Evidence map›Paper›PMID 42050524›Full record

ArticleBMC medical education2026

Knowledge, trust attitudes, and behaviors towards AI and their association with critical thinking disposition in Chinese undergraduate medical students: a cross-sectional study.

Songhua Ma, Yuyuan Lu, Huiqun Wu, Qing Zhou, Huiwei Huang, Jian Song

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Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Songhua MaMedical Department, Xinglin College, Nantong University, Nantong, 226236, China. songhuama@ntu.edu.cn.
Yuyuan LuGrade 2023, Clinical Medicine, Medical School of Nantong University, Nantong, 226001, China.
Huiqun WuDepartment of Medical Informatics, Medical School of Nantong University, Nantong, 226001, China.
Qing ZhouEducation and Training Department, Affiliated Hospital of Nantong University, Nantong, 226001, China.
Huiwei HuangDepartment of Physiology, Medical School of Nantong University, Nantong, 226001, China.
Jian SongDepartment of Medical Humanities, Medical School of Nantong University, Nantong, 226001, China. sj@ntu.edu.cn.

Funding

Jiangsu Province Degree and Graduate Education Teaching Reform Project JGKT25_C096Teaching and Research Reform Project of Nantong University 2024E05the Key Project of Higher Education Teaching Reform Research in Jiangsu Provinc 2025JGZZ41the Key Project of Higher Education Teaching Reform Research in Jiangsu Province 2025JGZD163
6 · The paper itself

Abstract

objectiveThe increasing use of artificial intelligence (AI) in medical education offers potential learning benefits but also raises concerns about its potential impact on critical thinking (CT) development. This study aims to examine the associations between medical undergraduates’ AI usage (frequency and duration), perceived trust attitudes towards AI, AI knowledge, responsible usage behaviors (verification and ethical considerations), and their CT disposition.

methodsA cross-sectional study was conducted at a Chinese medical school. Participants completed the Chinese version of Critical Thinking Disposition Inventory (CTDI-CV) and a structured questionnaire assessing AI usage, knowledge, perceived trust attitudes, responsible usage behaviors, and other related variables. Data were analyzed using Pearson correlation and multiple linear regression to identify predictors of CT disposition.

resultsThe mean CTDI-CV score was 292.19 (SD = 39.25), with 60.6% of participants showing a positive CT disposition. AI usage frequency and duration had no significant associations with CT disposition. Significant differences in CTDI-CV scores were found across majors (t = 5.244, p < 0.001), grade levels (F = 13.574, p < 0.001) and academic performance (F = 5.354, p < 0.01). Multiple linear regression revealed that responsible usage behavior was a significant positive predictor of CT disposition (Beta = 0.140, p < 0.05), whereas higher perceived trust attitude towards AI was a negative predictor (Beta = -0.129, p < 0.05).

conclusionsThe findings indicate that responsible usage behavior toward AI is positively associated with CT disposition, while perceived trust attitudes toward AI emerge as a negative predictor. These results suggest that medical education should place greater emphasis on cultivating critical engagement and verification skills rather than focusing solely on technical AI proficiency.

Indexed as

Artificial IntelligenceEducation, Medical, UndergraduateHealth Knowledge, Attitudes, PracticeStudents, MedicalThinkingTrustAdultChinaCross-Sectional StudiesFemaleHumansMaleSurveys and QuestionnairesYoung AdultArtificial IntelligenceCritical thinkingMedical educationMedical studentsUndergraduate

Identifiers

PMID42050524
PMCPMC13255303

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LicenceCC BY-NC-ND
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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.