Self-Reported Reliance on Artificial Intelligence Applications and Academic Stress among University Students: A Cross-Sectional Study
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Objective: This study aimed to reveal the relationship between self-reported levels of dependency and levels of academic stress among university students.
Methods and Materials: The study adopted a quantitative cross-sectional correlational approach and was conducted on a sample of 380 male and female students from Diyala University across various disciplines and academic levels. Data were collected using a structured questionnaire to measure the level of reliance on artificial intelligence applications and the level of academic stress.
Findings: The results showed a moderate level of both reliance on AI applications (55.5%) and academic stress (82.1%). Statistical analysis also revealed a statistically significant positive relationship between self-reported reliance on AI applications and academic stress (r =285, p =.001), indicating that increased academic stress correlated with a higher level of reliance on AI applications.
Conclusion: A significant positive correlation was found between self-reported levels of dependency and academic stress levels among university students. The study recommended guiding the balanced use of these applications and reducing academic stress to improve students' mental health.
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