Support for Generative Artificial Intelligence as a Predictor of AI Self-Efficacy, Valuing, and Integration in Educational Leadership and Teaching

Abstract

Generative Artificial Intelligence (GenAI) is reshaping educational leadership and redesigning instruction by offering new opportunities for efficiency, pedagogy, and assessment, while simultaneously raising ethical and professional challenges. This study examined the predictive influence of organizational support for GenAI on school heads’ self-efficacy, valuing, and integration, and explored how these leadership dimensions relate to teachers’ AI competence self-efficacy across the six TAICS domains (AI knowledge, pedagogy, assessment, ethics, human centered education, and professional engagement). Using a descriptive–correlational design, data were collected from 11 school heads and 125 teachers in District 3, Schools Division Office of Manila. Descriptive results revealed that school heads reported consistently high levels of support (M = 3.64) and valuing (M = 3.62), with slightly lower but positive scores in self-efficacy (M = 3.47) and integration (M = 3.45). Teachers demonstrated moderate competence across TAICS domains, with ethics (M = 3.13) and pedagogy (M = 3.11) scoring highest, and assessment (M = 3.01) lowest. Inferential analyses showed positive but non-significant correlations (r = .141 to .426, p > .05) and a regression model that explained 19.6% of the variance (R² = .196, F(3,7) = 0.569, p = .653). Although the model was not statistically significant, the effect size (Cohen’s f² = 0.24) indicated a medium to large practical effect, suggesting meaningful though inconclusive relationships. Findings suggest that while organizational support and leader valuing are strong, deliberate integration strategies and targeted professional initiatives are essential to effectively translate leadership commitment into enhanced teaching practices.