Evidence map›Paper›PMID 40830476›Full record

SynthesisBMC medical education2025

Effectiveness of generative artificial intelligence-based teaching versus traditional teaching methods in medical education: a meta-analysis of randomized controlled trials.

Juan Li, Kaiyu Yin, Yida Wang, Xiaoqin Jiang, Dongxu Chen

Abstract readMeta-AnalysisSystematic ReviewComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Synthesis 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 16 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 4 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

16 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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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

5 authors.

Juan Li *Department of Anesthesiology, West China Second University Hospital, Sichuan University, Chengdu, China.
Kaiyu Yin *Department of Anesthesiology, West China Second University Hospital, Sichuan University, Chengdu, China.
Yida WangKey Laboratory of BioResource and Eco-Environment of Ministry of Education, College of Life Science, Sichuan University, Chengdu, China.
Xiaoqin JiangDepartment of Anesthesiology, West China Second University Hospital, Sichuan University, Chengdu, China. 1598862657jxq@scu.edu.cn.
Dongxu ChenDepartment of Anesthesiology, West China Second University Hospital, Sichuan University, Chengdu, China. scucdx@foxmail.com.

Funding

Chengdu Science and Technology Department - Technological Innovation R&D Project No. 2024-YF05-00339-SNScience and Technology Department of Sichuan Province No. 2024NSFSC1679
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) has demonstrated remarkable capabilities across diverse medical applications, potentially revolutionizing healthcare delivery systems. This systematic review and meta-analysis investigated the comparative effectiveness of generative artificial intelligence (GAI)-based teaching methodologies versus conventional pedagogical approaches on educational outcomes among medical students.

methodsWe conducted a comprehensive literature search across multiple electronic databases, including PubMed, Cochrane Library, EMBASE, and Web of Science, encompassing studies published from January 2014 through January 2025. The review focused on randomized controlled trials (RCTs) that compared GAI-based teaching interventions with traditional instructional teaching methods in medical students.

resultsThe meta-analysis incorporated 11 eligible RCTs, comprising 786 medical students. Pooled analysis revealed no statistically significant difference in knowledge acquisition scores between GAI-based and traditional teaching approaches (standardized mean difference [SMD] 0.27, 95% confidence interval [CI] -0.31 to 0.85; p = 0.36). However, subgroup analysis indicated enhanced knowledge performance in the GAI group specifically for extended learning periods (exceeding one week) and practice-oriented courses. GAI-based instruction demonstrated superior outcomes in practical skill development compared to conventional methods (SMD 0.63, 95% CI 0.10-1.16; p = 0.02). Students in the GAI group reported significantly higher satisfaction scores with their learning experience.

conclusionWhile theoretical knowledge acquisition remains comparable between teaching modalities, the distinctive advantages of GAI-based approaches in practical skill development warrant their integration into medical curricula. Future research should focus on optimizing the integration of GAI-based teaching methods, standardizing implementation protocols, and evaluating long-term educational outcomes.

trial registrationThis protocol was registered on the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY) with the registration number INPLASY202510006. Registered on 2 January 2025.

Indexed as

Artificial IntelligenceEducation, MedicalTeachingGenerative Artificial IntelligenceHumansRandomized Controlled Trials as TopicStudents, MedicalArtificial intelligenceGenerative artificial intelligenceMedical education

Identifiers

PMID40830476
PMCPMC12362899

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.