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.
Raffy Alo (Thu,) studied this question.