人工智能辅助个性化反馈对牙科学生放射诊断表现的影响:一项对照研究。

IF 3.2 2区 医学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Busra Nur Gokkurt Yilmaz, Furkan Ozbey, Birkan Eyup Yilmaz
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引用次数: 0

摘要

背景:本研究旨在评估chatgpt - 40生成的基于MeSH的个性化学习指南对牙科学生放射学诊断性能的影响,并与传统的正确/错误反馈方法进行比较。方法:本随机对照研究在Afyonkarahisar健康科学大学牙科专业五年级学生中进行。共有110名学生被随机分为实验组和对照组。实验组接受了由chatgpt - 40基于医学主题标题(MeSH)生成的个性化学习指南,针对他们的学习差距。对照组只接受标准的正确/错误反馈分析。干预一个月后,进行后测以评估诊断的准确性和学生满意度。结果:实验组的测试成绩(3.6±1.0)明显高于对照组(1.3±1.2);p结论:基于chatgpt - 40的个性化反馈被证明是提高诊断表现和支持牙科教育学习的有效工具。研究结果表明,人工智能驱动的个性化教育策略在未来的牙科培训中具有巨大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effect of artificial intelligence-assisted personalized feedback on radiographic diagnostic performance of dental students: a controlled study.

Background: This study aimed to evaluate the impact of MeSH based personalized learning guides generated by ChatGPT-4o on the radiographic diagnostic performance of dental students and to compare it with the traditional correct/incorrect feedback method.

Methods: This randomized controlled study was conducted among fifth-year dental students at Afyonkarahisar Health Sciences University. A total of 110 students were randomly assigned to either the experimental or control group. The experimental group received personalized study guides targeting their learning gaps, generated by ChatGPT-4o based on Medical Subject Headings (MeSH). The control group received only a standard correct/incorrect feedback analysis. One month after the intervention, a post-test was administered to assess diagnostic accuracy and student satisfaction.

Results: The increase in test scores from pre- to post-test was significantly higher in the experimental group (3.6 ± 1.0) compared to the control group (1.3 ± 1.2; p < 0.001). Final test scores were also significantly higher in the experimental group (p < 0.001). Survey responses indicated that the experimental group rated the feedback as more understandable, beneficial, and motivating compared to the control group.

Conclusions: ChatGPT-4o based personalized feedback proved to be an effective tool for enhancing diagnostic performance and supporting learning in dental education. The findings suggest that AI-driven individualized educational strategies hold significant potential in the future of dental training.

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来源期刊
BMC Medical Education
BMC Medical Education EDUCATION, SCIENTIFIC DISCIPLINES-
CiteScore
4.90
自引率
11.10%
发文量
795
审稿时长
6 months
期刊介绍: BMC Medical Education is an open access journal publishing original peer-reviewed research articles in relation to the training of healthcare professionals, including undergraduate, postgraduate, and continuing education. The journal has a special focus on curriculum development, evaluations of performance, assessment of training needs and evidence-based medicine.
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