Artificial intelligence in nursing education: knowledge, perceptions, and associated factors among Egyptian nursing students across universities

Résumé fourni par la source

To assess Egyptian nursing students’ level of AI knowledge, perceptions of AI benefits, and fears regarding AI in nursing education and practice, and to examine associated sociodemographic factors. The rapid integration of artificial intelligence (AI) into healthcare has generated both enthusiasm and apprehension among nursing professionals and students. While AI offers substantial potential benefits in clinical efficiency, decision support, and educational innovation, important concerns remain regarding individualised care, professional displacement, and data privacy. This combination of promise and concern suggests a cautiously receptive context in which nursing students may recognise AI’s value while remaining uncertain about its implications for practice and education. Therefore, evidence-based assessment of nursing students’ knowledge and perceptions is needed. A cross-sectional descriptive design was utilised in this study. A convenience sample of 2412 nursing students was drawn from two nursing faculties between May 2024 and January 2025. Data were collected using a structured three-part questionnaire covering individual sociodemographic characteristics, a dichotomous AI knowledge test, and an attitude scale measuring perceived benefits and fears regarding AI. Approximately 46.4% of students demonstrated good AI knowledge (mean score 3.2 ± 1.3 out of 5). The mean perceived benefits score was 10.9 ± 3.5 (maximum 14), indicating generally favourable views of AI’s educational and clinical support applications. The mean fear score was 5.0 ± 1.6. Notably, 72.2% of students expressed concern that AI may replace nurses in the future, and 57.8% reported discomfort with using AI in educational settings. Statistically significant differences were observed across academic years for knowledge, perceived benefits, and fears, and across age groups for perceived benefits and fears; age-related differences in knowledge were smaller but remained statistically significant. No significant differences were found by gender or university. Egyptian nursing students demonstrated a foundational understanding of AI and broadly recognised its benefits for education and clinical support. Nevertheless, substantial gaps in formal AI training and pronounced fears about professional displacement were identified. Structured, ethically grounded AI educational programs are urgently needed to enhance students’ digital competencies and ensure the safe and effective use of AI in nursing practice. Not applicable. • Artificial intelligence (AI) is increasingly being integrated into healthcare systems and nursing-related contexts, with potential to improve efficiency, decision support, and selected aspects of patient care. • At the same time, AI adoption raises important ethical and professional concerns, including data privacy, accountability, and the possible impact of automation on healthcare roles. • Because nurses play a central role in patient care delivery, their readiness and acceptance are important to the safe and effective integration of AI into practice. • This study provides evidence from Egypt on nursing students’ knowledge of AI and their perceptions of its benefits and risks in nursing education and practice. • The findings show that students generally recognised several potential benefits of AI, while also expressing substantial concerns, particularly regarding nurse replacement, data confidentiality, and the appropriateness of AI in educational settings. • Significant differences in AI-related knowledge, perceived benefits, and fears were observed across academic years, while age was associated with perceived benefits and fears and showed a smaller association with knowledge. No significant differences were found by gender or university. • Nursing education programs should integrate structured AI-related content into undergraduate curricula to strengthen students’ digital competence and preparedness for AI-enabled practice. AI education should include ethical and professional dimensions, particularly data privacy, accountability, bias, and the continuing role of human clinical judgment in patient-centred care. • Educational strategies such as case-based learning, supervised discussion, and critical appraisal of AI-supported outputs may help reduce uncertainty and support informed, responsible use of AI in nursing.

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