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人工智能和纳入模拟教育和领导力:全球跨部门对多样性的评估

Joana Berger-Estilita^1,2, Mia Gisselbaek^3,4,5, Arnout Devos⁶

¹Institute for Medical Education, University of Bern, Bern, Switzerland.

Advances in simulation (London, England)

|May 4, 2025

概括

基于模拟的医学教育领导力的人工智能生成的个人资料揭示了平台偏见. 聊天GPT和双子座显示更多的多样性,而克劳德更喜欢老年,白人,男性的个人资料,可能会延续不平等.

科学领域:

医疗教育技术技术医学教育
医疗保健中的人工智能
健康职业教育教育专业

背景情况:

基于模拟的医学教育 (SBME) 对医疗保健培训至关重要,影响技能,专业身份和包容性.
在SBME中,领导层的人口统计数据对课程设计和学习者成果产生重大影响.
人工智能 (AI) 平台可以生成人口统计数据,但由于固有的偏见,可能会延续现有的不平等.

研究的目的:

评估由三个人工智能平台:ChatGPT,Gemini和Claude产生的模拟教练和模拟实验室负责人的人口特征.
识别与医疗保健教育领导相关的人工智能生成的人口数据中的潜在偏见.
在不同的人工智能模型中比较年龄,性别,种族/种族和医学专业的代表性.

主要方法:

全球横截面研究使用标准化的英语提示来查询ChatGPT,Gemini和Claude.
人口统计数据 (年龄,性别,种族/民族,医疗专业) 收集了2014名模拟教练和1880名实验室负责人.
统计分析包括ANOVA和基平方测试,使用邦费罗尼校正 (P <0.05).

主要成果:

在AI平台中发现了显著的人口差异.
克劳德产生了年长的实验室负责人 (平均年龄57岁),男性占主导地位 (63.5%) 和白人占主导地位 (47.8%),有利于麻醉学和外科手术.
聊天GPT和双子座的年龄差距较小,性别代表性平衡,种族多样性更大 (高达24.4%的黑人,20.6%的西班牙裔/拉丁裔),以及更广泛的跨学科专业代表性.

结论:

人工智能为SBME领导层生成的人口资料,突出了可能加强医疗保健教育不平等的偏见.
与克劳德相比,ChatGPT和Gemini表现出更多样化的人口统计数据,这些数据倾向于年长的白人男性个人资料,特别是在领导层.
通过道德发展,人工智能素养和促进多样化的领导力来解决人工智能偏见对于公平的SBME至关重要.

关键词:

人工智能 (AI) 是一种人工智能.这是一个偏见的偏见.多样性和包容性多样性和包容性伦理的人工智能设计性别代表性的性别代表性.医疗保健教育卫生保健教育基于模拟的医学教育

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