作为一个不断发展的社会技术系统,人工智能增强的持续专业发展:多方法理论框架发展研究研究
Vjekoslav Hlede1, Sofia Valanci2, G Robert D'Antuono3
1Learning Division, American Society of Anesthesiologists, 1061 American Lane, Schaumburg, IL, 60173-4973, United States, 1 7734705592.
JMIR medical education
|February 12, 2026
概括
人工智能 (AI) 通过整合复杂性理论和演员网络理论来改变医疗保健的持续专业发展 (CPD). ALEERRT-CA框架指导人工智能准备,负责任的实践设计,以及在这个不断发展的社会技术系统的未来研究.
科学领域:
- 社会技术系统 社会技术系统
- 健康 职业 教育 教育 专业
- 医疗保健中的人工智能
背景情况:
- 目前的研究缺乏人工智能对医疗保健持续专业发展 (CPD) 影响的统一模型,作为一个复杂的社会技术系统.
- 现有的框架往往孤立了诸如道德或识字等因素,忽视了它们的相互作用以及人工智能赋能患者和社会的角色.
- 需要一个全面的模型来理解AI对CPD和更广泛的医疗保健生态系统的动态影响.
研究的目的:
- 提出一个理论驱动的框架,将AI结构与复杂性理论 (CT) 和演员网络理论 (ANT) 整合起来,以解释AI对CPD系统的转变.
- 开发一个模型,提供对人工智能在CPD中的实践,研究和政策影响的见解.
- 为分析人工智能增强型CPD提供坚实的理论基础.
主要方法:
- 采用了多方法理论构建方法,从确定人工智能增强的CPD作为现象开始.
- 一个结构化的文献审查确定了AI增强的CPD关键组件以及CT和ANT的理论基础.
- 一个代的,人工智能辅助的诱导分析开发了该模型,该模型使用一个组织在CPD中使用人工智能的案例研究进行了验证.
主要成果:
- 开发的ALEERRT-CA框架包括六个支柱 (AI素养,可解释性,道德,准备性,可靠性,学习理论) 和两个理论镜头 (CT和ANT).
- 复杂性理论 (CT) 解释了宏观层面的系统动态,如人工智能增强的CPD中的出现和适应.
- 演员网络理论 (ANT) 阐明了人类和非人类参与者如何塑造人工智能增强的CPD,说明了人工智能对代理和新兴学习动态的重新分配.
结论:
- 这项研究引入了一个新的AI增强CPD的社会技术模型,ALEERRT-CA框架,集成CT和ANT以提高解释能力.
- 该框架是教育工作者,领导者和决策者评估人工智能准备情况,设计负责任的CPD和指导未来研究的实用工具.
- ALEERRT-CA为在医疗保健教育和实践中快速发展的人工智能领域的导航提供了关键的理论镜头.
关键词:
在这里,我们可以看到AIAIAI.人工智能可解释性人工智能准备情况人工智能可靠性AI可靠性通过人工智能增强的CPD.演员网络理论演员网络理论人工智能的人工智能是人工智能.黑盒子是一个黑盒子.复杂性的复杂性 复杂性的复杂性持续的专业发展 持续的专业发展学习理论的学习理论更多相关视频
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