From Academic Integrity to Professional Formation in Generative AI-Supported Nursing Education: Integrative Review.
- 1College of Healthcare Sciences, James Cook University, 1 James Cook Drive, Douglas, Queensland, 4811, Australia, 61 0404225612.
Abstract
Background
The rapid integration of generative AI (GenAI) into nursing education has prompted widespread concern about academic integrity and assessment validity. However, this dominant discourse may obscure a more fundamental challenge: preparing nursing students for clinical practice environments where AI already mediates triage, diagnostics, documentation, and clinical decision-making.Objective
This integrative review examines how GenAI is conceptualized within the nursing education literature and investigates the extent to which current discourse addresses professional formation rather than academic compliance.Methods
Following Whittemore and Knafl's integrative review methodology, a systematic search of PubMed, Google Scholar, and SciSpace (Typeset) was conducted for literature published between January 2022 and February 2026. Two reviewers independently screened 456 records, with 119 papers meeting the inclusion criteria. The methodological quality of empirical studies was assessed using the Mixed Methods Appraisal Tool (MMAT). Corpus-level descriptive mapping quantified dominant themes and conceptual patterns, while an interpretive synthesis examined how GenAI was framed and the extent to which professional formation elements, including identity, moral agency, and clinical judgment, were addressed.Results
Of the 119 unique studies analyzed, 84% (n=100) predominantly framed GenAI as a content-generation tool and academic integrity threat. Only 35.3% (n=42) conceptualized AI as a pedagogical tool, and a critical gap was identified in professional formation: identity formation was addressed in only 10% (n=12) of papers and moral agency in just 5% (n=6). Explicit acknowledgment that AI already routinely mediates frontline clinical decisions (eg, sepsis alerts and fall risk algorithms) in current practice settings was notably absent. The discourse emphasizes governance and faculty literacy, but sustained curricula addressing nurse identity in AI-mediated care remain underdeveloped.Conclusions
Current nursing education discourse predominantly frames AI as a writing tool and an academic integrity concern, failing to prepare students for the algorithmic practice environments they will inhabit. Nursing education must reframe AI as a professional formation issue, explicitly addressing how algorithms reshape clinical judgment, moral agency, and professional identity.
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BibTeX
@article{hartin2026from,
title = {{From Academic Integrity to Professional Formation in Generative AI-Supported Nursing Education: Integrative Review.}},
author = {Peter Hartin and Paul Baretta},
journal = {JMIR nursing},
year = {2026},
month = oct,
doi = {10.2196/99284},
url = {https://europepmc.org/article/MED/42962069},
note = {CC BY},
}
RIS
TY - JOUR
TI - From Academic Integrity to Professional Formation in Generative AI-Supported Nursing Education: Integrative Review.
AU - Peter Hartin
AU - Paul Baretta
PY - 2026
DA - 2026/10/01
JO - JMIR nursing
DO - 10.2196/99284
UR - https://europepmc.org/article/MED/42962069
AB - Background The rapid integration of generative AI (GenAI) into nursing education has prompted widespread concern about academic integrity and assessment validity. However, this dominant discourse may obscure a more fundamental challenge: preparing nursing students for clinical practice environments where AI already mediates triage, diagnostics, documentation, and clinical decision-making.Objective This integrative review examines how GenAI is conceptualized within the nursing education literature and investigates the extent to which current discourse addresses professional formation rather than academic compliance.Methods Following Whittemore and Knafl's integrative review methodology, a systematic search of PubMed, Google Scholar, and SciSpace (Typeset) was conducted for literature published between January 2022 and February 2026. Two reviewers independently screened 456 records, with 119 papers meeting the inclusion criteria. The methodological quality of empirical studies was assessed using the Mixed Methods Appraisal Tool (MMAT). Corpus-level descriptive mapping quantified dominant themes and conceptual patterns, while an interpretive synthesis examined how GenAI was framed and the extent to which professional formation elements, including identity, moral agency, and clinical judgment, were addressed.Results Of the 119 unique studies analyzed, 84% (n=100) predominantly framed GenAI as a content-generation tool and academic integrity threat. Only 35.3% (n=42) conceptualized AI as a pedagogical tool, and a critical gap was identified in professional formation: identity formation was addressed in only 10% (n=12) of papers and moral agency in just 5% (n=6). Explicit acknowledgment that AI already routinely mediates frontline clinical decisions (eg, sepsis alerts and fall risk algorithms) in current practice settings was notably absent. The discourse emphasizes governance and faculty literacy, but sustained curricula addressing nurse identity in AI-mediated care remain underdeveloped.Conclusions Current nursing education discourse predominantly frames AI as a writing tool and an academic integrity concern, failing to prepare students for the algorithmic practice environments they will inhabit. Nursing education must reframe AI as a professional formation issue, explicitly addressing how algorithms reshape clinical judgment, moral agency, and professional identity.
SN - 2562-7600
ER -