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Background <p>Nursing 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.</p> Objective <p>To 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.</p> Methods <p>A mixed-methods pilot study was conducted with second-year nursing students (<i>N</i> = 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.</p> Results <p>Students in the AI-enhanced group demonstrated significantly higher diagnostic accuracy, stronger NIC–NOC coherence, and greater self-efficacy than the control group (all <i>p</i> &lt; .001, large effect sizes). Qualitative analysis identified enhanced cue sensitivity, iterative refinement of diagnostic decisions, and increased perceived authenticity as key learning mechanisms.</p> Conclusion <p>Generative 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.</p> Implications for nursing education <p>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.</p> Clinical trial number <p>Not applicable.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月31日

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  • María José Ferreira Díaz

摘要

Background

Nursing 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.

Objective

To 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.

Methods

A 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.

Results

Students 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.

Conclusion

Generative 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.