Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Can Generative Artificial Intelligence Reliably Score Open-Ended Question Assessments in Undergraduate Medical 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.

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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:

There are numerous benefits to including open-ended questions (OEQs) in the assessment of medical knowledge, but one of the biggest challenges is the time it takes to grade student responses. With the widescale introduction of generative artificial intelligence (AI), it is plausible that OEQ exams can be automatically scored. The purpose of this study was to establish the accuracy of generative AI when scoring medical student OEQ exams. Students’ responses from OEQs administered at two US allopathic medical schools were analyzed. Case vignettes, questions, rubrics, and student responses were fed into the GPT-4 model via the Med2Lab platform. The Med2Lab system was specifically engineered to manage rubric integration and automate prompt workflows. Scores and feedback on students’ responses were generated and compared to faculty scores using Cohen’s weighted kappa (kw) to evaluate inter-rater reliability (IRR). An error pattern analysis was performed to assess why there were scoring discrepancies between faculty and GPT-4, then this information was used to perform rubric engineering. We ran 3 iterations of GPT-4 scoring after each rubric adjustment. By the third iteration, IRR between faculty and GPT-4 was substantial using the analytic rubric (question 1A: kw=0.94; question 2A: kw=0.88) and the holistic rubric (question 2H: kw=0.89). IRR for question 1H reached moderate reliability (kw=0.54). We identified errors in GPT-4 and faculty scoring, although score discrepancies were typically only 1-point. Our data suggest that generative AI can be used to reliably score OEQ exams using an iterative process of rubric engineering to achieve maximum reliability. Supplementary Information The online version contains supplementary material available at 10.1007/s40670-026-02638-2.

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

Doreen M. Olvet; Marieke Kruidering; Tracy B. Fulton; Bao Truong; Kumiko Endo; Robert Lucito; Joanne M. Willey. Can Generative Artificial Intelligence Reliably Score Open-Ended Question Assessments in Undergraduate Medical Education?. Medical Science Educator (2026-03-03). https://doi.org/10.1007/s40670-026-02638-2

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.