通过人工智能生成的视频彻底改变了教师发展和继续医学教育
Irene Contreras1, Samia Hossfeld1, Katharine de Boer1
1AO Education Institute, AO Foundation, Davos, Switzerland.
Journal of CME
|December 10, 2024
概括
生成型人工智能为创建持续医学教育 (CME) 视频提供了一种高效,成本效益的解决方案. 虽然学习者发现人工智能生成的视频具有吸引力,但要获得更广泛的接受,需要改善面部表情和声音的多样性.
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
- 数字学习数字学习
背景情况:
- 高质量的持续医学教育 (CME) 视频制作是资源密集的.
- 生成型人工智能为高效合成视频创作提供了一个潜在的解决方案.
- 评估用于教育内容生成的AI平台至关重要.
研究的目的:
- 为了比较CME视频制作的四个生成AI视频平台 (HeyGen,Synthesia,Colossyan,HourOne).
- 评估学习者对人工智能生成的教育视频的参与度和感知.
- 探索在教育环境中披露人工智能产生的内容的必要性.
主要方法:
- 使用Kano模型对AI视频生成平台进行比较分析.
- 创建两个人工智能生成的教育视频.
- 收集了25名学习者关于视频质量,参与度和AI识别的反.
主要成果:
- HeyGen平台满足了人工智能视频生成的大部分要求.
- 学习者认为人工智能视频质量好,吸引人,节奏良好.
- 只有32%的人认为视频是人工智能产生的,理由是表达力和发声能力的局限性.
- 24%的人认为披露人工智能生成的内容是必要的.
结论:
- 人工智能生成的视频是CME内容的可行,具有成本效益的替代方案.
- 需要进一步开发以增强现实主义 (面部表情,声调).
- 在教育中披露人工智能内容的道德准则需要仔细考虑.
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