Actionable companion · Assessment and evidence of learning

From evidence to district action: Can AI Write MCQs? A Comparison of ChatGPT-Generated and Educator-Authored Multiple-Choice Questions in Graduate Nursing Education

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.

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:

Background/Objectives: High-quality multiple-choice questions (MCQs) are essential for valid assessment in graduate-level nursing programs, yet developing such items is resource-intensive for educators. Large language models such as ChatGPT may generate educational assessment content; however, evidence regarding their effectiveness in producing graduate-level nursing examination items remains limited. This study compared the perceived quality of ChatGPT-generated and educator-authored MCQs for graduate-level nursing examinations and examined exploratory associations between assessor characteristics and quality ratings. Methods: A comparative cross-sectional study was conducted among 27 international nurse educators who independently evaluated 50 MCQs (25 ChatGPT-generated and 25 educator-authored) using an expert-reviewed rubric assessing eight quality domains, with the primary outcome being the between-source difference in perceived quality ratings. Participants were blinded to question source. Results: ChatGPT-generated MCQs received nominally higher ratings than educator-authored items across most domains, but effect sizes were uniformly small (Cohen’s d = 0.11–0.33). No significant differences were observed in perceived difficulty or distractor plausibility; after Bonferroni correction for multiple domain comparisons, differences in cognitive level, scenario relevance, and instructional alignment remained significant. Conclusions: ChatGPT-generated MCQs achieved perceived-quality ratings broadly comparable to educator-authored items when supported by structured prompting and expert refinement, although expert review remains necessary before classroom use, and these results should be regarded as preliminary. These findings support AI-assisted item development as a complementary strategy pending replication in larger, more rigorous studies.

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

Nader Alnomasy; Habib Alrashedi; Maha Dardouri; Soha Kamel Mosbah Mahmoud; Petelyne Pangket; Sang Bin You; Aluem Tark; Deborah Becker; Richard Balacuit Maestrado; Romeo Mostoles; Rayhanah R. Almutairi; Ebtsam Abouhashish; Sudharani B. Banappagoudar; Asim A. Alreshidi; Waleed Meajib Alshammari; Faisal Fahaad Alrshidi; Razan Alsayed; Sharifah Alsayed; Jiyoun Song. Can AI Write MCQs? A Comparison of ChatGPT-Generated and Educator-Authored Multiple-Choice Questions in Graduate Nursing Education. Nursing Reports (2026-08-27). https://doi.org/10.3390/nursrep16090304

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.