Interactive Sensemaking with SurveySense: Enhancing Survey Insights Through Human-AI Collaboration on an LLM-Based Platform

Jia Hui Bong, Chang Cai, Sing Yee Toh, Siyuan Liu, Xiuyi Fan

Abstract: Surveys are essential in various fields to gather feedback and information. Efficient data processing is thus crucial for quick, informed decision-making. As the volume of data continues to grow, automation in survey analysis has become increasingly important. This paper proposes a new software tool, SurveySense, which aims to streamline the survey analysis process. A study involving 26 participants was also conducted to evaluate the tool across six dimensions - Insight Quality, Efficiency, Consistency, User satisfaction and Trustworthiness, Human-AI Collaboration and Usability and Interaction. Results showed that SurveySense would be a helpful assistant for survey analysis but human inputs remain crucial in the process to further refine the AI-generated insights to align with personal goals.