Evidence map›Paper›PMID 41620736›Full record

ArticleBMC nursing2026

Generative AI adaptive narratives to enhance nursing diagnostic reasoning: a classroom innovation.

María José Ferreira Díaz

Abstract read
In one paragraph

Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

María José Ferreira DíazSchool of Nursing, University of Santiago de Compostela, Lugo, Spain. mariajose.ferreira@usc.es.ORCID http://orcid.org/0009-0001-9046-9560

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNursing students often struggle to apply standardized nursing languages (NANDA-I, NIC, NOC) in a coherent and context-sensitive way. Narrative pedagogy supports clinical reasoning, but traditional patient narratives are static and offer limited variability. Generative artificial intelligence (AI) provides new opportunities to create adaptive, cue-rich simulations grounded in authentic patient experiences.

objectiveTo develop and pilot-test an instructional design using generative AI–enhanced narrative simulations to support undergraduate nursing students’ diagnostic reasoning and NANDA–NIC–NOC linkage.

methodsA mixed-methods pilot study was conducted with second-year nursing students (N = 46). Authentic coronary patient narratives were transformed into adaptive simulated cases using a structured generative AI pipeline. Students were randomly assigned to a control group (static narrative) or an AI-enhanced group (adaptive narratives). Outcomes included diagnostic accuracy, NIC–NOC coherence, self-efficacy, and qualitative reflections.

resultsStudents in the AI-enhanced group demonstrated significantly higher diagnostic accuracy, stronger NIC–NOC coherence, and greater self-efficacy than the control group (all p < .001, large effect sizes). Qualitative analysis identified enhanced cue sensitivity, iterative refinement of diagnostic decisions, and increased perceived authenticity as key learning mechanisms.

conclusionGenerative AI–enhanced narrative simulation is a feasible and pedagogically valuable approach for strengthening nursing diagnostic reasoning using standardized nursing languages. By introducing controlled narrative variability grounded in real patient experiences, this method supports context-aware and reflective clinical reasoning in undergraduate nursing education. IMPLICATIONS FOR NURSING EDUCATION: Generative AI expands narrative pedagogy by enabling educators to create an unlimited range of realistic, ethically sound and cue-rich scenarios for teaching nursing diagnostic accuracy, clinical reasoning and patient-centred care planning. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Artificial intelligenceClinical reasoningEducation, NursingNursing processSimulation trainingStandardized nursing terminology.

Identifiers

PMID41620736
PMCPMC12947319

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.