Scale Development for the Meaningful Utilization of Generative Artificial Intelligence in Education Through Exploratory Factor Analysis: Meaningful Utilization of GenAI in Education - Samuel A. Balbin; Faith Micah D. Abenes-Balbin; John Peter G. De Leon (Proceedings of International Conference of Graduate School on Sustainability, 2026)
As generative AI technologies rapidly reshape education worldwide, higher education institutions face urgent concerns about how to integrate these tools meaningfully and ethically. This study developed and validated a contextualized measurement scale to assess the meaningful use of generative artificial intelligence (GenAI) in a Philippine higher education institution. Employing an exploratory sequential mixed-method design, qualitative analysis of institutional policies identified six themes: ethical use, academic integrity, institutional support, user responsibility, data privacy, and research integration. These informed the creation of 53 scale items, refined through expert validation. The scale was administered to 257 faculty members at Rizal Technological University, and exploratory factor analysis revealed a four-factor structure: Ethical AI Awareness, AI-Enhanced Engagement, Transparency and Disclosure, and Operational Readiness. These factors explained 64.5 percent of the variance, with strong internal consistency, averaging a Cronbach’s alpha of 0.903. Model fit indices, including RMSEA at 0.065 and TLI at 0.874, indicated acceptable structural validity. The resulting instrument offers a reliable framework for evaluating GenAI integration in classrooms, providing insights for policy formulation and capacity building. By grounding the tool in local context and empirical data, the study contributes to responsible, inclusive, and transparent AI adoption in Philippine higher education and offers a foundation for future research and institutional governance.