AI dependency and its relationships with anxiety, quality of life, and digital stress
Abstract Background The integration of artificial intelligence (AI) into higher education has accelerated, yet little is known about the psychological mechanisms underlying students’ reliance on AI. This study conceptualizes AI dependency as a complex cognitive–motivational construct that extends beyond mere usage, influencing anxiety, digital stress, and quality of life. Methods A 521 participants (predominantly undergraduate, 80% female) recruited via snowball sampling at King Abdulaziz University. Self-administered standardized instruments assessed AI dependency, AI-related general anxiety, digital stress, quality of life, and AI dependency. A network analysis approach was employed to examine the interrelations among AI dependency, cognitive offloading, anxiety, availability pressure, FoMO, digital overload, digital vigilance, social acceptance anxiety, and quality of life among university students. This approach allowed identification of central variables and conditional interactions within a dynamic psychological system. Results AI dependency emerged as a structurally foundational variable, reorganizing students’ cognitive and emotional experiences. It was closely linked to digital stressors, including digital vigilance and fear of missing out, while anxiety functioned as a mediator connecting cognitive reliance to environmental pressures. Social acceptance anxiety translated cognitive pressures into relational–identity concerns, and cumulative effects manifested in reduced quality of life. The network revealed non-linear, conditional associations, highlighting that the psychological impact of AI dependency is mediated by cognitive, motivational, and contextual factors rather than by direct usage intensity alone. Conclusions AI dependency is not a neutral or purely functional behavior but a central psychological construct with both potential advantages, such as reduced cognitive load and increased efficiency, and risks, including diminished autonomy, heightened anxiety, and long-term digital strain. These findings offer a culturally contextualized model for understanding AI’s influence on student well-being and provide a framework for interventions that target central nodes in the network to promote healthier engagement with AI in academic settings.