Application of an AI-enabled personalized medical education model for improving clinical thinking among cardiovascular surgery trainees

Cardiovascular surgery involves complex clinical scenarios and high demands on clinical judgment. This study aimed to evaluate the effectiveness of an artificial intelligence (AI)-enabled personalized teaching model in cardiovascular surgery training. From February to June 2025, 158 trainees in the department of cardiovascular surgery at our hospital were randomly assigned to an experimental group ( n = 79) or a control group ( n = 79). The control group received conventional teaching, including lectures, bedside teaching, and case discussions. The experimental group received a personalized teaching model integrating an intelligent question bank, staged case-based teaching, and AI-based standardized patient training. Outcomes included theoretical examination scores, the Chinese version of the California Critical Thinking Disposition Inventory (CTDI-CV), clinical interview performance, and teaching satisfaction. The experimental group achieved significantly higher theoretical examination scores than the control group (84.56 ± 5.07 vs. 77.95 ± 9.04, P < 0.001). CTDI-CV results showed significantly higher total scores and higher scores in truth-seeking, open-mindedness, analyticity, systematicity, and inquisitiveness in the experimental group (all P < 0.05). In the clinical interview assessment, the experimental group scored significantly higher in completeness of information collection, communication skills, diagnostic reasoning, and overall performance (all P < 0.001), while the between-group difference in empathy was not significant ( P = 0.328). Teaching satisfaction in the experimental group was high, with an overall satisfaction score of 4.68 ± 0.39. The AI-enabled personalized teaching model may represent a feasible and effective approach for cardiovascular surgery education. It was associated with higher theoretical examination scores, better critical thinking disposition, higher scores in several dimensions of simulated clinical interview performance, and high learner satisfaction. Further studies are needed to confirm its broader applicability and long-term educational value.

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Publication Details

Journal
BMC Medical Education
Published
2026-07-25
DOI
https://doi.org/10.1186/s12909-026-09993-z
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00

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article

Application of an AI-enabled personalized medical education model for improving clinical thinking among cardiovascular surgery trainees

Yi Yang, Liang Huang, Qiannan Guo, Yu Hu et al.
BMC Medical Education
Artificial Intelligence in Healthcare and Education
article

Application of an AI-enabled personalized medical education model for improving clinical thinking among cardiovascular surgery trainees

Yi Yang, Liang Huang, Qiannan Guo, Yu Hu, Jiawei Shi, S Li
article en

Abstract

Cardiovascular surgery involves complex clinical scenarios and high demands on clinical judgment. This study aimed to evaluate the effectiveness of an artificial intelligence (AI)-enabled personalized teaching model in cardiovascular surgery training. From February to June 2025, 158 trainees in the department of cardiovascular surgery at our hospital were randomly assigned to an experimental group ( n = 79) or a control group ( n = 79). The control group received conventional teaching, including lectures, bedside teaching, and case discussions. The experimental group received a personalized teaching model integrating an intelligent question bank, staged case-based teaching, and AI-based standardized patient training. Outcomes included theoretical examination scores, the Chinese version of the California Critical Thinking Disposition Inventory (CTDI-CV), clinical interview performance, and teaching satisfaction. The experimental group achieved significantly higher theoretical examination scores than the control group (84.56 ± 5.07 vs. 77.95 ± 9.04, P < 0.001). CTDI-CV results showed significantly higher total scores and higher scores in truth-seeking, open-mindedness, analyticity, systematicity, and inquisitiveness in the experimental group (all P < 0.05). In the clinical interview assessment, the experimental group scored significantly higher in completeness of information collection, communication skills, diagnostic reasoning, and overall performance (all P < 0.001), while the between-group difference in empathy was not significant ( P = 0.328). Teaching satisfaction in the experimental group was high, with an overall satisfaction score of 4.68 ± 0.39. The AI-enabled personalized teaching model may represent a feasible and effective approach for cardiovascular surgery education. It was associated with higher theoretical examination scores, better critical thinking disposition, higher scores in several dimensions of simulated clinical interview performance, and high learner satisfaction. Further studies are needed to confirm its broader applicability and long-term educational value.

BMC Medical Education
Wuhan Union Hospital (CN), Union Hospital (CN)
National Key Research and Development Program of China
Quality Education
Openalex Percentile: Top 11%
Artificial Intelligence in Healthcare and Education
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