The state of artificial intelligence in nursing education: past, present, and future directions
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As health care continues to evolve and become increasingly complex, nursing education must also evolve to keep pace with the changing landscape [...].
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AI systems have evolved from early computer-assisted learning and simulation platforms to adaptive learning environments, virtual tutors and generative large language models.
“AI systems, defined as software capable of supporting logical and informed judgments [9], have evolved from early computer-assisted learning and simulation platforms to adaptive learning environments, virtual tutors and generative large language models (LLMs) [10,11].”
From Artificial Intelligence–Enabled Educational Tools and Clinical Reasoning in Undergraduate Nursing Education: A Systematic Review (2026) · cites this paper as [11]
The educational intervention was guided by Bloom's Taxonomy of Learning, a hierarchy of cognitive domains associated with learning.
“The educational intervention was guided by Bloom's Taxonomy of Learning, which describes a hierarchy of cognitive domains associated with learning.28”
From Increasing Generative Artificial Intelligence Competency among Students Enrolled in Doctoral Nursing Research Coursework (2024) · cites this paper as [28]
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“China has launched a national AI development plan to achieve significant progress in AI research and innovation by 2030 [6].”
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- Authors
- De Gagne, J. C.
- Affiliations
- School of Nursing, Duke University, 307 Trent Drive, DUMC 3322, Durham, NC 27710, USA
- ORCID
- De Gagne, Jennie C.
- Venue
- International Journal of Environmental Research and Public Health
- Publisher
- MDPI AG
- Published
- 2023-03-10
- Volume
- 20
- Issue
- 6
- Pages
- 4884
- ISSN
- 1660-4601 (ISSN-L)
- PMC
- PMC10049425
- OpenAlex
- W4323846876
- Cited by
- 191
- References
- 5
- Citations by year
- 2026: 57 · 2025: 79 · 2024: 43 · 2023: 11 · 2021: 1
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- 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, educational technology, future of nursing education
- MeSH terms
- Artificial Intelligence, Delivery of Health Care, Education, Nursing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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