Dataset for AI dependence model

The rapid adoption of generative AI in higher education has outpaced institutional policy, creating uncertainty regarding AI cheating and appropriate AI use. This study investigates the drivers of AI dependence among college students and its psychological consequences: "noAIphobia" (anxiety when AI is unavailable) and AI use stigma. Drawing on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework, 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 primary driver of AI dependence, alongside efficiency and quality expectancies. AI dependence creates two opposing psychological routes affecting continuance intention: It increases noAIphobia, which drives continued use to avoid deprivation anxiety, while also increasing AI use stigma, which discourages AI use due to fear of negative social judgment. Furthermore, academic integrity concerns amplify the relationship between dependence and AI use stigma. These findings highlight the tensions students face when using AI, suggesting that higher education institutions must establish clear policies to mitigate psychological distress and promote healthy AI engagement.