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AI还是没有? 评估ChatGPT (开放AI) 和PerplexityAI在本科护理研究中的潜在使用:一个探索性的案例研究

Jamie Qiao Xin Ng¹, Joelle Yan Xin Chua¹, Mahesh Choolani²

¹Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

Nurse education in practice

|July 29, 2025

概括

像ChatGPT-4o和PerplexityAI这样的大型语言模型 (LLM) 可以帮助护理学生学习研究概念. 结构化的提示会产生更好的AI响应,ChatGPT模型的表现优于Perplexity AI.

科学领域:

护理教育护理教育
医疗保健中的人工智能
研究方法研究方法研究方法学

背景情况:

大型语言模型 (LLMs) 为护理学生提供了掌握研究基础的潜力.
关于LLM在回答护理研究问题的表现的研究有限.

研究的目的:

评估聊天GPT-4o,聊天GPT-4o迷你,和困惑的人工智能为本科护理研究问题.
在Bloom的分类层和提示结构中比较AI响应质量.
在教学研究概念中探索教师对人工智能的观点.

主要方法:

探索性案例研究评估ChatGPT-4o,ChatGPT-4o Mini和困惑的人工智能.
提出了41个与研究相关的问题,使用三个提示类型:非结构化,教授结构化和学生结构化.
人工智能对答案的评价使用了五点的利克特级别,包括准确性,相关性,清晰性,例子,批判性思维和引用.

主要成果:

与非结构化提示相比,结构化提示显著提高了AI响应质量.
所有经过测试的AI模型都在布鲁姆的分类层次上表现出有效性.
在回答研究问题时,ChatGPT-4o和ChatGPT-4o Mini的表现优于Perplexity AI.

结论:

人工智能模型显示承诺作为补充工具的本科护理研究教育.
需要进一步的研究来评估人工智能对特定护理研究学习成果的影响.

关键词:

人工智能的人工智能是人工智能.聊天GPT 聊天GPT 聊天教育教育教育教育教育教育.大型语言模型.护理学生的学生.困惑感困惑感困惑感研究方法研究方法论.

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