Exploring the Relationship between AI Dependency and Academic Self-Efficacy: A Study of Diyala University Students
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Objective: This research was conducted to assess the relationship between academic self-efficacy and dependence on artificial intelligence among students at Diyala University.
Methods and Materials: The research was a descriptive cross-sectional design, conducted with 380 students at Diyala University. In collecting data, four tools were used for data collection: the Socio-Demographic Characteristics of the Student, Students' Information and Opinions Regarding Artificial Intelligence Technology, the Academic Situations Specific Perceived Self-Efficacy Scale (ASSPSE), and the Artificial Intelligence Dependence Scale. The data were analyzed and interpreted using the application Statistical Package for Social Sciences (SPSS), version 26.0. Data were analyzed using descriptive statistics, Kolmogorov–Smirnov, Shapiro–Wilk, Spearman, Mann Whitney U, and Kruskal–Wallis tests.
Findings: The result was that more than half of university students (54.5%) perceive moderate academic self-efficacy, while more than half of university students (56.6%) demonstrate low dependency on AI.
Conclusion: The results of this study indicate a significant negative correlation between dependence on artificial intelligence and perceived academic self-efficacy among university students. This indicates that higher dependence on AI is associated with lower levels of academic self-efficacy.
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