Social Work, Hong Kong Baptist University, Hong Kong, Hong Kong.
F1000Res. 2024 Oct 4;13:601. doi: 10.12688/f1000research.151952.2. eCollection 2024.
Researchers are leading the development of AI designed to conduct interviews. These developments imply that AI's role is expanding from mere data analysis to becoming a tool for social researchers to interact with and comprehend their subjects. Yet, academic discussions have not addressed the potential impacts of AI on narrative interviews. In narrative interviews, the method of collecting data is a collaborative effort. The interviewer also contributes to exploring and shaping the interviewee's story. A compelling narrative interviewer has to display critical skills, such as maintaining a specific questioning order, showing empathy, and helping participants delve into and build their own stories.
This case study configured an OpenAI Assistant on WhatsApp to conduct narrative interviews with a human participant. The participant shared the same story in two distinct conversations: first, following a standard cycle and answering questions earnestly, and second, deliberately sidetracking the assistant from the main interview path as instructed by the researcher, to test how well the metrics could reflect the deliberate differences between different conversations. The AI's performance was evaluated through conversation analysis and specific narrative indicators, focusing on its adherence to the interview structure, empathy, narrative coherence, complexity, and support for human participant agency. The study sought to answer these questions: 1) How can the proposed metrics help us, as social researchers without a technical background, understand the quality of the AI-driven interviews in this study? 2) What do these findings contribute to our discussion on using AI in narrative interviews for social research? 3) What further research could these results inspire?
The findings show to what extent the AI maintained structure and adaptability in conversations, illustrating its potential to support personalized, flexible narrative interviews based on specific needs.
These results suggest that social researchers without a technical background can use observation-based metrics to gauge how well an AI assistant conducts narrative interviews. They also prompt reflection on AI's role in narrative interviews and spark further research.
研究人员正在引领 AI 设计的发展，旨在进行访谈。这些发展意味着 AI 的角色正在从单纯的数据分析扩展到成为社会研究人员与研究对象互动和理解他们的工具。然而，学术讨论尚未涉及 AI 对叙事访谈的潜在影响。在叙事访谈中，收集数据的方法是一种协作努力。访谈者也有助于探索和塑造受访者的故事。一个引人入胜的叙事访谈者必须展示关键技能，例如保持特定的提问顺序、表现出同理心，并帮助参与者深入挖掘并构建自己的故事。
本案例研究在 WhatsApp 上配置了一个 OpenAI Assistant 来对一名人类参与者进行叙事访谈。该参与者在两个不同的对话中分享了同一个故事：首先，按照标准周期认真回答问题，其次，按照研究人员的指示故意引导助手偏离主要访谈路径，以测试指标如何反映不同对话之间的故意差异。通过会话分析和特定的叙事指标评估 AI 的表现，重点关注其对访谈结构的遵守、同理心、叙事连贯性、复杂性和对人类参与者代理的支持。本研究旨在回答以下问题：1）作为没有技术背景的社会研究人员，这些建议的指标如何帮助我们理解本研究中 AI 驱动访谈的质量？2）这些发现对我们在社会研究中使用叙事访谈的 AI 的讨论有何贡献？3）这些结果可以激发哪些进一步的研究？
研究结果表明，AI 在对话中保持结构和适应性的程度，说明了其支持基于特定需求的个性化、灵活叙事访谈的潜力。
这些结果表明，没有技术背景的社会研究人员可以使用基于观察的指标来衡量 AI 助手进行叙事访谈的效果。它们还促使人们反思 AI 在叙事访谈中的作用，并激发进一步的研究。
