Generalized Linear Model Mediation Model: Modeling Generative Artificial Intelligence Adoption in Higher Education: Insights from the Theory of Planned Behavior
TL;DR
The results suggest that students’ willingness to engage with GenAI is shaped not only by favorable perceptions but also by confidence in the system’s reliability, integrity, and ethical use, suggesting the need to foster trust and cultivate positive attitudes through transparent policies, ethical guidelines, and targeted GenAI literacy initiatives.
Abstract
This study investigates the determinants of generative artificial intelligence (GenAI) adoption in higher education through a modified Theory of Planned Behavior (TPB) framework. Focusing on tertiary-level students, the model highlights attitude and trust as primary predictors of behavioral intention, excluding subjective norms and perceived behavioral control. Data were collected using a structured survey and analyzed using generalized linear modeling to examine the relationships among the constructs. The findings indicate that both attitude toward GenAI and trust in technology exert significant positive effects on students’ intention to adopt GenAI for academic use, with trust demonstrating a comparatively stronger influence. These results suggest that students’ willingness to engage with GenAI is shaped not only by favorable perceptions but also by confidence in the system’s reliability, integrity, and ethical use. The study contributes to the literature by refining the TPB framework in the context of emerging educational technologies and providing empirical evidence from a developing country setting. Implications for higher education institutions include the need to foster trust and cultivate positive attitudes through transparent policies, ethical guidelines, and targeted GenAI literacy initiatives.