在工程设计教学中对人工智能和专家评估进行比较分析
Tuğra Karademir Coşkun1, Esra Bozkurt Altan2
1Computer Education, Faculty of Education, Sinop, Turkiye.
PloS one
|September 22, 2025
概括
使用生成人工智能 (GAI) 的结构化提示可靠地评估科学课程计划,与专家评估相匹配. 这为STEM教育中的教师专业发展提供了一个可扩展的工具.
科学领域:
- 在STEM教育方面.
- 科学教学科学教学
- 工程设计整合 工程设计整合
背景情况:
- 将工程设计整合到科学教育中是一个优先事项.
- 教师往往缺乏这种整合的教学专业知识.
- 生成型人工智能 (GAI) 为课程计划评估提供了潜在的支持.
研究的目的:
- 调查GAI课程计划评估的可靠性和有效性.
- 将GAI评估与专家导师评估进行比较.
- 探索GAI和专家提供的反中的差异.
主要方法:
- 混合方法研究与43科学教师.
- 在工程设计整合方面进行为期4个月的专业发展.
- 通过使用ChatGPT 4.5 (结构化和非结构化提示) 和专家导师评估52个课程计划.
- 定量分析 (ICC,布兰德-阿尔特曼) 和定性分析 (编码).
主要成果:
- 结构化AI提示和专家评估之间的高度一致性 (ICC = 0.708).
- 对于非结构化提示的低和无意义的协议 (ICC = 0.076).
- 人工智能评估更为积极/整体;专家提供了更为关键/详细的反.
结论:
- 结构化的AI提示是可靠和有效的,用于评估科学课程计划.
- GAI工具,特别是有结构提示的工具,可以成为教师可扩展的支持系统.
- 非结构化的AI提示需要改进;GAI在教学评估中具有潜力和局限性.
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