From Social Drivers to AI Usage and Dependency in Higher Education

AI is rapidly permeating higher education, yet the mechanisms that sustain student use and tip it toward dependency remain underexplored. This cross-sectional study tests a model in which social drivers (fear of missing out, word-of-mouth, and subjective norms) shape AI use via trust in AI, and examines how perceived competence and perceived intelligence link use to dependency. A structured survey of 985 students from universities in North Cyprus was analyzed using partial least squares structural equation modeling. All hypothesized paths were supported. Word-of-mouth and trust in AI were the strongest predictors of AI use; fear of missing out and subjective norms also increased use directly and indirectly through trust. AI use showed a modest direct effect on dependency, with the primary pathway operating via perceived gains in competence and intelligence. The findings reveal a dual process: adoption is driven by social influence and trust, while dependency is created through the psychological benefits of having successful interactions. Implications include designing courses and policies that help calibrate trust, providing scaffolding of self-regulation, and include verification and reflection activities that allow students to still engage in independence thought to maximize the benefits of using AI.