教导护理学生有效的人工智能提示工程:CARE框架
Mary E Bester1, Kathryn Zeigler
1Author Affiliations: School of Nursing, Savannah, Georgia.
Nurse educator
|August 27, 2025
概括
本研究介绍了CARE快速工程框架,以帮助学生通过人工智能 (AI) 提高批判性思维和临床决策. 护士教育者可以使用这个框架来教导有效的人工智能提示,并确保在医疗保健教育中负责任地使用人工智能.
科学领域:
- 医疗专业教育
- 医疗保健中的人工智能
- 临床决策支持
背景情况:
- 有效的人工智能 (AI) 提示对于学生利用AI提高批判性思维和临床决策至关重要.
- 人工智能在教育中的整合需要有结构的方法来引导学生的互动和学习.
研究的目的:
- 介绍和描述CARE (上下文,行动,角色,期望) 快速工程框架.
- 为护士教育工作者提供一个系统的方法来教导学生有效的人工智能提示.
- 强调人工智能辅助学习和临床实践中人类联系的重要性.
主要方法:
- 开发CARE快速工程框架.
- 强调在人工智能交互中保持"人类联系".
- 讨论每一个CARE元素与改善AI产出的补救提示策略.
- 验证人工智能产出的过程,修改上下文,并提交后续行动.
主要成果:
- 学生利用他们的技能查询人工智能信息, 需要有效的提示技术.
- 导师在负责和有效的人工智能设计中发挥着至关重要的作用.
- 通过验证,修订和后续行动,CARE框架有助于改进人工智能产出.
结论:
- 该框架为护士教育者提供了一种系统的方法来教导临床决策技能.
- 它支持教师在角色建模有效的人工智能提示工程.
- 确保学生开发和利用有效的人工智能提示来提高学习和实践.
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