Evidence map›Paper›PMID 41286823›Full record

Trial reportBMC medical education2025

Comparing AI chatbot simulation and peer role-play for OSCE preparation: a pilot randomized controlled trial.

Hye-Yoon Lee, Jundong Kim, Hyojae Choi, Hyojin Bae, Aram Jeong, Sungyoul Choi, Ji-Hwan Kim, Chang-Eop Kim

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Observational
  3. Article
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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.

Hye-Yoon Lee *Division of Humanities and Social Medicine, School of Korean Medicine, Pusan National University, Yangsan, Korea.
Jundong Kim *Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, Korea.
Hyojae ChoiDepartment of Sasang Constitutional Medicine, School of Korean Medicine, Pusan National University, Yangsan, Korea.
Hyojin BaeDepartment of Physiology, College of Korean Medicine, Dongguk University , Gyeongju, Korea.
Aram JeongDepartment of Pediatrics, College of Korean Medicine, Gachon University, Seongnam, Korea.
Sungyoul ChoiDepartment of Neuropsychiatry, College of Korean medicine, Gachon University, Seongnam, Korea.
Ji-Hwan KimDepartment of Sasang Constitutional Medicine, School of Korean Medicine, Pusan National University, Yangsan, Korea. jani77@pusan.ac.kr.
Chang-Eop KimDepartment of Physiology, College of Korean Medicine, Gachon University, Seongnam, Korea. eopchang@gachon.ac.kr.

Funding

National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) no. 2022R1I1A2066653the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) RS-2024-00339889
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is increasingly applied in medical education, but its role in fostering interactive clinical competencies remains underexplored. This pilot study aimed to compare the feasibility and educational impact of an AI chatbot-based simulation with traditional peer role-play (PRP) for Objective Structured Clinical Examination (OSCE) preparation, and to share practical lessons from implementing a novel AI tool in a trial setting.

methodsNineteen final-year Korean medicine students were randomly assigned to either an AI chatbot group (n = 9) or a PRP group (n = 10) after a baseline knowledge test. Both groups underwent a 30-minute physical examination practice session, followed by a one-hour clinical interview training session specific to their group. The AI chatbot group practiced with a GPT-4o/Claude 3.5-based chatbot providing scenario-driven responses and automated feedback, while the PRP group practiced in pairs under tutor supervision. All participants then completed two OSCE stations (dizziness and shoulder pain). Performance was assessed using a structured checklist covering four domains: history taking, physical examination, patient education, and physician-patient interaction. Post-study questionnaires evaluated the learning experience.

resultsAlthough the differences in OSCE scores between the groups did not reach statistical significance, several interesting and complementary trends were observed. For example, the PRP group tended to score higher in history taking (mean 74.4 vs. 66.2 in dizziness scenario; Hedges' g = -0.68, mean 58.6 vs. 54.5 in shoulder pain scenario; Hedges' g = -0.21), while the AI chatbot group showed a tendency towards higher scores in patient education (32.5 vs. 22.2 in dizziness scenario Hedges' g = 0.44, 85.0 vs. 66.7 in shoulder pain scenario; Hedges' g = 0.99). Survey results reflected these following trends. The PRP group valued the authenticity of the interaction and the exam-like environment. In contrast, the AI chatbot group reported higher satisfaction with the autonomy, opportunity for repetitive practice, and structured feedback.

conclusionIn this pilot study, AI chatbot-based training and PRP demonstrated complementary strengths for OSCE preparation. While PRP appears effective for developing performance-based procedural and communication skills in a realistic setting, AI chatbots show potential for fostering clinical reasoning in a self-paced, reflective learning environment. These complementary strengths suggest a blended learning model, combining both methods, may be optimal for holistic clinical skills development. Further research is needed to validate these preliminary findings.

Indexed as

Artificial IntelligenceClinical CompetenceEducational MeasurementEducation, Medical, UndergraduatePeer GroupRole PlayingSimulation TrainingAdultFemaleGenerative Artificial IntelligenceHumansMalePhysical ExaminationPilot ProjectsRepublic of KoreaStudents, MedicalArtificial intelligenceChatbotClinical reasoningLarge Language ModelMedical educationObjective Structured Clinical ExaminationPeer role-playSimulation-based training

Identifiers

PMID41286823
PMCPMC12750692

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.