提示,珍珠,不完美:在定性数据分析中比较ChatGPT和人类研究人员
Jonas Wachinger1, Kate Bärnighausen1,2, Louis N Schäfer1
1Heidelberg Institute of Global Health, University Hospital Heidelberg, Heidelberg, Germany.
Qualitative health research
|May 22, 2024
概括
像ChatGPT这样的大型语言模型显示出对定性分析的希望,识别了与人类研究人员相似的主题. 在提供支持的同时,人工智能工具也对已建立的研究实践提出了挑战.
科学领域:
- 社会科学 社会科学 社会科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 大型语言模型 (LLM) 对科学工作的影响正在讨论中.
- 关于LLM在定性研究和分析方面的潜力,目前的见解有限.
研究的目的:
- 使用ChatGPT探索人工智能支持的定性分析的潜力和陷.
- 将ChatGPT对采访成绩单的分析与经验丰富的人类研究人员的分析进行比较.
主要方法:
- 聊天GPT使用各种提示分析了采访成绩单.
- 结果与经验丰富的人类研究人员的分析进行了比较.
- 评估了ChatGPT提出代码书的能力,并将主题与理论联系起来.
主要成果:
- 聊天GPT确定了与人类分析有相当大的重叠的主题,包括细微的动态.
- 聊天GPT提出了一个代码书和关键引文与面部有效性,需要审查.
- 聊天GPT有效地将发现嵌入到更广泛的理论话语中,即使是看似不合适的模型.
结论:
- 聊天GPT在定性分析中表现出意想不到的表现,特别是在主题识别和理论嵌入方面.
- 像ChatGPT这样的AI工具可以支持定性研究,但也可以挑战最佳实践.
- 需要进一步讨论将人工智能纳入严格的定性研究和教学.
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