Knowledge, Attitude, Benefits, Risks, Barriers, Professional Impact, and Preparedness of Nursing Students Toward the...

Background/Objectives: Artificial intelligence (AI) is increasingly used in healthcare to support clinical decision-making, patient monitoring, and administrative tasks. Nurses are expected to work with these technologies. However, the evidence suggests that their knowledge and preparedness remain limited. As future healthcare providers, nursing students must be prepared to integrate AI into their practice. This study aimed to assess nursing students' knowledge, attitudes, perceived benefits and risks, barriers, professional impact, and preparedness toward AI in healthcare. Methods: This cross-sectional descriptive study was conducted between April and July 2024 at the College of Nursing, University of Hail, Saudi Arabia. A convenience sample of 320 undergraduate nursing students completed an online structured questionnaire that assessed their demographics, knowledge, attitudes, perceived barriers, benefits, risks, professional impact, and preparedness. Data were analyzed using IBM SPSS version 27 with descriptive statistics. Inferential analyses, including independent t-tests and one-way ANOVA, were performed to examine differences between groups. Pearson's correlation was used to identify correlations between the study variables. Statistical significance was set at p < 0.05. Results: Most students (79.7%) had poor AI knowledge, whereas 52.5% reported positive attitudes. Older students (≥24 years) and internship students showed significantly more positive attitudes (p < 0.001). Knowledge was weakly correlated with attitudes (r = 0.147), benefits (r = 0.222), and risks (r = 0.152). Attitudes were weakly positively correlated with benefits (r = 0.243) and negatively correlated with barriers (r = -0.219). Conclusions: Despite their positive attitudes, nursing students showed limited knowledge and preparedness. Integrating AI education and practical training into nursing curricula is therefore essential. These findings should be interpreted cautiously given the cross-sectional design, single-institution sampling, and reliance on self-reported measures, which may limit generalizability.