Correlates and predictors of generative artificial intelligence adoption among Filipino college teachers

Abstract

Filipino college teachers in public higher education institutions (HEIs) are increasingly expected to integrate generative artificial intelligence (GenAI) tools into their practice. Evidence on what drives this adoption in Philippine public universities, however, remains scarce. This study used a quantitative, cross-sectional correlational design to examine the levels, relationships, and predictors of GenAI adoption among 301 teachers from two public HEIs in Pampanga, Philippines, guided by the unified theory of acceptance and use of technology (UTAUT). Teachers reported strong agreement across all UTAUT constructs, with effort expectancy receiving the highest rating and facilitating conditions the lowest. Performance expectancy (β = 0.406, p < 0.001) was the strongest predictor of behavioral intention, followed by social influence (β = 0.295, p < 0.001) and effort expectancy (β = 0.257, p < 0.001), which together explained 65% of the variance in intention. Facilitating conditions did not significantly predict behavioral intention but, together with behavioral intention (β = 0.629, p < 0.001), predicted actual use (β = 0.266, p < 0.001). Teachers are more likely to adopt GenAI when they see clear instructional benefits and when peers and administrators encourage its use; sustained practice depends on whether institutions can provide the necessary infrastructure and training to support it. Public HEIs are urged to invest in AI literacy programs, strengthen peer and administrative endorsement, and address gaps in information and communication technology (ICT) infrastructure to support responsible and lasting GenAI integration.