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在肥胖生理学和病因学的体验性学习中实施GRATL和人工智能
Zhiyong Cheng1, Jinying Yang1, Karla P Shelnutt2
1Food Science and Human Nutrition Department, University of Florida Institute of Food and Agricultural Science (UF/IFAS), Gainesville, FL 32611.
Advances in physiology education
|July 25, 2025
概括
本研究介绍了研究能力测试每文献 (GRATL) 框架的收益,将人工智能 (AI) 与体验式学习 (EL) 整合起来,以教授肥胖症. 格拉特尔框架有效地提高了学生的知识,研究技能和职业能力.
科学领域:
- 医疗教育 医学教育
- 公共卫生 公共卫生
- 教育中的人工智能
背景情况:
- 有效的学习和传播肥胖知识对于预防和治疗至关重要.
- 专业人士和公众必须合作,打击这种慢性疾病.
- 现有的教育框架可能无法充分整合研究技能与实际应用.
研究的目的:
- 引入和评估新的研究能力测试 (GRATL) 框架.
- 评估人工智能 (AI) 在体验式学习 (EL) 中的整合,用于肥胖教育.
- 确定框架对学生的研究能力和职业能力的影响.
主要方法:
- 格拉特尔框架的设计包括七个研究能力领域作为预期的学习成果.
- 人工智能 (AI) 被整合到体验式学习 (EL) 的社区外展项目中.
- 使用定量矩阵来评估学生研究能力的增长和AI应用.
主要成果:
- 格拉特尔框架显著提高了学生的学科知识和研究能力.
- 学生表现出更好的职业能力技能,包括批判性思维,沟通和人工智能辅助的知识构建.
- 社区宣传改善了公众对肥胖和生活方式因素的理解.
结论:
- 由人工智能辅助的EL增强的GRATL框架有效地改善了肥胖生理学和病因学方面的学习成果.
- 该框架促进了基本的研究和职业技能,为学生准备应对未来的挑战.
- 格拉特尔框架的可适应性表明,它有可能扩展到其他学科.
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