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.

翻译：问卷调查作为大规模数据收集的广泛手段，其固定结构往往限制了所获定性洞察的深度。尽管访谈能自然产生更丰富的回答，但在跨地域和大规模参与者群体中实施具有挑战性。为部分弥合这一差距，本研究探索基于大语言模型的聊天机器人通过嵌入问卷的访谈探针来支持定性数据收集的潜力。我们评估了四种基于理论的访谈探针：描述性、个体性、澄清性和解释性。通过裂区实验设计（N=64），我们比较了这些人机交互研究关键阶段（探索、需求收集和评估）中探针对回答质量与用户体验的影响。研究结果表明，探针有助于收集高质量的调查数据，且特定探针在不同研究阶段具有显著效果。我们为使用聊天机器人作为研究工具以丰富定性数据收集提供了实践与方法学启示。