Background: Nursing students often produce care plans that are plausible yet generic, with weak linkage among diagnoses, interventions and outcomes. Teacher-mediated, bounded generative AI may support revision while preserving clinical judgement. Aims: To assess whether an AI-supported Reflect–Compare–Revise workflow improves nursing care plan quality and diagnosis–intervention–outcome alignment using a standardised newborn vignette. Methods: A one-group pretest–posttest study involved 40 second-year nursing students at a public university in the Philippines. Students wrote an unaided care plan, then revised it using the structured workflow. Two blinded nurse-educator raters scored paired plans using the Quality of Diagnoses, Interventions and Outcomes instrument. Results: Post-revision documentation quality improved across all domains, with the largest raw gains in diagnostic formulation and nursing-sensitive outcomes. Interventions and outcomes remained the weaker downstream domains, with outcomes lowest at posttest. Diagnosis–intervention–outcome alignment and specificity increased, although explicit cross-linking remained inconsistent. Conclusion: This study adds preliminary evidence for nursing education: higher scores after guided AI-supported revision were associated with clearer and more coherent clinical reasoning in care planning. Policy recommendations are to permit bounded AI use with standardised prompts, revision logs/checklists, rubric-based feedback and verification safeguards to protect documentation integrity and patient safety.
No takes yet. Share an insight, caveat, or question.
Funa et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: