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使用参与行动研究来开发人工智能增强,同行驱动,基于案例和模拟增强的课程,用于紧急医疗住院人员
Kyle W Eastwood1, Daniah Allali2, Sittichok Leela-Amornsin3
1Department of Emergency Medicine, Dalhousie University, Halifax, NS, Canada. ky675200@dal.ca.
CJEM
|February 18, 2026
概括
高级紧急医疗学员通过人工智能增强的同行驱动框架提高了复苏医学知识. 这种方法结合了案例研究,模拟和人工智能工具,用于在六个月内增强学习和知识保留.
科学领域:
- 医学教育 医学教育
- 人工智能在医学中的应用
- 紧急医疗培训培训 紧急医疗培训
背景情况:
- 传统的医学教育方法可能无法完全优化复杂领域的知识获取,如复苏医学.
- 高级学员需要先进的学习策略,将理论知识与实际应用相结合.
- 技术和同行合作的整合为增强自主学习提供了潜力.
研究的目的:
- 开发和评估人工智能 (AI) 增强的,同行驱动的高级紧急医疗学员培训框架.
- 通过结构化,自主导的学习方法,增强在复苏医学中的知识获取和保留.
- 评估整合生成人工智能工具和基于模拟的学习对培训人员绩效的影响.
主要方法:
- 参与行动研究被用来开发一个框架,整合同行选择的阅读,基于案例的讨论,高保真模拟和间隔重复的闪卡.
- 实习生参与六个月的结构化学习周期,基于"理想难度"和故意实践原则.
- 利用基于证据的快速工程来创建人工智能生成的问题和补充学习活动.
主要成果:
- 人工智能增强的同行驱动框架促进了结构化的学习周期,增强了在复苏医学中的知识获取.
- 人工智能生成的问题支持基于检索的学习,闪卡集成改善了知识保留.
- 基于模拟的强化通过临床应用提供了"理想的难度",从而随着时间的推移改善了自我报告的回忆.
结论:
- 开发的框架有效地支持高级紧急医疗学员在复苏医学方面的自我指导学习.
- 人工智能增强和同行驱动策略的结合显著提高了知识保留.
- 这种可适应的方法具有在各种医学教育环境中应用的潜力.
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