ArticleMedical science educator2026
Can Generative Artificial Intelligence Reliably Score Open-Ended Question Assessments in Undergraduate Medical Education?
Article in Medical science educator, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 (k Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-026-02638-2.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.