Chatbots for Data Collection in Surveys: A Comparison of Four Theory-Based Interview Probes

要旨

Surveys are a widespread method for collecting data at scale, but their rigid structure often limits the depth of qualitative insights obtained. While interviews naturally yield richer responses, they are challenging to conduct across diverse locations and large participant pools. To partially bridge this gap, we investigate the potential of using LLM-based chatbots to support qualitative data collection through interview probes embedded in surveys. We assess four theory-based interview probes: descriptive, idiographic, clarifying, and explanatory. Through a split-plot study design (N=64), we compare the probes' impact on response quality and user experience across three key stages of HCI research: exploration, requirements gathering, and evaluation. Our results show that probes facilitate the collection of high-quality survey data, with specific probes proving effective at different research stages. We contribute practical and methodological implications for using chatbots as research tools to enrich qualitative data collection.

著者
Rune Møberg. Jacobsen
Aalborg University, Aalborg, Denmark
Samuel Rhys. Cox
Aalborg University, Aalborg, Denmark
Carla F.. Griggio
Aalborg University, Copenhagen, Denmark
Niels van Berkel
Aalborg University, Aalborg, Denmark
DOI

10.1145/3706598.3714128

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714128

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Data Interpretation and Storytelling

G418+G419
6 件の発表
2025-04-28 23:10:00
2025-04-29 00:40:00
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