From Social Drivers to Sustainable AI Usage and Dependency in Higher Education: Roles of Trust, Perceived Competence, and Perceived Intelligence

Abstract

Generative artificial intelligence (AI) is rapidly reshaping higher education, yet the social pathways that trigger intensive use and may evolve into dependency remain insufficiently understood. This study examines how social drivers shape sustainable AI usage and the potential progression toward dependency. We surveyed 965 university students and analyzed the data using partial least squares structural equation modeling (PLS-SEM). The findings show that social factors, including fear of missing out, word-of-mouth, and subjective norms, primarily influence AI usage through trust in AI. AI usage has a limited direct effect on dependency, whereas dependency is more strongly associated with psychological evaluations of AI benefits, including perceived competence enhancement and perceived intelligence of AI systems. These results support a staged sociotechnical account of AI engagement and point to sustainability-relevant implications for responsible AI integration in higher education (Sustainable Development Goal 4), including trust calibration, competence building, and safeguards against over-reliance that may undermine long-term learning outcomes.

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