Overreliance on Generative Artificial Intelligence Applications and Its Relationship with Self-Regulated Learning in Physical Geography among Fifth-Grade Literary Female Students
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Abstract
This study investigated overreliance on generative artificial intelligence applications and its relationship with self-regulated learning in physical geography among fifth-grade literary female students. A descriptive correlational design was adopted. The sample comprised 100 students equally distributed across two classes at Bab Al-Ilm Secondary School, Baghdad Al-Karkh Third Directorate of Education, during the 2025-2026 academic year. Two scales measured AI overreliance and self-regulated learning. Data were analyzed using means, standard deviations, one-sample t-tests, Pearson correlation, one-way ANOVA, and Scheffe post-hoc comparisons. Results showed a significantly elevated level of AI overreliance, a significantly lower level of self-regulated learning, and a negative correlation between the two variables (r = -0.61). Self-regulation also declined progressively as AI-use intensity increased. The study recommends guided AI use in geography lessons through tasks requiring verification, map reading, explanation, comparison, and systematic reference to the textbook.
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