Agentic education · Learning science

Actionable companion · Retention & transfer

Learning with agency: Artificial Intelligence-Based Adaptive Simulation Integrated With a Workforce Decision-Support System to Improve Simulated Nursing Leadership Performance: A Randomized Controlled 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.

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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:

Background The growing integration of artificial intelligence (AI) in healthcare necessitates that nurse leaders develop advanced competencies in decision-making, communication, and data-informed clinical judgment. Traditional simulation-based education, while effective, often relies on fixed scenarios with limited adaptability and minimal real-time decision support. AI-enhanced simulation offers a potential solution by creating responsive, adaptive learning environments that support leadership development. Methods This randomized controlled, parallel-group, pretest-posttest trial included 130 final-year nursing students who were randomly allocated to an intervention group (n = 65), receiving AI-adaptive simulation integrated with an AI-based workforce decision-support system, or a control group (n = 65). Twenty-one clinical preceptors served as trained and blinded outcome assessors and were not included in the randomized sample. Leadership performance was assessed using the Creighton Simulation Evaluation Instrument (C-SEI). Secondary outcomes included team performance, general self-efficacy, and AI acceptance. Results At Week 10, the intervention group demonstrated greater improvement in leadership performance than the control group (between-group difference in change = 5.20, 95% CI 4.18-6.22, p < 0.001), with a large standardized effect (Cohen's d = 1.78). Greater improvements were also observed in team performance (between-group difference in change = 0.63, 95% CI 0.44-0.82, p < 0.001, d = 1.24) and general self-efficacy (between-group difference in change = 5.30, 95% CI 3.82-6.78, p < 0.001, d = 1.37). Intervention-group students reported favorable AI acceptance, including perceived usefulness and ease of use. Conclusions AI-adaptive simulation integrated with an AI-based workforce decision-support system was associated with improved leadership performance, team performance, and self-efficacy among final-year nursing students under controlled simulation conditions. Further research is needed to determine whether these effects are sustained and transfer to clinical practice.

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Randomized 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

This study used a randomized controlled study with a parallel-group, pretest-posttest design. The study was reported in accordance with the Consolidated Standards of Reporting Trials (CONSORT) 2025 statement for randomized trials [13].

Limitations and uncertainty · source passage

Several limitations should be considered. First, the study was conducted at one institution and one affiliated clinical training site, which may limit the generalizability of the findings to other educational and clinical settings. Second, the sample consisted predominantly of final-year nursing students, with relatively few clinical preceptors. Therefore, the findings may not be fully applicable to experienced nurse leaders or nurses working in diverse clinical contexts. Third, the outcomes were assessed in a high-fidelity simulation environment, which cannot fully replicate the complexity, unpredictability, and interpersonal demands of actual clinical practice. Fourth, follow-up assessment was limited to the immediate post-intervention period; therefore, the longer-term retention and transfer of leadership competencies to clinical practice were not assessed.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

  • Include a delayed assessment and an unfamiliar transfer problem without AI. Immediate supported performance cannot establish durable learning.
  • 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

  1. 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.
  2. 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.
  3. 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.
  4. 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

Mohammed Almalki. Artificial Intelligence-Based Adaptive Simulation Integrated With a Workforce Decision-Support System to Improve Simulated Nursing Leadership Performance: A Randomized Controlled Study. Cureus (2026-10-01). https://doi.org/10.7759/cureus.117315

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.