Affiliations:
Faculty of Nursing, University of Hafr Al-Batin, Hafr Al-Batin, Saudi Arabia
Artificial intelligence (AI) is increasingly influencing nursing education and clinical practice worldwide; however, limited evidence exists on how academic and demographic factors affect nursing students’ readiness to engage with AI in low- and middle-income countries. This study examined the relationship between academic competence and nursing students’ attitudes toward AI, with academic seniority as a mediating variable and age and gender as moderating variables. A cross-sectional, multi-institutional survey was conducted among 550 undergraduate nursing students from six Egyptian universities during the 2024–2025 academic year. Data were collected using validated measures of academic competence and attitudes toward AI. Correlation, regression, mediation, and moderation analyses were applied to test the study hypotheses. The findings showed a significant positive association between academic competence and attitudes toward AI (r = 0.47, p < .001). Academic seniority partially mediated this relationship (β = 0.04, p < .001), while age (β = 0.08, p = .008) and gender (β = 0.10, p = .013) significantly moderated it, with stronger associations observed among older and female students. These results highlight the importance of competence-based educational approaches and inclusive curriculum design in supporting AI integration in nursing education. Providing targeted support for younger and less academically advanced students, along with enhancing faculty digital skills, may improve equitable AI readiness among nursing students.
Artificial intelligence, Nursing education, Academic competence, Student attitudes, Digital readiness
https://doi.org/10.21833/ijaas.2026.02.002
Eltantawy, E. E. E. (2026). Academic competence as a predictor of nursing students’ readiness for artificial intelligence. International Journal of Advanced and Applied Sciences, 13(2), 10–16. https://doi.org/10.21833/ijaas.2026.02.002