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. 2025 Jun;72(2):e70018.
doi: 10.1111/inr.70018.

A systematic review and sequential explanatory synthesis: Artificial intelligence in healthcare education, a case of nursing

S Aslı Bozkurt  1 , Sinan Aydoğan  2 , Fatma Dursun Ergezen  3 , Aykut Türkoğlu  4
Affiliations

A systematic review and sequential explanatory synthesis: Artificial intelligence in healthcare education, a case of nursing

S Aslı Bozkurt et al. Int Nurs Rev. 2025 Jun.

Abstract

Aim: This review aims to explore the impact of artificial intelligence (AI) on knowledge acquisition, skills development, and attitudes among nursing students.

Background: AI offers hopeful opportunities to enhance learning experiences and prepare future healthcare professionals.

Methods: This was a sequential explanatory mixed-method systematic review. This review was conducted in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive electronic database was searched to identify relevant studies. Eligibility criteria were studies examining the impacts of AI interventions on nursing students' knowledge, skills, or attitudes. The methodological quality of the studies was assessed using the mixed-method appraisal tool.

Results: Nine research articles were included in the review. These studies utilized both quantitative and qualitative methodologies to examine the impact of AI on nursing students. Quantitative studies found positive relations between AI interventions and knowledge acquisition, skills development, and attitudes toward AI among nursing students. Qualitative findings revealed the positive outcomes of AI in fostering learning engagement, self-efficacy, and confidence.

Conclusions: AI shows potential for supporting knowledge acquisition, skills development, and fostering positive attitudes among nursing students.

Implications for nursing practice and nursing policy: AI-driven interventions enhance nursing education by improving clinical decision-making, confidence, and knowledge acquisition. By integrating AI, nurse educators can develop more interactive, personalized, and impactful learning environments that may help students with the complexities of contemporary healthcare. Policies, standardized guidelines, and faculty development programs may be developed that can promote ethical AI integration, equitable access, and faculty training. These changes can be considered essential to maximize AI's benefits.

Keywords: artificial intelligence; attitude; knowledge acquisition; learning; nursing education; skill development; systematic review.

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Conflict of interest statement

No conflict of interest has been declared by the author(s).

Figures

FIGURE 1
FIGURE 1
PRISMA diagram.
See this image and copyright information in PMC

References

    1. Abujaber, A.A. , Abd‐Alrazaq, A. , Al‐Qudimat, A.R. & Nashwan, A.J. (2023) A strengths, weaknesses, opportunities, and threats (SWOT) analysis of ChatGPT integration in nursing education: a narrative review. Cureus, 15 (11), e48643. 10.7759/cureus.48643 - DOI - PMC - PubMed
    1. Alzahrani, N. , Davis, R.L. , Reangsing, C. & Oerther, S. (2023) An Ignatian approach to incorporating artificial intelligence into nursing curricula. Nurse Education in Practice, 68, 103608. 10.1016/j.nepr.2023.103608 - DOI - PubMed
    1. American Association of Colleges of Nursing (2023) New data show enrollment declines in schools of nursing, raising concerns about the nation's nursing workforce . May 2. [Accessed 17th February 2025]. Available at: https://www.aacnnursing.org/news‐data/all‐news/new‐data‐show‐enrollment‐...
    1. Baashar, Y. , Alkawsi, G. , Mustafa, A. , Alkahtani, A.A. , Alsariera, Y.A. , Ali, A.Q. , Hashim, W. & Tiong, S.K. (2022) Toward predicting student's academic performance using artificial neural networks (ANNs). Applied Sciences, 12 (3), 1289. 10.3390/app12031289 - DOI
    1. Bloom, B.S. & Krathwohl, D.R. (1984) Taxonomy of educational objectives: the classification of educational goals. Michigan: David McKay Company.

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