高等教育中人工智能的悖论:对巴勒斯坦教育工作者对人工智能依赖的定性调查
Anas Ali Alhur1, Zuheir N Khlaif2, Bilal Hamamra3
1Imam Abdulrahman Bin Faisal University, Damam, Saudi Arabia.
JMIR medical education
|September 15, 2025
概括
在医学教育中过度依赖人工智能 (AI),特别是在巴勒斯坦,导致技能退化和教学侵蚀. 策略包括设定人工智能界限和促进人工智能素养,以实现负责任的整合.
科学领域:
- 医学教育 医学教育
- 数字健康数字健康
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 越来越多地被整合到医学教育中,用于教学设计,内容创建和行政效率.
- 像ChatGPT这样的工具正在改变数字健康培训,但教师过度依赖会给教学自主,认知参与和道德带来风险.
- 医学教育工作者对人工智能依赖的经验研究有限,特别是在像全球南方这样的代表性不足的地区.
研究的目的:
- 确定导致巴勒斯坦医学教育工作者对人工智能依赖的因素.
- 评估人工智能依赖对教学自主,决策和专业认同的影响.
- 在数字医学教育中提出可持续和负责任的AI整合策略.
主要方法:
- 使用半结构面试 (n=22) 和焦点小组讨论 (n=24) 与46名医学教育工作者进行定性研究设计.
- 在NVivo软件的支持下,对15.5小时的转录数据进行了主题分析.
- 参与者代表了各种健康科学领域,AI使用频率不同 (每天,每周,每月).
主要成果:
- 人工智能依赖的主要驱动因素包括机构工作量 (>80%),学术信心低 (60%),完美主义相关的压力 (50%).
- 人工智能过度依赖的后果包括技能缩 (89%),教学侵蚀 (76%),动机下降 (67%),道德风险 (52%),社会碎片化 (48%) 和创造力抑制 (43%).
- 拟议的战略包括建立AI使用边界,培养混合智能,并将AI素养纳入教学.
结论:
- 在医学教育中不受控制的人工智能依赖可能会侵蚀临床医生的基本能力和教学诚信.
- 该研究强调了人工智能过度使用的重大认知,教学和伦理后果.
- 实施人工智能素养,专业发展和道德框架对于负责任的AI整合到医学教育中至关重要.
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