When the algorithm enters the classroom: A critical integrative review of large language models, nursing education structural gaps, and the reconstitution of professional identity

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Large language models (LLMs) are being integrated into nursing education at a pace that outstrips the field's capacity to evaluate their consequences. Existing syntheses have mapped what LLMs can do in nursing contexts. Among those identified in this review, none has examined how their integration affects the professional development processes through which nursing expertise is formed. This critical integrative review addresses that gap.Following Whittemore and Knafl’s (2005) five-stage framework, we systematically searched four databases (PubMed/MEDLINE, Web of Science, Scopus, CINAHL) for studies published between January 2017 and December 2025, yielding 489 included studies from 47 countries/regions. Two reviewers independently screened all 1182 records; inter-rater agreement was κ = 0.67 at the title and abstract stage and κ = 0.78 at the full-text stage. Findings were analysed through an integrated theoretical framework combining Benner's model of professional skill acquisition, cognitive load theory (Sweller, 1988), automation bias (Parasuraman & Manzey, 2010), and Wenger's participatory account of identity formation.Three principal findings emerged. First, LLMs address real structural gaps in nursing education—including deficits in individualised learning support and clinical simulation access—but their benefits are consistently moderated by implementation framework, economic equity, and model-specific accuracy limitations. Second, LLM integration is associated with both competency enhancement and competency attrition, depending on whether the displaced cognitive work is extraneous to or constitutive of the competence being developed; unstructured AI reliance was associated with measurable deficits in ethical reasoning and individualised clinical judgement. Third, an Evidence Gap Map of the 489-study corpus shows that the domains of greatest policy consequence are supported by the least rigorous evidence. We identified no randomised or quasi-experimental studies of professional identity development, only three quasi-experimental studies of relational and ethical competency, and no studies with follow-up beyond 12 months, whereas controlled evidence is concentrated in cognitive and technical outcomes.This review introduces the Professional Identity Tension Model as a preliminary conceptual framework for distinguishing LLM integration that supports professional formation from integration that silently substitutes for it, and proposes the concept of Structural Empathy Suppression to reframe the conditions under which AI appears to outperform nurses on relational metrics. LLMs in nursing education both fill structural gaps and shape professional identity; the field now requires research designs and implementation frameworks capable of distinguishing between the two.