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Objective <p>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.</p> Methods <p>From February to June 2025, 158 trainees in the department of cardiovascular surgery at our hospital were randomly assigned to an experimental group (<i>n</i> = 79) or a control group (<i>n</i> = 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.</p> Results <p>The experimental group achieved significantly higher theoretical examination scores than the control group (84.56 ± 5.07 vs. 77.95 ± 9.04, <i>P</i> &lt; 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 <i>P</i> &lt; 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 <i>P</i> &lt; 0.001), while the between-group difference in empathy was not significant (<i>P</i> = 0.328). Teaching satisfaction in the experimental group was high, with an overall satisfaction score of 4.68 ± 0.39.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月25日

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  • Yuehang Yang,
  • Li Sheng,
  • Liang Huang,
  • Qiannan Guo,
  • Yuting Hu,
  • Jiawei Shi

摘要

Objective

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.

Methods

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.

Results

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.

Conclusion

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.