ArticleInternational journal of nursing studies advances2026
Nursing students' readiness for and acceptance of artificial intelligence technologies in clinical skills training: A cross-sectional study.
Article in International journal of nursing studies advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Artificial intelligence is increasingly transforming healthcare delivery and health professions education, particularly in clinical skills training and simulation-based learning environments. This transformation necessitates an evaluation of the readiness of future nurses. However, limited evidence exists regarding nursing students' readiness and acceptance of artificial intelligence-based technologies in clinical skills training within Saudi universities. Aim: The aim was to assess nursing students' readiness and acceptance, and intention to use artificial intelligence-based healthcare technologies in clinical skills training. Design: A cross-sectional descriptive correlational design was used. Methods: A self-administered online questionnaire was distributed to a convenience sample of 747 undergraduate nursing students across 10 universities in Saudi Arabia. The survey measured artificial intelligence readiness (cognition, technical ability, vision, and ethics) and artificial intelligence acceptance (perceived usefulness, perceived ease of use, attitude, behavioral intention), along with demographic and educational data. Data were analyzed using descriptive statistics, independent Results: Participants demonstrated a moderate to high level of overall readiness and acceptance for artificial intelligence in clinical training. The highest readiness scores were observed in the vision and ethics domains, whereas technical ability was the lowest. For acceptance, attitude and behavioral intention were the highest-rated subdomains. Academic year was positively associated with both readiness and acceptance, with more advanced students demonstrating higher levels. Participants with prior exposure to artificial intelligence demonstrated significantly higher readiness and acceptance scores than those without prior exposure, although the magnitude of the association was small. A moderate positive relationship was also observed between readiness and acceptance. Conclusions and implications: Participating nursing students demonstrated conceptual and ethical preparedness for artificial intelligence integration but reported gaps in technical competence. Academic progression was associated with higher readiness and acceptance, while prior exposure was also associated with more favorable readiness and acceptance outcomes. We suggest that Saudi nursing students may have lower technical skills and practical competence compared with their conceptual, ethical, and attitudinal readiness for artificial intelligence, highlighting the need for further attention to technical training within artificial intelligence education.
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