Evidence map›Paper›PMID 42871190›Full record

ArticlePRiMER (Leawood, Kan.)2026

Comparative Outcomes of ChatGPT Versus Faculty-Facilitated Simulation Design by Medical Students.

Chad N Taniguchi, Keely Myers, Erin Jyo, Kyle Ishikawa, Benjamin W Berg, Jannet Lee-Jayaram

Abstract read
In one paragraph

Article in PRiMER (Leawood, Kan.), 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

6 authors.

Chad N TaniguchiJohn A. Burns School of Medicine, Honolulu, HI.
Keely MyersJohn A. Burns School of Medicine, Honolulu, HI.
Erin JyoJohn A. Burns School of Medicine, Honolulu, HI.
Kyle IshikawaDepartment of Quantitative Health Sciences, John A. Burns School of Medicine, Honolulu, HI.
Benjamin W BergSimTiki Simulation Center, John A. Burns School of Medicine, Honolulu, HI.
Jannet Lee-JayaramSimTiki Simulation Center, John A. Burns School of Medicine, Honolulu, HI.

Funding

The Role of gp120 on Cardiovascular Disease in People Living with HIVU54MD007601 · NIMHD · UNIVERSITY OF HAWAII AT MANOA · PI Jerris Robert Hedges · 2017 to 2026
$59.5M
NIMHD NIH HHS U54 MD007601
6 · The paper itself

Abstract

Introduction: Limited research describes generative artificial intelligence (AI) and large language models, such as ChatGPT, to support the design of health care simulation-based manikin scenarios (SBMS). This pilot study describes the design process and quality assessment of SBMS designed by medical students with ChatGPT generative AI support (AIG scenarios) compared to those designed by medical student interest groups (MSIG) with faculty guidance (MSIG scenarios). Methods: Based on this medical school simulation center's scenario design template, we compared the time to completion of the scenario between five MSIG SBMS and five AIG SBMS on the same clinical topics. Three blinded simulation experts rated all randomized scenarios using the Simulation Scenario Evaluation Tool (SSET). We performed paired Results: MSIG SBMS design required months of faculty-student collaboration, whereas AIG SBMS were completed in a mean of 37 minutes. AIG scenarios had significantly higher Scenario Materials and Resources SSET scores. We found no differences in other SSET elements or overall scores. Rater agreement across most categories was low. The Debriefing Plan element received the lowest scores in both groups. Conclusions: This study supports the potential of simulation novices using ChatGPT to streamline health care SBMS design. AIG scenarios required less time to create and were comparable in quality to MSIG scenarios. By accelerating the initial scenario design process, AI offers an efficient tool for developing educational simulations, expanding opportunities for students to engage in scenario design as an educational methodology.

Indexed as

ChatGPTgenerative AIinstructional designlarge language modelsmedical educationsimulation-based health care education

Identifiers

PMID42871190
PMCPMC13658997

What OpenQuestion holds

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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.