动态调查：利用大语言模型（LLM）融合质性深度、定量结构与协作互动
Dynamic Surveys: Using LLMs to Blend Qualitative Depth,Quantitative Structure, and Collaborative Interaction
AI总结：
该研究提出动态调查平台，利用LLM实时聚类质性回答并引出定量内容，经93名参与者的两项实地研究验证，其能提供更丰富见解并提升参与度。
AI中文摘要：
调查是收集数据、获取社会现象见解的强大工具，在产品设计、营销、科学研究中至关重要。然而，传统开放式与封闭式问题格式限制了研究人员捕获兼具质性见解丰富性与定量分析严谨性数据的能力。为解决这些问题，我们提出动态调查（Dynamic Surveys），这一调查平台利用大语言模型（LLM）实时对质性回答进行动态聚类，并针对这些聚类引出定量评分与排序，以及受访者关于自身观点与更广泛受访者趋势对比的质性反思，尤其适用于早期或探索性研究场景。该过程生成一份报告，向调查创建者与受访者展示聚类后的回答，以及每个聚类的排名、评分分布和后续反思。为评估动态调查，我们开展了两项为期2个月的实地研究，共93名参与者。第一项研究中，52名学生为职业研讨会提供输入；第二项研究中，41名学生对其学术课程的缺口给出反馈。其中，44名受访者填写了关于使用动态调查体验的调查。我们还将生成的报告分享给4名对其工作相关见解感兴趣的人员，并对他们进行访谈，以了解其对结果的看法以及平台设计中存在的任何背景风险。研究结果表明，动态调查不仅比传统调查工具提供更丰富、更深入的回答见解，还能提高参与度并培养社区意识。我们探讨了调查平台设计的更广泛意义，即融合质性深度与定量结构，以促进更丰富的见解并提供更具协作性的互动。
英文摘要：
Surveys are a powerful tool for collecting data and eliciting insights on social phenomena, and are critical in product design, marketing, scientific research. However, traditional open-ended and closed-ended question formats limit researchers' ability to capture data that combines both the richness of qualitative insights and the analytical rigor of quantitative data. To address these problems, we propose Dynamic Surveys, a survey platform that uses Large Language Models (LLMs) to dynamically cluster qualitative responses in real time and to elicit quantitative ratings and rankings on those clusters and qualitative reflections on how their views compare to broader respondent trends, especially helpful in early-stage or exploratory research settings. This process generates a report showing survey creators and respondents the clustered responses as well as each cluster's rank, rating distribution, and follow-up reflections. To evaluate Dynamic Surveys, we conducted two field studies with 93 participants over a 2-month period. In the first study, 52 students provided input for a career workshop, while in the second, 41 students gave feedback on gaps in their academic curriculum. Of these, 44 respondents filled out a survey on their experience using Dynamic Surveys. We also shared the generated report with 4 individuals who were interested in the insights for their work, and interviewed them to understand their perspectives on the results and any contextual risks they saw in the platform design. Our findings suggest that Dynamic Surveys not only provide richer and deeper insights into responses compared with traditional survey tools, but also increase engagement and foster a sense of community. We discuss broader implications for the design of survey platforms that blend qualitative depth with quantitative structure, facilitating richer insights and offering more collaborative interactions.
