JMIR Publications

ABSTRACT

Background:

The rapid integration of generative artificial intelligence (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 generative AI is conceptualized within 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 was conducted for literature published between January 2022 and January 2026. Two reviewers independently screened 456 records, with 119 papers meeting inclusion criteria. Methodological quality of empirical studies was assessed using the Mixed Methods Appraisal Tool. Corpus-level descriptive mapping quantified dominant themes and conceptual patterns, and interpretive synthesis examined how generative AI 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 35.2% (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). E xplicit acknowledgment that AI already routinely mediates frontline clinical decisions (eg, sepsis alerts, falls 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 predominately frames AI as a writing tool and 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.