Background. Despite the importance of studying factors contributing to nursing students’ attitudes toward artificial intelligence, yet according to our knowledge, no study has addressed the relationship between personality traits and the attitude of nursing students toward artificial intelligence. Aim. This study aimed to unveil whether nursing students’ personality traits are related to their attitude toward AI. Methods. This multicenter cross‐sectional study included 218 nursing students from three governmental universities across various regions of the Kingdom of Saudi Arabia. Data were gathered online, utilizing the Big Five Inventory, the General Attitudes toward Artificial Intelligence Scale, and a demographic questionnaire. Descriptive statistics, Pearson’s correlation, and regression analysis were employed. The research complied with the STROBE checklist. Results. Findings indicated that nursing students with a high score in the openness trait displayed positive attitudes toward artificial intelligence. Conversely, those who scored high in neuroticism and agreeableness exhibited fewer positive attitudes toward artificial intelligence and more negative attitudes toward artificial intelligence. Additionally, nursing students who ranked high in conscientiousness showed a negative attitude toward artificial intelligence. Conclusion. Except for extraversion, personality traits appear to predict attitudes toward artificial intelligence. Implications for Nursing Management. The current study provides a foundation for understanding how generative AI can be integrated into nursing education and practice in a manner that is both effective and considerate of the diverse psychological profiles of students.

Background: Climate change poses a worldwide challenge with anticipated exacerbation in the future, resulting in irreversible consequences. Nursing students may be vulnerable to experiencing psychological effects associated with climate change. Aim: The current study aimed to investigate the relationship between nursing students’ knowledge and attitudes toward climate change and their psychological distress. Method: This descriptive cross-sectional study recruited 377 nursing students from three universities located in Saudi Arabia, Jordan, and Egypt in the Middle East. Data collection was conducted using scales for assessing nursing students’ knowledge and attitudes towards climate change and their psychological distress. Correlations were assessed and multiple regression analysis was conducted to explore the predictors of students’ psychological distress. Results: The current study showed that knowledge regarding climate change significantly and positively correlated to the attitude toward climate change (r = 0.213), then again, the score of psychological distress significantly and negatively correlated to the score of students’ knowledge and attitude regarding climate change (r = − 0.182 and − 0.110 respectively). Regression analyses showed that academic achievement had the strongest positive impact on students’ psychological distress, while knowledge regarding climate change and attitude toward climate change had negative impacts (β = 0.381, β=-0.205, and β=-0.045 respectively). Moreover, knowledge and attitude regarding climate change were found to be significant predictors of students’ psychological distress, collectively accounting for 18.2% of the observed variance. Conclusions/Implication for future practice: The findings highlight the importance of incorporating climate change into nursing education programs. By enhancing nursing students’ knowledge and attitudes towards climate change, there is potential to reduce their psychological distress. This study underscores the need for curriculum reforms that integrate climate change topics, aiming to foster a well-informed and resilient future nursing workforce.