PREDICTORS OF EFFECTIVENESS OF GENERATIVE ARTIFICIAL INTELLIGENCE GOVERNANCE FRAMEWORKS IN HIGHER EDUCATION INSTITUTIONS: BASIS FOR POLICY DEVELOPMENT

The rapid integration of Generative Artificial Intelligence (Generative AI) presents higher education institutions (HEIs) with dual challenges and opportunities, necessitating robust governance to uphold academic integrity and ethical standards. This study aimed to identify the critical predictors influencing the perceived effectiveness of Generative AI governance frameworks within HEIs in Calamba City, Laguna, Philippines, providing an empirical foundation for tailored policy development. A quantitative, descriptive-correlational research design was employed, utilizing a self-developed questionnaire based on a Semantic Differential Scale. Survey data were collected from a stratified sample of Faculty, Administrators, and Students. Data analyses included descriptive statistics, Pearson correlation, and standard multiple regression analysis to test the predictive power of seven variables: leadership, policies, infrastructure, ethical considerations, faculty readiness, student readiness, and budgetary considerations. Findings confirmed that governance effectiveness is fundamentally an ethical imperative, with Ethical Considerations emerging as the strongest independent predictor for all groups. Key divergences were observed: Faculty/Administrators’ perceived effectiveness was significantly driven by Budgetary Considerations and Faculty Readiness, indicating resource constraints. Conversely, students prioritized Technological Infrastructure and Institutional Policies, highlighting gaps in resource accessibility and clarity of rules. Both groups rated overall effectiveness highly but identified needs for improvement in mitigating algorithmic bias and clarifying intellectual property rights. Results highlighted a resource-perceptual mismatch, indicating that successful governance extends beyond compliance to focus on ethical principles. The study recommended adopting the AI-COMPASS Governance Framework to prioritize sustainability and enhance ethical guardrails. This research enriches the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theory by linking institutional factors to technology governance success.