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在虚拟解剖学研究工具中利用人工智能进行自动反:一项Q方法研究.

Yasmeen Mezil1, Philip Yu2, Layla Rahimpour2

  • 1Department of Anatomy and Genetics, College of Medicine, Alfaisal University, Riyadh, Kingdom of Saudi Arabia.

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一个基于人工智能的在线工具通过提供客观结构化实践考试 (OSPE) 的可访问实践来增强解剖学教育. 学生们发现AI OSPE有价值,对3D和2D解剖学可视化有不同的偏好.

关键词:
人工智能的人工智能是人工智能.决策树学习学习的方法客观结构化的实践考试.个性化的反,个性化的反.q-方法的使用方法.虚拟解剖学研究工具 虚拟解剖学研究工具

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科学领域:

  • 解剖学教育 解剖学教育
  • 教育中的人工智能
  • 教育技术的教育技术

背景情况:

  • 目标结构实践考试 (OSPE) 是标准的解剖评估方法.
  • 学生的准备受限于获得学习材料和模拟考试.
  • 资源限制和与流行病相关的访问问题需要创新的解决方案.

研究的目的:

  • 为OSPE研究开发和评估一个AI驱动的在线工具.
  • 通过使用Q方法来评估学生对AI OSPE工具的看法.
  • 为了确定学生对虚拟解剖学学习资源的偏好.

主要方法:

  • 开发一个基于网络的AI OSPE研究工具.
  • 与第一年职业治疗学生进行试点测试.
  • Q-方法和个别因素分析,以确定学生群体.

主要成果:

  • 两个不同的学生群体出现了:空间学习者和实用思想者.
  • 空间学习者更喜欢3D解剖结构,以便更好地定位.
  • 实用思想家们重视了该工具的实用性和课程相关性.

结论:

  • 人工智能驱动的教育工具可以补充传统的解剖学研究方法.
  • 学生的偏好各不相同,这凸显了对多样化的虚拟学习资源的需求.
  • 这些发现有助于提高基于人工智能的解剖学研究工具的质量.