Understanding Student Dependency on AI: The Role of AI Literacy, Academic Self-Efficacy, and Resource Management Strategies

Abstract

The integration of artificial intelligence (AI) tools in higher education raises important questions about how students engage with these technologies and what shapes that engagement. This study examined how AI literacy, academic self-efficacy, and self-regulated resource management strategies are associated with students’ reported AI dependency. Participants were 478 students from Israeli higher education institutions who completed a cross-sectional online survey assessing AI dependency, AI literacy (four subscales), academic self-efficacy, and resource management strategies (time and study management, effort regulation, and help seeking). Multiple regression and K-means cluster analysis were used. The skill-based dimensions of AI literacy were positively associated with AI dependency, whereas AI self-efficacy and academic self-efficacy were both negatively associated, suggesting a unified compensatory self-efficacy mechanism. Effort regulation also predicted lower dependency, while general academic help seeking predicted higher dependency. The model explained 27.3% of the variance in AI dependency. Cluster analysis identified four learner profiles differing in literacy-dependency combinations and in self-regulatory resources. The findings suggest that fostering AI literacy alone is insufficient. Developing students’ self-efficacy beliefs and self-regulated learning practices appears equally important for promoting balanced and intentional AI engagement in higher education.