ArticleJournal of multidisciplinary healthcare2025
Attitudes and Readiness for Artificial Intelligence Adoption Among Nursing Students in Saudi Arabia: A Cross-Sectional Study.
Article in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Healthcare Providers' Perceptions and Multi-Level Determinants of Adoption of an AI-Powered Electrocardiography Interpretation Clinical Decision Support System in Ethiopia: A Formative Qualitative Study.International journal of environmental research and public health · 2026Article
- Evaluating AI adoption challenges in healthcare using a Multi-Criteria Decision-Making approach: implications for predictive risk analytics.Frontiers in artificial intelligence · 2026Article
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2 authors.
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Abstract
Purpose: Although artificial intelligence (AI) has garnered increasing attention in education and healthcare, examining the attitude and readiness of future nurses toward AI integration is crucial for creating successful curricula and promoting appropriate utilization of this technology. Patients and Methods: This cross-sectional study was conducted among 227 nursing students at King Saud University using two validated instruments: the General Attitudes toward Artificial Intelligence Scale and the Medical Artificial Intelligence Readiness Scale for Medical Students. Responses were recorded using a 5-point Likert scale. Descriptive and inferential statistics, including t-tests and correlation analyses, were employed to examine differences across study variables. Results: Of the 227 participants, 48% (n = 109) were aged 18-24 years. Majority participants were women (60.4%, n = 137), and 39.6% (n = 90) were men. Nursing students generally demonstrated moderately positive attitudes toward AI. However, an independent-sample Conclusion: The findings of this study may help nursing colleges to incorporate AI into their curricula. However, the study's cross-sectional design, single site setting, and short duration limit causal inference and generalizability.
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