BMC Medical Education - Article in Press
Attitudes and readiness of nursing students practising in surgical units towards artificial intelligence: a cross-sectional study
Authors: Nihal Celikturk Doruker & Reyhan Hafsa Kara
Received: 22 July 2025 | Accepted: 5 January 2026 | Published Online: 14 January 2026
This analysis provides a concise overview of nursing students' perspectives on Artificial Intelligence in surgical units, highlighting key readiness indicators and areas for educational focus.
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Average AI Readiness Score (MAIRS-MS)
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Believe AI Will Improve Nursing Practice
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Actively Use AI Applications
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Report Knowledge of AI in Surgical Nursing
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AI's Expanding Footprint in Healthcare
Artificial Intelligence is rapidly integrating into healthcare, particularly within technologically advanced surgical fields. This integration spans both virtual applications (e.g., electronic patient records, decision-support algorithms, image processing) and physical applications (e.g., surgical robots, smart prosthetics).
The anticipated benefits include enhanced healthcare quality, cost reduction, improved diagnostics, and more efficient care planning. For nursing, AI influences roles, workflows, and patient relationships, promising to facilitate practices, improve education, and elevate care quality. Ensuring patient safety and privacy remains paramount amidst this technological evolution.
Rigorous Cross-Sectional Methodology
This cross-sectional study, conducted during the spring semester of the 2023-2024 academic year, involved 178 second-year nursing students from a university's Faculty of Nursing who were undertaking clinical practice in surgical units.
Data was collected using a custom Sociodemographic Form, the General Attitude towards Artificial Intelligence Scale (GAASI) for attitudes (positive and negative), and the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) for readiness (cognition, ability, vision, ethics). Statistical analysis included descriptive statistics, t-tests, ANOVA, Mann-Whitney U, Kruskal-Wallis, and Pearson correlation, with p < 0.05 indicating significance. Ethical approval (Protocol No: 2313, January 30, 2024) and informed consent were obtained.
Key Quantitative Findings
The study found a mean positive attitude score (GAASI) of 42.46±6.14 and a negative attitude score of 25.13±5.78. The total mean MAIRS-MS readiness score was 70.75±12.85, with factor scores showing 'ability' as highest (27.63±5.51).
Significantly, students with higher technological competence, better AI understanding, active AI use, and a belief in AI's positive impact on nursing and careers demonstrated more positive attitudes and higher readiness. A strong positive correlation (r=0.790, p<0.001) was observed between positive AI attitudes and overall AI readiness.
Interpreting the Implications for Nursing
The generally positive attitudes and moderate-to-high readiness among nursing students in surgical units align with existing literature, indicating a receptive environment for AI integration. Factors such as AI understanding, active use in academic tasks, and belief in AI's career impact significantly influenced these positive attitudes.
The findings emphasize that cognitive readiness is bolstered by technology proficiency and awareness of surgical-specific AI applications. Furthermore, direct engagement with AI and perception of robotic surgery as an AI application fostered higher ethical preparedness and vision regarding AI's future potential. These insights advocate for proactive curriculum development to leverage existing positive sentiments and address specific knowledge gaps.
Strategic Conclusions for AI Integration
This study concludes that nursing students specializing in surgical units exhibit generally positive attitudes towards Artificial Intelligence and possess a moderate-to-high level of readiness for its adoption. A critical finding is the direct correlation: as positive attitudes towards AI increase, so does their readiness to utilize it effectively.
Therefore, it is strongly recommended to integrate AI technologies into undergraduate nursing education, focus on developing students' technological proficiency, and specifically introduce AI applications relevant to surgical nursing. These strategic interventions are anticipated to enhance both individual professional development and significantly improve the quality and safety of surgical patient care.
Acknowledging Study Constraints
This study's findings should be interpreted within its inherent limitations. As a single-university study, the generalizability of the results may be constrained. The specific timeframe of data collection also means that attitudes towards AI, a rapidly evolving field, might change over time.
Additionally, the reliance on voluntary participation and the self-report nature of the scales introduce potential biases, such as social desirability, where participants might provide responses they perceive as more acceptable rather than fully accurate reflections of their true attitudes or readiness. These factors could partially limit the broader applicability of the study's conclusions.
Enterprise Process Flow: Study Methodology Overview
Study Population (195 second-year nursing students)
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Exclusion (4 non-clinical practice, 13 refused) (17 total)
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Study Sample (178 second-year nursing students)
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Data Collection (Sociodemographic Form, GAASI, MAIRS-MS)
r=0.790
Strong Positive Correlation: Positive AI Attitudes & Readiness (p<0.001)
This study identified a robust positive correlation between nursing students' positive attitudes towards Artificial Intelligence and their overall readiness to integrate AI into surgical nursing practice. This suggests that fostering positive attitudes is key to successful AI adoption.
| Factor | Mean Score | Interpretation |
|---|---|---|
| Cognition | 23.08 / 40 | Understanding fundamental AI concepts and data science. |
| Ability | 27.63 / 40 | Selecting appropriate AI, integrating it, and explaining to patients. |
| Vision | 9.68 / 15 | Evaluating AI's strengths, weaknesses, opportunities, and threats. |
| Ethics | 10.35 / 15 | Compliance with legal and ethical principles in AI use. |
| Total Readiness | 70.75 / 110 | Overall preparedness for AI in medical applications. |
Nursing students demonstrated varied levels of readiness across different dimensions of the MAIRS-MS. The 'Ability' factor scored highest, reflecting confidence in practical AI application, while 'Vision' and 'Ethics' showed solid foundational understanding needing further development.
Educational Imperatives for AI in Surgical Nursing
The findings underscore the critical need to integrate AI education directly into surgical nursing curricula. With high positive attitudes (70.76% average positive attitude score) and a strong belief that AI will improve nursing practice (90.4% of students), there is a receptive environment for learning. However, only 13.5% of students reported knowledge of AI in surgical nursing, highlighting a significant knowledge gap.
Educational strategies should include theoretical grounding in AI, practical exposure to robotic surgery and decision-support systems, and discussions on ethical and legal considerations. This will not only maintain positive attitudes but also bolster their cognitive, ability, vision, and ethical readiness for AI's evolving role.
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