Factors influencing registered nurses’ attitudes toward and literacy in artificial intelligence: a cross-sectional systematic review
- 1The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- 2Acute and Critical Care Nursing Innovation and Translation Engineering Research Center of Chongqing Education Commission of China, Chongqing, China
- 3Chongqing Geriatric Specialized Nursing Clinical Diagnosis and Treatment Center, Chongqing, China
Abstract
BackgroundPresently, artificial intelligence technology is being extensively applied across clinical nursing practice. However, whether this technology can be deeply integrated into clinical workflows depends largely on nurses’ attitudes and literacy regarding artificial intelligence. Consequently, conducting an in depth exploration of core factors shaping registered nurses’ artificial intelligence attitudes and literacy holds significant importance for driving the rapid development of intelligent nursing.MethodsThis study strictly adhered to PRISMA guidelines. Systematic searches were conducted across seven English databases, namely PubMed, Web of Science, Medline, Embase, The Cochrane Library, IEEE Xplore, and CINAHL, alongside three Chinese databases, including China National Knowledge Infrastructure (CNKI), Wanfang Data, and VIP Database. The search timeframe ranged from database inception through May 30, 2026. Following screening according to inclusion and exclusion criteria, independent factors influencing attitudes and literacy were extracted. Employing a narrative synthesis approach, these influencing factors were categorized, summarized, and narratively integrated across distinct thematic domains.ResultsA total of 30 studies involving 12,958 registered nurses were included in this systematic review. The majority of the studies were conducted in China and Turkey. The independent factors influencing registered nurses’ attitudes toward artificial intelligence included educational attainment, age, length of service, gender, professional title/nursing level, hospital tier/category, AI awareness/understanding/knowledge, AI training experience, AI usage experience, artificial intelligence anxiety, and digital leadership. The factors independently affecting nurses’ artificial intelligence literacy included leadership style, AI training experience, and usage experience/frequency.ConclusionCurrent evidence indicates that key modifiable factors to improve nurses’ attitudes toward and literacy in artificial intelligence include AI-related training, practical hands-on experience, and organizational leadership, while technical anxiety should not be overlooked. Compared with demographic characteristics, educational and experiential interventions appear more effective in enhancing nurses’ artificial intelligence literacy levels. Nursing managers should actively implement smart training programs, increase opportunities for nurses to apply new technologies in clinical practice, and formulate relevant policies regarding data privacy protection and accountability division, thereby promoting the safe and effective integration of AI technology into patient care.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/recorddashboard
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BibTeX
@article{dai2026factors,
title = {{Factors influencing registered nurses’ attitudes toward and literacy in artificial intelligence: a cross-sectional systematic review}},
author = {Yingqi Dai and Xili Zhao and Xili Zhao and Xili Zhao and Xiao Pan and Jiaxin Wei},
journal = {Frontiers in Public Health},
year = {2026},
month = oct,
doi = {10.3389/fpubh.2026.1997701},
url = {https://doi.org/10.3389/fpubh.2026.1997701},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Factors influencing registered nurses’ attitudes toward and literacy in artificial intelligence: a cross-sectional systematic review
AU - Yingqi Dai
AU - Xili Zhao
AU - Xili Zhao
AU - Xili Zhao
AU - Xiao Pan
AU - Jiaxin Wei
PY - 2026
DA - 2026/10/23
JO - Frontiers in Public Health
DO - 10.3389/fpubh.2026.1997701
UR - https://doi.org/10.3389/fpubh.2026.1997701
AB - BackgroundPresently, artificial intelligence technology is being extensively applied across clinical nursing practice. However, whether this technology can be deeply integrated into clinical workflows depends largely on nurses’ attitudes and literacy regarding artificial intelligence. Consequently, conducting an in depth exploration of core factors shaping registered nurses’ artificial intelligence attitudes and literacy holds significant importance for driving the rapid development of intelligent nursing.MethodsThis study strictly adhered to PRISMA guidelines. Systematic searches were conducted across seven English databases, namely PubMed, Web of Science, Medline, Embase, The Cochrane Library, IEEE Xplore, and CINAHL, alongside three Chinese databases, including China National Knowledge Infrastructure (CNKI), Wanfang Data, and VIP Database. The search timeframe ranged from database inception through May 30, 2026. Following screening according to inclusion and exclusion criteria, independent factors influencing attitudes and literacy were extracted. Employing a narrative synthesis approach, these influencing factors were categorized, summarized, and narratively integrated across distinct thematic domains.ResultsA total of 30 studies involving 12,958 registered nurses were included in this systematic review. The majority of the studies were conducted in China and Turkey. The independent factors influencing registered nurses’ attitudes toward artificial intelligence included educational attainment, age, length of service, gender, professional title/nursing level, hospital tier/category, AI awareness/understanding/knowledge, AI training experience, AI usage experience, artificial intelligence anxiety, and digital leadership. The factors independently affecting nurses’ artificial intelligence literacy included leadership style, AI training experience, and usage experience/frequency.ConclusionCurrent evidence indicates that key modifiable factors to improve nurses’ attitudes toward and literacy in artificial intelligence include AI-related training, practical hands-on experience, and organizational leadership, while technical anxiety should not be overlooked. Compared with demographic characteristics, educational and experiential interventions appear more effective in enhancing nurses’ artificial intelligence literacy levels. Nursing managers should actively implement smart training programs, increase opportunities for nurses to apply new technologies in clinical practice, and formulate relevant policies regarding data privacy protection and accountability division, thereby promoting the safe and effective integration of AI technology into patient care.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/recorddashboard
SN - 2296-2565
ER -