Actionable companion · Motivation & engagement
Learning with agency: Development and effects of a scenario-based labor nursing simulation education program using an artificial intelligence tutor: a quasi-experimental study
AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.
Evidence and context
Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Health-professions learning must be distinguished from K–12 instruction.
Original authors’ abstract, reproduced under the linked source license:
Abstract Purpose This study was conducted to determine the effects of a scenario-based labor nursing simulation education program using an artificial intelligence (AI) tutor on knowledge, critical thinking disposition, clinical performance abilities, and digital literacy. Methods This quasi-experimental study employed a non-equivalent control group pre- and post-test design. Seventy-two fourth-year nursing students (38 experimental, 34 control) were recruited from a university in Gumi, Korea. The experimental group participated in a scenario-based simulation education program supported by an AI tutor, while the control group engaged in conventional high-fidelity simulation training. Both groups attended sessions once per week for 2 hours over five sessions. Data were collected from August to December 2023 and analyzed using descriptive statistics, homogeneity testing, and the independent t-test. Results Simulation using an AI tutor resulted in significantly higher scores for the experimental group compared to the control group in nursing knowledge (t=7.03, p<.001), clinical performance (t=7.80, p=.020), and digital literacy (t=5.02, p<.001). Conclusion The scenario-based labor nursing simulation program utilizing an AI tutor was effective in improving nursing students’ delivery of knowledge, clinical performance, and digital literacy. Future research should focus on developing diverse and advanced simulation programs tailored to various areas of women’s health nursing, to further enhance critical thinking and clinical judgment capabilities. Keywords: Artificial intelligence, Education, Nursing, Obstetric nursing, Simulation training
What kind of evidence is this?
Supporting AI learning science · Comparative / pre-post study reported
Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.
Study methods · source passage
A non-equivalent pre-test/post-test design was employed to analyze the effects of a scenario-based labor nursing simulation education program using an AI tutor on labor nursing knowledge, critical thinking disposition, clinical performance, and digital literacy among nursing students. This study adhered to the TREND (Transparent Reporting of Evaluations with Nonrandomized Designs) statement guidelines for quasi-experimental studies [21].
Limitations and uncertainty · source passage
Abstract Purpose This study was conducted to determine the effects of a scenario-based labor nursing simulation education program using an artificial intelligence (AI) tutor on knowledge, critical thinking disposition, clinical performance abilities, and digital literacy. Methods This quasi-experimental study employed a non-equivalent control group pre- and post-test design. Seventy-two fourth-year nursing students (38 experimental, 34 control) were recruited from a university in Gumi, Korea. The experimental group participated in a scenario-based simulation education program supported by an AI tutor, while the control group engaged in conventional high-fidelity simulation training. Both groups attended sessions once per week for 2 hours over five sessions. Data were collected from August to December 2023 and analyzed using descriptive statistics, homogeneity testing, and the independent t-test. Results Simulation using an AI tutor resulted in significantly higher scores for the experimental group compared to the control group in nursing knowledge (t=7.03, p<.001), clinical performance (t=7.80, p=.020), and digital literacy (t=5.02, p<.001). Conclusion The scenario-based labor nursing simulation program utilizing an AI tutor was effective in improving nursing students’ delivery of knowledge, clinical performance, and digital literacy. Future research should focus on developing diverse and advanced simulation programs tailored to various areas of women’s health nursing, to further enhance critical thinking and clinical judgment capabilities. Keywords: Artificial intelligence, Education, Nursing, Obstetric nursing, Simulation training
Design the system before the pilot
Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.
- Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
- Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.
A 90-day district pilot
- Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
- Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
- Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
- Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.
The board decision brief
Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.
Attribution and reuse
Seo-A Park; Hye Young Kim. Development and effects of a scenario-based labor nursing simulation education program using an artificial intelligence tutor: a quasi-experimental study. Women's Health Nursing (2025-06-30). https://doi.org/10.4069/whn.2025.06.18
Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.