Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Comparing generative artificial intelligence platforms and nursing student performance on a women’s health nursing examination in Korea: a Rasch model approach

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

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Evidence and context

Health-professions education context: test transfer to K–12 rather than assuming it.

Original authors’ abstract, reproduced under the linked source license:

Purpose This psychometric study aimed to compare the ability parameter estimates of generative artificial intelligence (AI) platforms with those of nursing students on a 50-item women’s health nursing examination at Hallym University, Korea, using the Rasch model. It also sought to estimate item difficulty parameters and evaluate AI performance across varying difficulty levels. Methods The exam, consisting of 39 multiple-choice items and 11 true/false items, was administered to 111 fourth-year nursing students in June 2023. In December 2024, 6 generative AI platforms (GPT-4o, ChatGPT free version, Claude.ai, Clova X, Mistral.ai, Google Gemini) completed the same items. The responses were analyzed using the Rasch model to estimate the ability and difficulty parameters. Unidimensionality was verified by the Dimensionality Evaluation to Enumerate Contributing Traits (DETECT), and analyses were conducted using the R packages irtQ and TAM. Results The items satisfied unidimensionality (DETECT=–0.16). Item difficulty parameter estimates ranged from –3.87 to 1.96 logits (mean=–0.61), with a mean difficulty index of 0.79. Examinees’ ability parameter estimates ranged from –0.71 to 3.15 logits (mean=1.17). GPT-4o, ChatGPT free version, and Claude.ai outperformed the median student ability (1.09 logits), scoring 2.68, 2.34, and 2.34, respectively, while Clova X, Mistral.ai, and Google Gemini exhibited lower scores (0.20, –0.12, 0.80). The test information curve peaked below θ=0, indicating suitability for examinees with low to average ability. Conclusion Advanced generative AI platforms approximated the performance of high-performing students, but outcomes varied. The Rasch model effectively evaluated AI competency, supporting its potential utility for future AI performance assessments in nursing education.

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. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. 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, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. 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

Eun Jeong Ko; Tae Kyung Lee; Geum Hee Jeong. Comparing generative artificial intelligence platforms and nursing student performance on a women’s health nursing examination in Korea: a Rasch model approach. Journal of Educational Evaluation for Health Professions (2025-09-05). https://doi.org/10.3352/jeehp.2025.22.23

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.