Demographic Determinants of AI Utilization in Higher Education: Evidence from SUCs in Davao City
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This study examined whether demographic characteristics significantly influence the Actual System Use (ASU) of Artificial Intelligence (AI) in instruction among faculty members in State Universities and Colleges (SUCs) in Davao City. Anchored on the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology, the study employed a quantitative descriptive–comparative design using survey responses from 188 faculty members. Independent samples t-test, one-way ANOVA, and Tukey’s HSD post hoc analysis were utilized to determine differences in ASU across demographic groups. Results revealed no significant differences in AI utilization based on gender and academic discipline, with only small to negligible effect sizes. In contrast, significant differences with moderate to large effect sizes were observed across age, years of teaching, and salary grade. Post hoc analyses further showed that younger, less experienced, and lower-ranked faculty demonstrated significantly higher levels of AI utilization compared to older and more senior faculty members. These findings suggest that AI adoption in higher education is shaped more strongly by career-stage dynamics and institutional roles than by gender or disciplinary background. The study highlights the need for differentiated faculty development initiatives, mentoring programs, and targeted institutional support mechanisms to promote equitable and sustainable AI integration in higher education.
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Bond, M., Bedenlier, S., Marín, V. I., & Händel, M. (2023). Emergency remote teaching in higher education: Mapping the first global online semester. International Journal of Educational Technology in Higher Education, 20(1), 1–24. https://doi.org/10.1186/s41239-021-00282-x
Chiu, T. K. F. (2022). Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(S1), S14–S30. https://doi.org/10.1080/15391523.2021.1891998
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Dwivedi, Y. K., Hughes, D. L., Rana, N. P., Slade, E. L., & Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, K. A., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Huang, J., Saleh, S., & Liu, Y. (2022). Examining faculty adoption of artificial intelligence in higher education: The role of motivation and institutional support. Computers & Education: Artificial Intelligence, 3, 100089. https://doi.org/10.1016/j.caeai.2022.100089
König, J., Jäger-Biela, D. J., & Glutsch, N. (2020). Adapting to online teaching during COVID-19 school closure: Teacher education and teacher competence effects among early career teachers in Germany. European Journal of Teacher Education, 43(4), 608–622. https://doi.org/10.1080/02619768.2020.1809650
Koumachi, B. (2019). Digital natives and digital immigrants: Myth or reality? International Journal of Technology in Education, 2(2), 45–52.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Oh, E., & Reeves, T. C. (2014). Generational differences and the integration of technology in learning environments. Educational Technology Research and Development, 62(5), 619–640. https://doi.org/10.1007/s11423-014-9353-0
Oreg, S., Vakola, M., & Armenakis, A. (2018). Change recipients’ reactions to organizational change: A 60-year review of quantitative studies. Journal of Applied Behavioral Science, 54(1), 5–38. https://doi.org/10.1177/0021886317714271
Teo, T., Ursavaş, Ö. F., & Bahçekapılı, T. (2019). Efficiency of the technology acceptance model to explain pre-service teachers’ intention to use technology: A Turkish study. Educational Technology & Society, 22(3), 1–13.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Yuen, A. H. K., Cheng, M., & Chan, C. K. K. (2021). Gender differences in technology adoption in higher education: A global perspective. Computers & Education, 168, 104192. https://doi.org/10.1016/j.compedu.2021.104192
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
Zheng, B., Lin, C. H., & Kwon, J. B. (2025). Artificial intelligence adoption in higher education: Examining demographic and institutional influences. Computers & Education: Artificial Intelligence, 6, 100180. https://doi.org/10.1016/j.caeai.2024.100180