Enhancing clinical skills in pediatric trainees: a comparative study of ChatGPT-assisted and traditional teaching methods

医学教育 情感(语言学) 医学 客观结构化临床检查 教育测量 沟通技巧 心理学 课程 教育学 沟通
作者
Hongjun Ba,Lili Zhang,Zizheng Yi
出处
期刊:BMC Medical Education [Springer Nature]
卷期号:24 (1) 被引量:6
标识
DOI:10.1186/s12909-024-05565-1
摘要

Abstract Background As artificial intelligence (AI) increasingly integrates into medical education, its specific impact on the development of clinical skills among pediatric trainees needs detailed investigation. Pediatric training presents unique challenges which AI tools like ChatGPT may be well-suited to address. Objective This study evaluates the effectiveness of ChatGPT-assisted instruction versus traditional teaching methods on pediatric trainees’ clinical skills performance. Methods A cohort of pediatric trainees ( n = 77) was randomly assigned to two groups; one underwent ChatGPT-assisted training, while the other received conventional instruction over a period of two weeks. Performance was assessed using theoretical knowledge exams and Mini-Clinical Evaluation Exercises (Mini-CEX), with particular attention to professional conduct, clinical judgment, patient communication, and overall clinical skills. Trainees’ acceptance and satisfaction with the AI-assisted method were evaluated through a structured survey. Results Both groups performed similarly in theoretical exams, indicating no significant difference ( p > 0.05). However, the ChatGPT-assisted group showed a statistically significant improvement in Mini-CEX scores ( p < 0.05), particularly in patient communication and clinical judgment. The AI-teaching approach received positive feedback from the majority of trainees, highlighting the perceived benefits in interactive learning and skill acquisition. Conclusion ChatGPT-assisted instruction did not affect theoretical knowledge acquisition but did enhance practical clinical skills among pediatric trainees. The positive reception of the AI-based method suggests that it has the potential to complement and augment traditional training approaches in pediatric education. These promising results warrant further exploration into the broader applications of AI in medical education scenarios.
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