AI Summary & Key Takeaways
AI InsightIt is recommended to develop educational strategies that support academic self-efficacy and encourage the use of AI applications in nursing education.
📌 3 Key Takeaways
Abstract
Aims: The aim of this study was to examine the relationship between nursing students’ academic self-efficacy levels and their attitudes toward Artificial Intelligence (AI). Methods: This study was conducted using a descriptive and cross-sectional design and included 994 nursing students. Data were collected using an information form containing students’ sociodemographic characteristics, the Academic Self-Efficacy Scale, and the AI Attitude Scale. Descriptive statistics, independent samples t-test, one-way analysis of variance, Pearson correlation, and linear regression analyses were used for data analysis. Results: According to the findings, students’ academic self-efficacy levels were above moderate level, and their attitudes toward AI were positive. Correlation analysis revealed a positive, moderate, and statistically significant relationship between academic self-efficacy and attitudes toward AI (r=0.58; p<0.001). In addition, academic self-efficacy was positively associated with positive attitudes toward AI and negatively associated with negative attitudes. Regression analysis demonstrated that academic self-efficacy was a significant predictor of attitudes toward AI. Specifically, this negative association was also significant (r=-0.53; p<0.001), and academic self-efficacy alone explained 33.3% of the variance in attitudes toward AI (β=0,58; p<0,001), increasing to 44.4% when demographic variables were added to the model (β=0.42; p<0.001). Conclusion: Higher levels of academic self-efficacy were found to play an important role in developing more positive attitudes toward AI among students. It is recommended to develop educational strategies that support academic self-efficacy and encourage the use of AI applications in nursing education.