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Updated: Sep 14, 2025

Using Learning Outcome Measures to assess Doctoral Nursing Education
Published on: June 21, 2010
从案例到自信:通过在研究生护理教育中的协作学习开发诊断推理技能
1About the Author Michelle L. Jackson, PhD, RN, is associate professor and director, Nurse Practitioner Program, Point Loma Nazarene University School of Nursing, San Diego, California. The author received a $500 Pedagogical Enrichment Grant for Inclusive Practice to support time spent researching, reflecting, and developing inclusive classroom strategies. ChatGPT was used to edit this manuscript. All content was reviewed, revised, and approved by the author in accordance with ethical publication standards. For more information, contact Dr. Jackson at mjackso2@pointloma.edu .
人工智能 (AI) 可以通过模拟的临床病例来增强研究生护理学生的诊断推理. 这种协作式的学习方法提高了学生的信心,并改变了异步的护理教育.
科学领域:
- 护理教育 护理教育
- 医疗模拟 医疗模拟
- 医疗保健中的人工智能
背景情况:
- 向研究生护理学生教授诊断推理是一项挑战,尤其是在异步学习环境中.
- 由于缺乏实时交互,因此需要创新的参与和技能发展策略.
研究的目的:
- 展示一种协作式学习方法,使用人工智能生成的案例来改善高级实践提供者学生的诊断推理.
- 评估人工智能支持的协作学习对学生信心和临床应用技能的影响.
主要方法:
- 利用人工智能 (AI) 为第一年高级实践提供者学生生成模拟的临床场景.
- 在异步教育环境中实施协作学习模式.
- 进行了前后评估,以衡量学生信心和诊断推理技能的变化.
主要成果:
- 前后评估表明,学生的信心显著增加.
- 定性反思强调了在学习过程中对同行合作的感知价值.
- 由人工智能驱动的协作模式在提高诊断推理技能方面被证明是有效的.
结论:
- 由人工智能生成的案例增强的协作学习,为异步护理计划中的临床教育提供了一种变革性的方法.
- 这种创新模式有效地解决了在远程学习环境中教学诊断推理的挑战.
- 该研究强调了人工智能的潜力,以提高护理教育,并为未来的高级实践提供者做好准备.
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