Perceptions and institutional readiness for generative AI adoption in education using a multi-method approach - Gorro, Ken; Ranolo, Elmo; Roble, Lawrence; Ybañez, Adrian; Ilano, Anthony; Pepito, Joseph; Santillan, Rue Nicole; Ranolo, Cesar; Barbecho, Emardy; Mata, Purity; Neiz, Anna Marie (International Journal of Evaluation and Research in Education (IJERE), 2025)
The rapid emergence of generative artificial intelligence (GenAI) tools like ChatGPT is reshaping educational practices, presenting both transformative opportunities and institutional challenges. This study offers a novel, integrative framework for understanding the adoption of GenAI tools in higher education by combining quantitative and qualitative analyses within a hybrid methodological design. Specifically, it is the first to incorporate the analytical hierarchy process (AHP), fuzzy decision-making trial and evaluation laboratory (Fuzzy DEMATEL), and the extended technology acceptance model (ETAM) in a unified model of adoption, augmented by thematic analysis of user experiences. A stratified random sample of 1,297 participants—comprising 1,191 students and 105 faculty members from various departments—ensured proportional representation across the university. AHP was employed to prioritize key adoption criteria, Fuzzy DEMATEL uncovered the causal interdependencies among constructs, and ETAM validated the direct and indirect effects influencing behavioral intention. Thematic analysis provided contextual depth regarding institutional barriers and individual perceptions. Findings reveal that attitude toward GenAI and intention to use (IU) are the strongest drivers of adoption. Notably, university support (US) emerged as a central enabler, significantly influencing both awareness and perceived usefulness (PU). This study contributes a comprehensive and multi-method framework that educational institutions can use to ethically, effectively, and equitably integrate GenAI technologies into academic ecosystems.