在医学教育中对人工智能的道德参与
1Department of PhysiologyAll India Institute of Medical Sciences, Deoghar, Jharkhand, India.
Advances in physiology education
|January 3, 2025
概括
大型语言模型 (LLM) 提供医疗教育的好处,如改善信息获取,但需要谨慎的伦理整合. 机构需要明确的指导方针来负责任地使用人工智能,优先考虑内容质量和学生学习而不是检测.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
- 教育技术的教育技术
背景情况:
- 大型语言模型 (LLM) 在医学教育中带来了新的机遇和挑战.
- 由人工智能驱动的工具可以改善信息获取和批判性思维技能.
- 潜在的风险包括过度依赖和道德考虑.
研究的目的:
- 探索法学士在医学教育中的整合.
- 确定伦理和有效的LLM实施策略.
- 引导机构制定负责任的人工智能使用政策.
主要方法:
- 在教育背景下分析LLMs的双重性质.
- 讨论学生和教师的伦理考虑和最佳实践.
- 制定机构指导方针的框架.
主要成果:
- LLM可以作为补充教育资源,增强可访问性和公平性.
- 优先考虑内容质量和培养反文化至关重要.
- 伦理使用需要意识到LLM的局限性和数据谨慎.
结论:
- 机构必须制定适应性指导方针,以负责任地整合LLM,与他们的教学使命保持一致.
- 将LLM的好处与风险相平衡,对于有效的医学教育至关重要.
- 应该把重点放在增强学习和保持学术诚信上.
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