The online survey in qualitative research: can AI act as a probing tool?

IF 1.6
Frontiers in research metrics and analytics Pub Date : 2025-09-05 eCollection Date: 2025-01-01 DOI:10.3389/frma.2025.1519008
Ryan Thomas Williams, Ewan Ingleby
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引用次数: 0

Abstract

Surveys are commonly associated with quantitative methods, yet there is growing recognition of their potential to yield qualitative insights into complex social phenomena. However, the effectiveness of open-ended survey questions is often limited by issues such as respondent fatigue and low-quality responses. To address these limitations, researchers are increasingly exploring the use of artificial intelligence (AI) to support dynamic survey design, probing questions, and participant engagement. This article explores the role of qualitative surveys in social science research, by considering their alignment with qualitative paradigms. The content assesses how AI-powered features, such as machine learning and chatbot-driven interfaces, can enhance data collection through adaptive questioning. The article also discusses key challenges related to data quality, participant inclusivity, and ethical considerations. Particular attention is given to the concept of "felt anonymity" in online surveys, which can encourage candid disclosures on sensitive topics and broaden participation across diverse populations. When designed with ethical and methodological care, qualitative surveys can thus serve as powerful tools for accessing underrepresented perspectives. By integrating AI into qualitative survey design, researchers can enhance both the richness and reach of their data. This article argues that AI-powered qualitative surveys, especially those capable of dynamic probing, offer a promising hybrid approach, bridging the scalability of surveys with the responsiveness of interviews, and calls for further empirical study of their ethical and epistemological implications.

质性研究中的在线调查:人工智能能否作为一种探索工具?
调查通常与定量方法联系在一起,但越来越多的人认识到它们对复杂社会现象产生定性见解的潜力。然而,开放式调查问题的有效性往往受到诸如被调查者疲劳和低质量回答等问题的限制。为了解决这些限制,研究人员越来越多地探索使用人工智能(AI)来支持动态调查设计、探索性问题和参与者参与。本文探讨了定性调查在社会科学研究中的作用,通过考虑它们与定性范式的一致性。内容评估了人工智能驱动的功能,如机器学习和聊天机器人驱动的界面,如何通过自适应提问来增强数据收集。本文还讨论了与数据质量、参与者包容性和道德考虑相关的关键挑战。特别注意在线调查中的“感觉匿名”概念,这可以鼓励对敏感话题的坦率披露,并扩大不同人群的参与。因此,如果在设计时考虑到伦理和方法,定性调查可以成为获得代表性不足的观点的有力工具。通过将人工智能整合到定性调查设计中,研究人员可以提高数据的丰富性和覆盖面。本文认为,人工智能驱动的定性调查,特别是那些能够动态探测的调查,提供了一种有前途的混合方法,将调查的可扩展性与访谈的响应性联系起来,并呼吁对其伦理和认识论意义进行进一步的实证研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.50
自引率
0.00%
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审稿时长
14 weeks
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