platforms. It is recommended that
future studies involve more than just one stakeholder, larger sample sizes, and
comparison between different countries in order to improve the external validity and
generalizability of the results.

References
[1]. Al Darayseh, A. (2023). Acceptance of artificial intelligence in teaching science:
Science teachers’ perspective. Computers and Education: Artificial Intelligence, 4, 100132.
https://doi.org/10.1016/j.caeai.2023.100132
[2]. Carmona-Lavado, A., Cuevas-Rodríguez, G., Cabello-Medina, C., & Fedriani, E. M.
(2021). Does open innovation always work? The role of complementary assets.
Technological Forecasting and Social Change, 162, 120316.
https://doi.org/10.1016/j.techfore.2020.120316

650