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Integration of artificial intelligence in nursing education: a cross-national exploration.
BMC Nurs. 2025 Aug 05; 24(1):1026.BN

Abstract

BACKGROUND

Understanding nurse educators' perceptions of Artificial Intelligence (AI) in education, particularly cross-nationally and considering cultural and exposure influences, remains limited. This study addressed this gap by determining the impact of AI integration in nursing education.

METHODS

Participants included 1021 nurse educators from the Philippines, Saudi Arabia, India, and Egypt, with data collected between January and March 2025.

RESULTS

Perceived benefits of AI showed consistent means across countries, though significant differences (p < 0.001) were observed in perceived threats, exposure, and the influence of culture by country. Strong positive correlations emerged between exposure to AI and both trust in AI (r = 0.653) and perceived benefits (r = 0.625). Nationality significantly predicted perceived risks, exposure, and cultural impact (all p < 0.001), emphasizing the relevance of cultural background to AI applicability.

CONCLUSION

Nurse educators acknowledge AI's educational potential but show diverse perceptions regarding risks, exposure, and cultural impact. Increased AI exposure fosters trust and perceived benefits, highlighting the need for contextualized, culturally-attuned, and exposure-driven AI integration in nursing education.

Links

PMC Free PDF

Authors+Show Affiliations

Almalki M
College of Nursing, Taif University, P.O. Box 11099, Taif, 21944, Kingdom of Saudi Arabia.
Sacgaca L
College of Nursing, Taif University, P.O. Box 11099, Taif, 21944, Kingdom of Saudi Arabia.
Pangket P
College of Nursing, Taif University, P.O. Box 11099, Taif, 21944, Kingdom of Saudi Arabia.
Pasay-An E
College of Nursing, King Khalid University, Abha, Saudi Arabia.
Deep SH
Department of Nursing, Prince Sultan Military College of Health Sciences, Al Amal Dhahran City, 34313, Saudi Arabia.
Maskay G
School of Healthcare Education, Mountain Province State University, Bontoc, Mountain Province, Philippines.
Benjamin LS
College of Nursing, King Khalid University, Abha, Saudi Arabia.
Hashim SMA
College of Nursing, King Khalid University, Abha, Saudi Arabia.
Gonzales A
Faculty of Nursing, University of Tabuk, Tabuk City, Saudi Arabia.
Rosal R
Research, Planning and Development Center, Virgen Milagrosa University Foundation, Inc., San Carlos City, Pangasinan, Philippines.
Mohsen M
College of Nursing, King Khalid University, Abha, Saudi Arabia.
Mostoles R
College of Nursing, University of Hail, Hail City, Saudi Arabia. rpmostolesjr@gmail.com.
Lagura GAL
College of Nursing, University of Hail, Hail City, Saudi Arabia.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

40764580

Citation

Almalki, Mohammed, et al. "Integration of Artificial Intelligence in Nursing Education: a Cross-national Exploration." BMC Nursing, vol. 24, no. 1, 2025, p. 1026.
Almalki M, Sacgaca L, Pangket P, et al. Integration of artificial intelligence in nursing education: a cross-national exploration. BMC Nurs. 2025;24(1):1026.
Almalki, M., Sacgaca, L., Pangket, P., Pasay-An, E., Deep, S. H., Maskay, G., Benjamin, L. S., Hashim, S. M. A., Gonzales, A., Rosal, R., Mohsen, M., Mostoles, R., & Lagura, G. A. L. (2025). Integration of artificial intelligence in nursing education: a cross-national exploration. BMC Nursing, 24(1), 1026. https://doi.org/10.1186/s12912-025-03689-3
Almalki M, et al. Integration of Artificial Intelligence in Nursing Education: a Cross-national Exploration. BMC Nurs. 2025 Aug 5;24(1):1026. PubMed PMID: 40764580.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Integration of artificial intelligence in nursing education: a cross-national exploration. AU - Almalki,Mohammed, AU - Sacgaca,Lailani, AU - Pangket,Petelyne, AU - Pasay-An,Eddieson, AU - Deep,Shereen Hussein, AU - Maskay,Georgina, AU - Benjamin,Lizy Sonia, AU - Hashim,Sahar Mahmoud Abdulla, AU - Gonzales,Analita, AU - Rosal,Rosalyn, AU - Mohsen,Maysa, AU - Mostoles,Romeo, AU - Lagura,Grace Ann Lim, Y1 - 2025/08/05/ PY - 2025/4/28/received PY - 2025/7/30/accepted PY - 2025/8/6/medline PY - 2025/8/6/pubmed PY - 2025/8/5/entrez PY - 2025/8/5/pmc-release KW - Artificial intelligence KW - Cross-cultural studies KW - Faculty KW - Nursing KW - Nursing education KW - Perception KW - Risk assessment SP - 1026 EP - 1026 JF - BMC nursing JO - BMC Nurs VL - 24 IS - 1 N2 - BACKGROUND: Understanding nurse educators' perceptions of Artificial Intelligence (AI) in education, particularly cross-nationally and considering cultural and exposure influences, remains limited. This study addressed this gap by determining the impact of AI integration in nursing education. METHODS: Participants included 1021 nurse educators from the Philippines, Saudi Arabia, India, and Egypt, with data collected between January and March 2025. RESULTS: Perceived benefits of AI showed consistent means across countries, though significant differences (p < 0.001) were observed in perceived threats, exposure, and the influence of culture by country. Strong positive correlations emerged between exposure to AI and both trust in AI (r = 0.653) and perceived benefits (r = 0.625). Nationality significantly predicted perceived risks, exposure, and cultural impact (all p < 0.001), emphasizing the relevance of cultural background to AI applicability. CONCLUSION: Nurse educators acknowledge AI's educational potential but show diverse perceptions regarding risks, exposure, and cultural impact. Increased AI exposure fosters trust and perceived benefits, highlighting the need for contextualized, culturally-attuned, and exposure-driven AI integration in nursing education. SN - 1472-6955 UR - https://www.unboundmedicine.com/medline/citation/40764580/Integration_of_artificial_intelligence_in_nursing_education:_a_cross-national_exploration DB - PRIME DP - Unbound Medicine ER -
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