Exploring the Relationship between Fear of Being without Artificial Intelligence, AI Addiction, and Academic Self-Efficacy among University Students

Abstract

This study examined the links between AI addiction, academic self-efficacy, related anxieties, and fear of being without AI in education (AILPES). The study employed a descriptive and correlational design and collected data from 544 undergraduate students. Data collection tools included a Personal Information Form, the Artificial Intelligence Addiction Scale, the Academic Self-Efficacy Scale, and the Fear of Being Without Artificial Intelligence in Education Scale. Descriptive statistics were used to summarize demographic characteristics and study variables, while Pearson correlation coefficients were calculated to examine bivariate relationships among variables. Multiple linear regression analysis was conducted to test the predictive effects of ASE, AIAS, and grade point average (GPA) on AILPES. The findings indicated that age was negatively associated with AILPES and lack of academic confidence without AI. ASE was positively related to both AILPES and academic self-efficacy anxiety. AIAS demonstrated strong positive correlations with AILPES, academic self-efficacy anxiety, and lack of academic confidence without AI. The results revealed that fear of being without AI among university students is significantly associated with both AI addiction and academic self-efficacy. While AI addiction increases this fear, higher academic self-efficacy appears to reduce it. Based on these findings, it is recommended that higher education institutions implement educational and psychoeducational strategies to promote balanced AI use, enhance AI literacy, and support students’ academic self-efficacy. The findings highlight the importance of educational strategies aimed at enhancing AI literacy and promoting digital balance.

Keywords

• Artificial intelligence
• addiction
• academic self-efficacy
• phobia education
• university students

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