Motivational factors and psychological correlates of AI dependence among college students: Competitive conformity, noAIphobia, and AI use stigma

Abstract

The rapid adoption of generative AI in higher education has outpaced institutional policy, creating uncertainty regarding AI cheating and appropriate AI use. Building on the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, this study examines the motivational predictors of AI dependence among college students and the psychological states associated with it: “noAIphobia” (anxiety when AI is unavailable) and AI use stigma. To empirically test the model, we surveyed 393 U.S. college students and analyzed the data using partial least squares structural equation modeling (PLS-SEM). Results indicate that competitive conformity—the fear of falling behind peers—is a strong predictor of AI dependence, alongside efficiency and quality expectancies. AI dependence predicts two psychological states that affect continuance intention in opposing directions: noAIphobia is positively associated with continued use as students seek to avoid AI deprivation anxiety, whereas AI use stigma is negatively associated with continued use due to fear of negative social judgment. Academic integrity concerns further amplify the dependence–stigma relationship. These findings reveal the psychological tensions students experience when using AI and underscore the need for clear institutional policies to mitigate psychological distress and promote healthy AI engagement.