Evidence map›Paper›PMID 42106900›Full record

Trial reportMedical education online2026

Using randomization to compare AI and expert-generated formative assessment questions in medical education.

Akshat Kumar, Katie Stinson, Litao Wang, Elizabeth Nugent, Yongsu Lee, Mark Hormann, LaTanya Jones Love, Toufeeq Ahmed Syed

Abstract readRandomized Controlled TrialComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Medical education online, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Akshat KumarDepartment of Internal Medicine, University of California San Diego Health, San Diego, United States.
Katie StinsonMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, United States.
Litao WangMcGovern Medical School, The University of Texas Health Science Center at Houston, Houston, United States.
Elizabeth NugentMcGovern Medical School, The University of Texas Health Science Center at Houston, Houston, United States.
Yongsu LeeSchool of Public Health, The University of Texas Health Science Center at Houston, Houston, United States.
Mark HormannMcGovern Medical School, The University of Texas Health Science Center at Houston, Houston, United States.
LaTanya Jones LoveMcGovern Medical School, The University of Texas Health Science Center at Houston, Houston, United States.
Toufeeq Ahmed SyedMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAI-generated content is being used across the education spectrum and is beneficial for creating complex multiple-choice questions, such as those used in medical education. However, evaluating AI-generated content is challenging, and existing testing and evaluation methods are falling short. This study uses randomization to compare medical students' performance on and subjective evaluation of AI versus expert-generated questions. We hypothesized that there would be no difference in student performance or subjective evaluation of AI vs expert-generated questions.

methodsWe designed a single-center, randomized study where medical student participants received either one AI- or expert-generated question per day over four weeks.

resultsOur study showed that participants had similar perceptions of AI versus expert-generated questions ( DISCUSSION: Randomization was crucial to show that AI-generated questions were nearly imperceptible from expert-generated questions, demonstrating the need for additional evaluation methods to compare AI- and expert-generated medical education content.

Indexed as

Artificial IntelligenceEducational MeasurementEducation, MedicalFemaleHumansStudents, Medicalformative assessmentGenerative AImedical educationmultiple-choice questionsrandomized study

Identifiers

PMID42106900
PMCPMC13159578

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.