Integrating AI Language Models in Qualitative Research: Replicating Interview Data Analysis with ChatGPT

Mohammad S. Jalali, Ali Akhavan
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引用次数: 1

Abstract

The recent advent of artificial intelligence (AI) language tools like ChatGPT has opened up new opportunities in qualitative research. We revisited a previous project on obesity prevention interventions, where we developed a causal loop diagram through in‐depth interview data analysis. Utilizing ChatGPT in our replication process, we compared its results against our original approach. We discuss that ChatGPT contributes to improved efficiency and unbiased data processing; however, it also reveals limitations in context understanding. Our findings suggest that AI language tools currently have great potential to serve as an augmentative tool rather than a replacement for the intricate analytical tasks performed by humans. With ongoing advancements, AI technologies may soon offer more substantial support in enhancing qualitative research capabilities, an area that deserves more investigation. © 2024 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.
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在定性研究中整合人工智能语言模型:使用 ChatGPT 复制访谈数据分析
最近,ChatGPT 等人工智能(AI)语言工具的出现为定性研究带来了新的机遇。我们重新审视了之前一个关于肥胖预防干预的项目,在该项目中,我们通过深入的访谈数据分析绘制了一个因果循环图。我们在复制过程中使用了 ChatGPT,并将其结果与我们最初的方法进行了比较。我们讨论了 ChatGPT 对提高效率和无偏见数据处理的贡献,但它也揭示了上下文理解的局限性。我们的研究结果表明,人工智能语言工具目前具有很大的潜力,可以作为一种辅助工具,而不是取代人类执行的复杂分析任务。随着技术的不断进步,人工智能技术可能很快就会在提高定性研究能力方面提供更多实质性支持,这一领域值得更多研究。© 2024 作者。系统动力学评论》由 John Wiley & Sons Ltd 代表系统动力学学会出版。
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