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Cited by 58Artificial Intelligence in Healthcare and EducationHealthcare Education and Workforce IssuesHealth Sciences Research and Education

Embracing artificial intelligence in nursing: exploring the relationship between artificial intelligence-related attitudes, creative self-efficacy, and clinical reasoning competency among nurses

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Atalla, Amal Diab Ghanem, El-Gawad Mousa, Marwa Abd, Hashish, Ebtsam Aly Abou, Elseesy, Naglaa Abdelaziz Mahmoud, Abd El kader Mohamed, Aziza Ibrahim, Sobhi Mohamed, Samia Mohamed·2025·BMC Nursing
DOI 10.1186/s12912-025-03306-3PMC PMC12181922
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Abstract

Background: As artificial intelligence (AI) becomes an integral part of healthcare, nursing practice is rapidly evolving, requiring a deeper understanding of how nurses’ attitudes toward AI influence essential competencies such as creative self-efficacy and clinical reasoning competency, both of which are crucial for delivering safe and effective patient care. Aim: This study aimed to explore the relationship between nurses’ AI-related attitudes, creative self-efficacy, and clinical reasoning competency. Methods: A cross-sectional descriptive-correlational design was employed, involving a convenience sample of 380 nurses working in critical care units at a university-affiliated hospital in Egypt. Data were collected using three validated instruments: the Nurses’ Artificial Intelligence Attitudes Scale, the Creative Self-Efficacy Scale, and the Clinical Reasoning Competency Scale. Data analysis included both descriptive and inferential statistics. Results: The majority of nurses demonstrated high levels of AI-related attitudes and clinical reasoning competency, while moderate levels of creative self-efficacy were observed. A strong positive correlation was found between AI attitudes and both creative self-efficacy and clinical reasoning competency ( r = 0.559 and r = 0.728, p < 0.001, respectively). Regression analysis confirmed that AI attitudes were significant predictors of both creative self-efficacy and clinical reasoning competency, explaining 37.4% and 56.5% of their variance, respectively. Additionally, educational qualifications and years of nursing experience were identified as significant factors influencing these competencies. Conclusion and Implications: Positive attitudes toward artificial intelligence (AI) play a crucial role in enhancing nurses’ creative self-efficacy and clinical reasoning competency. Therefore, fostering positive perceptions of AI and providing targeted training are vital to prepare nurses for AI-integrated clinical environments. Integrating AI-focused content into nursing education and promoting continuous professional development are key strategies to strengthen nurses’ readiness to engage with AI-driven healthcare. Additionally, healthcare organizations and nursing leaders should create supportive environments that encourage AI adoption while preserving the principles of patient-centered care. Clinical trial number: Not applicable.

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  • AI-supported decision-support tools are designed to assist clinicians in recognizing clinical deterioration, processing complex information, prioritizing care actions, and reducing cognitive workload during time-sensitive situations.

    “Such systems are designed to assist clinicians in recognizing clinical deterioration, processing complex information, prioritizing care actions, and reducing cognitive workload during time-sensitive situations [1].”

    From Artificial Intelligence-Based Adaptive Simulation Integrated With a Workforce Decision-Support System to Improve Simulated Nursing Leadership Performance: A Randomized Controlled Study (2026) · cites this paper as [1]

    The Literature review (AI and AI-related attitudes; no page provided) says AI-based decision-support systems analyze patient data in real time, offer recommendations to prevent deterioration, and help nurses make timely decisions. The Theoretical underpinning section says AI can analyze complex data and reduce nurses’ cognitive load. The paper discusses selecting appropriate interventions, but does not specifically say these tools prioritize care actions; its discussion is chiefly about nurses rather than clinicians generally. (LLM, conf=0.82)

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Cited by

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Authors
Atalla, A. D. G., El-Gawad Mousa, M. A., Hashish, E. A. A., Elseesy, N. A. M., Abd El kader Mohamed, A. I., & Sobhi Mohamed, S. M.
Affiliations
Alexandria University (EG)
Corresponding author
Atalla, Amal Diab Ghanem
ORCID
Venue
BMC Nursing
Publisher
Springer Science and Business Media LLC
Published
2025-06-20
Volume
24
Issue
1
Pages
661-661
ISSN
1472-6955 (ISSN-L)
DOI
10.1186/s12912-025-03306-3
PMC
PMC12181922
OpenAlex
W4411493818
Cited by
58
References
9
Citations by year
2026: 46 · 2025: 12
FWCI
13.86
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Nursing (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Alexandria University
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Nursing › Leadership and Management › Healthcare Education and Workforce Issues
  • Health Sciences › Health Professions › General Health Professions › Health Sciences Research and Education
Keywords
artificial intelligence in nursing, creative self-efficacy, critical care nursing, nursing education, AI adoption in healthcare, nursing experience, academic qualifications

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