ArticleKorean journal of medical education2026
Enhancing history-taking education through GPT-4-based virtual patients and automated assessment: a study of medical student perceptions.
Article in Korean journal of medical education, 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
5 authors.
Funding
Abstract
purposeTo develop and evaluate a large language model (LLM)-based learning tool, featuring virtual patients (VPs) and virtual assessors (VAs), and to assess its impact on medical students' perceptions of history-taking education compared to conventional learning methods.
methodsA tool using the GPT-4 API was developed to provide seven clinical VP scenarios and a VA that delivered both immediate, reflective dialogue and comprehensive written feedback. First- and second-year medical students participated in a 6-day study. Pre- and post-participation surveys using a 5-point Likert scale assessed perceptions of the LLM tool versus conventional methods across usability, self-efficacy, and feedback quality domains.
resultsTwenty-one students completed the study. The LLM-based tool demonstrated statistically significant improvements over conventional methods in all assessed domains. Students reported greater comfort during practice (mean 4.57 vs. 2.95, p=0.0002). Significant gains were seen in six of eight self-efficacy measures, including confidence in handling unfamiliar cases (4.00 vs. 2.90, p=0.0002). All nine feedback quality dimensions improved significantly, with feedback perceived as more specific (4.43 vs. 3.24, p=0.0005) and personalized (4.19 vs. 3.19, p=0.0001).
conclusionAn LLM-based learning tool featuring VPs and VAs can significantly enhance medical students' perceived learning experience in history-taking education. It offers a scalable, accessible, and cost-effective complementary training method. Future research should validate these subjective improvements with objective performance metrics.
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.