Evidence map›Paper›PMID 42323590›Full record

ArticleBMC medical education2026

Construction of a faculty competency model for medical simulation education integrated with GenAI: a multi-method quantitative study based on the perspective of medical students.

Ying Zhu-Ge, Xin-Tong Yao, Hong-Xia Mei, Hong-Xiang Yao, Qi Chen

Abstract read
In one paragraph

Article in BMC 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.

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0citing papers in PubMed
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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

5 authors.

Ying Zhu-Ge *The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325024, China.
Xin-Tong Yao *Wenzhou Medical University, Wenzhou, Zhejiang, China.
Hong-Xia MeiThe Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325024, China.
Hong-Xiang YaoThe Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325024, China. Lsyhx2004@163.com.
Qi ChenThe Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325024, China.

Funding

the Special Fund for Clinical Scientific Research of Wu Jieping Medical Foundation Grant No. 3206750, 2020-10-36
6 · The paper itself

Abstract

backgroundThe rapid advancement of Generative Artificial Intelligence (GenAI) is reshaping the landscape of medical simulation education, necessitating the enhancement of faculty competencies to effectively integrate evolving knowledge systems with GenAI for collaborative decision-making. However, current educational technologies face systemic limitations, including fragmented functionality, a disconnect between conventional and GenAI-driven teaching approaches, and a lack of dynamic capability assessment tools. Constructing a standardised capability scale is crucial to overcoming adaptation bottlenecks.

methodsGrounded in the integrated Technological Pedagogical Content Knowledge (TPACK) framework, this research proposed a two-round Delphi method to construct a faculty competency assessment scale, with stratified sampling involving 434 participants from clinical medicine, medical technology, and nursing fields. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used to test model fit (CFI = 0.939, RMSEA = 0.117). This proposed research was not registered as a clinical trial.

resultsThe resulting 16-item scale exhibited strong psychometric properties, demonstrating excellent internal consistency (Cronbach's α = 0.979), sampling adequacy (KMO = 0.961), and significant sphericity (Bartlett's test, p < 0.001). No factor covariance was detected, and item consensus was high (Kendall's W = 0.761, p < 0.001). The model showed acceptable fit (χ²/df = 6.893).

conclusionThis empirically validated and standardised assessment model provides robust support for integrating GenAI with simulation-based health sciences education. It offers a foundational framework for advancing faculty competency development and fostering adaptive, technology-enhanced teaching practices, while noting that the scale primarily captures ethical awareness rather than comprehensive governance competence.

trial registrationNot applicable. As this research did not involve clinical interventions, trial registration was not required.

Indexed as

Clinical CompetenceFaculty, MedicalGenerative Artificial IntelligenceProfessional CompetenceSimulation TrainingStudents, MedicalDelphi TechniqueFactor Analysis, StatisticalFemaleHumansPsychometricsFaculty competenceGenerative Artificial IntelligenceHealth sciences educationMixed methods researchTPACK competency model

Identifiers

PMID42323590
PMCPMC13536622

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Registered trials

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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.