整合生成人工智能以促进基于查询的学习:将诱导人工智能研究助理与PubMed和CINAHL进行比较
Rachel F Fenske1, Jo Ann A Otts1
1University of South Alabama, Mobile, Alabama, USA.
Medical reference services quarterly
|November 4, 2024
概括
生成型人工智能 (GenAI) 工具可以通过促进基于调查的学习 (IBL) 来增强研究生护理教育. 这项研究发现,人工智能研究助理Elicit对于信息素养和文献搜索是有效的.
科学领域:
- 护理教育 护理教育
- 教育中的人工智能
- 信息素养 信息素养 信息素养
背景情况:
- 生成型人工智能 (GenAI) 为高等教育带来了机遇和挑战.
- 教师必须决定是否将GenAI整合到课程设计中,或抵制其使用.
- 有意的课程设计可以利用GenAI来促进基于调查的学习 (IBL).
研究的目的:
- 确定将GenAI整合到课程设计中的有效策略,以促进IBL.
- 为了比较GenAI文献搜索工具 (Elicit) 与传统数据库 (PubMed,CINAHL) 的有效性,用于研究生护理学生.
主要方法:
- 一个描述性的研究设计被雇用与研究生护理学生.
- 用一个两阶段的框架来组织信息并证明工具偏好是合理的.
- 开发了一个标题来评估批判性思维,IBL,信息素养和文献搜索结构.
主要成果:
- 学生们比较了Elicit,PubMed和CINAHL,确定了每一个人的优点和弱点.
- 该研究评估了基于准确性,相关性和效率的工具有效性.
- 在搜索工具之间发现了一种关系,有助于学生的评估.
结论:
- 像Elicit这样的GenAI工具可以有效地集成到护理教育中,以提高IBL和信息素养.
- 研究生护理学生可以通过评估不同的文献搜索工具来培养批判性思维能力.
- 这种方法支持有意的课程设计,以改善学生的学习成果在基于证据的实践.
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