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Cited by 327Artificial Intelligence in Healthcare and EducationArtificial Intelligence in EducationInnovations in Medical Education

Predicted Influences of Artificial Intelligence on Nursing Education: Scoping Review

Open access
Buchanan, Christine, Howitt, M Lyndsay, Wilson, Rita, Booth, Richard G, Risling, Tracie, Bamford, Megan·2021·JMIR Nursing
DOI 10.2196/23933PMC PMC8328269

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Abstract

Background: It is predicted that artificial intelligence (AI) will transform nursing across all domains of nursing practice, including administration, clinical care, education, policy, and research. Increasingly, researchers are exploring the potential influences of AI health technologies (AIHTs) on nursing in general and on nursing education more specifically. However, little emphasis has been placed on synthesizing this body of literature. Objective: A scoping review was conducted to summarize the current and predicted influences of AIHTs on nursing education over the next 10 years and beyond. Methods: This scoping review followed a previously published protocol from April 2020. Using an established scoping review methodology, the databases of MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Centre, Scopus, Web of Science, and Proquest were searched. In addition to the use of these electronic databases, a targeted website search was performed to access relevant grey literature. Abstracts and full-text studies were independently screened by two reviewers using prespecified inclusion and exclusion criteria. Included literature focused on nursing education and digital health technologies that incorporate AI. Data were charted using a structured form and narratively summarized into categories. Results: A total of 27 articles were identified (20 expository papers, six studies with quantitative or prototyping methods, and one qualitative study). The population included nurses, nurse educators, and nursing students at the entry-to-practice, undergraduate, graduate, and doctoral levels. A variety of AIHTs were discussed, including virtual avatar apps, smart homes, predictive analytics, virtual or augmented reality, and robots. The two key categories derived from the literature were (1) influences of AI on nursing education in academic institutions and (2) influences of AI on nursing education in clinical practice. Conclusions: Curricular reform is urgently needed within nursing education programs in academic institutions and clinical practice settings to prepare nurses and nursing students to practice safely and efficiently in the age of AI. Additionally, nurse educators need to adopt new and evolving pedagogies that incorporate AI to better support students at all levels of education. Finally, nursing students and practicing nurses must be equipped with the requisite knowledge and skills to effectively assess AIHTs and safely integrate those deemed appropriate to support person-centered compassionate nursing care in practice settings. International Registered Report Identifier (IRRID): RR2-10.2196/17490

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  • By simulating realistic scenarios, these technologies bridge the gap between theoretical knowledge and practical application, allowing students to refine their skills in controlled, feedback-rich settings.

    “By simulating realistic scenarios, these technologies bridge the gap between theoretical knowledge and practical application, allowing students to refine their skills in controlled, feedback-rich settings [36,47].”

    From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [47]

    The Results section (Influences of AI on Nursing Education in Academic Institutions; page unavailable) describes interactive clinical scenarios that may improve students’ comprehension and communication skills before they enter clinical practice. It presents these benefits as potential or predicted, however, and does not establish that the scenarios bridge theory and practice or provide feedback-rich settings. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • The absence of standardized AI literacy programs and insufficient integration of AI concepts into nursing curricula limit the preparedness of future nurses to engage effectively with these technologies.

    “The absence of standardized AI literacy programs and insufficient integration of AI concepts into nursing curricula limit the preparedness of future nurses to engage effectively with these technologies [39,47].”

    From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [47]

    The Introduction (Current State) says nursing curricula need to change so future nurses gain the competencies to work with AI, and the Discussion (Key Considerations) calls for substantial reform to prepare them to use it effectively. But the paper does not establish an absence of standardized AI literacy programs; the Introduction instead describes existing informatics competency resources whose uptake is uncertain. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Real-time AI-driven feedback systems support continuous skill improvement, ensuring that nursing graduates are well-prepared for the complexities of modern health care.

    “Moreover, real-time AI-driven feedback systems support continuous skill improvement, ensuring that nursing graduates are well-prepared for the complexities of modern health care [47].”

    From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [47]

    The Results section (page not provided) says AI-supported learning may improve nursing students’ clinical reasoning and help prepare them for technology-rich clinical settings. It also describes tools that assess students during simulations so educators can tailor instruction. It does not establish that real-time AI-driven feedback continuously improves skills or ensures graduates are well prepared; those outcomes are presented as possibilities, not guarantees. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • The most prevalent use of ITS in teaching course content was in medical and scientific fields, such as virtual patient simulators or case studies for nursing, medical or dental students and staff.

    “Most prevalent was the use of ITS in the medical and scientific fields, for example, as virtual patient simulators or case studies to nursing, medical or dental students and staff (Buchanan et al., 2021;Hwang et al., 2022;Saghiri et al., 2022).”

    From A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour (2024) · cites this paper as Buchanan et al., 2021

    The Results (Overview of Articles; page unavailable) describe a virtual patient gaming app used to teach nursing students through virtual case scenarios. The paper does not establish that intelligent tutoring systems (ITS) were most prevalent in medical and scientific fields, or document the claimed use with medical or dental students and staff. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Research on AIEd often overlooks deeper student-centred and learning factors.

    “Despite rapid technical advances, research on AI in Education (AIEd) often overlooks deeper student-centred and learning factors [17, 22].”

    From AI Sensing and Intervention in Higher Education: Student Perceptions of Learning Impacts, Affective Responses, and Ethical Priorities (2026) · cites this paper as [22]

    The Discussion’s Future Research section says more research is needed on the most effective ways to use AI as a tool in nursing education, supporting a narrower claim about gaps in educational research. It does not say that AIEd research generally overlooks deeper student-centred or learning factors, or contrast those factors with technical advances. Page numbers are unavailable in our copy. (LLM, conf=0.72)

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Research works printed but not linked: 2

  1. [12]Kozier BE Berman A Snyder S Buck M Yiu L Stamler L Fundamentals of Canadian Nursing: Concepts, Process, and Practice, Third Canadian Edition 2013 Toronto, Canada Pearson Education Canada
  2. [48]Preparing the healthcare workforce to deliver the digital future NHS Health Education England 2018 2021-01-12 http://allcatsrgrey.org.uk/wp/download/education/medical_education/Topol-Review-interim-report_0.pdf

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Authors
Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., & Bamford, M.
Affiliations
  • Registered Nurses' Association of Ontario (CA)
  • Western University (CA)
  • University of Saskatchewan (CA)
Corresponding author
Buchanan, Christine
ORCID
Venue
JMIR Nursing
Publisher
JMIR Publications Inc.
Published
2021-01-28
Volume
4
Issue
1
Pages
e23933
ISSN
2562-7600 (ISSN-L)
DOI
10.2196/23933
PMC
PMC8328269
OpenAlex
W3118983571
Cited by
326
References
46
Citations by year
2026: 110 · 2025: 117 · 2024: 58 · 2023: 24 · 2022: 12 · 2021: 5
FWCI
2.01
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Nursing (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • University Health Network
  • Registered Nurses' Association of Ontario
  • Associated Medical Services
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
Keywords
artificial intelligence, nursing education, virtual reality, augmented reality, avatars, predictive analytics, nursing robots, home automation, curriculum reform, nursing pedagogy, digital health technologies, nursing students, nursing faculty, clinical practice

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