Evidence map›Paper›PMID 42110261›Full record

ArticleFrontiers in public health2026

Application of the artificial intelligence-assisted World Café teaching model in clinical pharmacology graduate course: a pilot study.

Ying Guo, Lan Chun Zhang, Qiong Li, Yun Xia Xiong, Wei Yan Hu, Hai Yun Luo, Jian Ping Xie, Hao Fei Yu

Abstract read
In one paragraph

Article in Frontiers in public health, 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

8 authors.

Ying Guo *Faculty of Basic Medical Science, Kunming Medical University, Kunming, Yunnan, China.
Lan Chun Zhang *Yunnan Key Laboratory of Pharmacology for Natural Products, Department of Zoology, School of Pharmaceutical Sciences, Yunnan College of Modern Biomedical Industry, Kunming Medical University, Kunming, Yunnan, China.
Qiong Li *Yunnan Key Laboratory of Pharmacology for Natural Products, Department of Zoology, School of Pharmaceutical Sciences, Yunnan College of Modern Biomedical Industry, Kunming Medical University, Kunming, Yunnan, China.
Yun Xia Xiong *Faculty of Basic Medical Science, Kunming Medical University, Kunming, Yunnan, China.
Wei Yan HuYunnan Key Laboratory of Pharmacology for Natural Products, Department of Zoology, School of Pharmaceutical Sciences, Yunnan College of Modern Biomedical Industry, Kunming Medical University, Kunming, Yunnan, China.
Hai Yun LuoFaculty of Basic Medical Science, Kunming Medical University, Kunming, Yunnan, China.
Jian Ping XieLibrary, Yunnan Minzu University, Kunming, Yunnan, China.
Hao Fei YuYunnan Key Laboratory of Pharmacology for Natural Products, Department of Zoology, School of Pharmaceutical Sciences, Yunnan College of Modern Biomedical Industry, Kunming Medical University, Kunming, Yunnan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: To investigate the application effectiveness of an artificial intelligence (AI)-assisted World Café teaching model in graduate-level clinical pharmacology course and to evaluate its impact on facilitating students' acquisition and integration of professional knowledge, developing clinical decision-making competencies, and enriching the teaching-learning experience. Methods: A pilot study was conducted involving 56 first-year master's students enrolled in a clinical pharmacology course at Kunming Medical University. Instruction was organized around a complex comprehensive clinical case of depression. Students participated in a structured five-stage seminar using the World Café format, supported by AI tools to facilitate group discussions and learning. A multi-method assessment strategy was employed, integrating a teaching effectiveness perception questionnaire, a specialized knowledge test, and a comparative analysis of AI-generated versus instructor-generated scores on case discussion records. Results: Student evaluations of the teaching model were favorable, with agreement rates exceeding 90% across all items assessing the learning experience. Mean self-rated scores for clinical decision-making abilities each exceeded 4.0 points. Following the intervention, post-test accuracy on depression etiology knowledge improved significantly (median increase from 60.71 to 78.43%; Conclusion: The AI-assisted World Café model effectively promotes the assimilation of complex knowledge and fosters higher-order clinical decision-making abilities among clinical pharmacology graduate students. It facilitates a pedagogical shift from passive knowledge transmission toward active capacity building. Furthermore, AI demonstrated reliability and promising utility as a tool for formative assessment, offering empirical support for innovative human-computer collaborative teaching approaches.

Indexed as

Artificial IntelligenceEducation, GraduateModels, EducationalPharmacology, ClinicalCurriculumFemaleHumansMalePilot ProjectsTeachingartificial intelligenceclinical decision-makingclinical pharmacologyformative assessmentmedical educationteaching reformWorld Café

Identifiers

PMID42110261
PMCPMC13153067

What Socratic holds

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LicenceCC BY
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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 Socratic graph.