Data Has Entered the Chat: How Data Workers Conduct Exploratory Visual Analytic Conversations with GenAI Agents
Matt-Heun Hong, Anamaria Crisan · ACM Transactions on Interactive Intelligent Systems · 2025
We investigate the potential of leveraging the code-generating capabilities of Large Language Models (LLMs) to support exploratory visual analysis (EVA) via conversational user interfaces (CUIs). We developed a technology probe that was deployed through two studies with a total of 50 data workers to explore the structure and flow of visual analytic conversations during EVA. We analyzed conversations from both studies using thematic analysis and derived a state transition diagram summarizing the conversational flow between four states of participant utterances ( Analytic Tasks , Editing Operations , Elaborations and Enrichments , and Directive Commands ) and two states of Generative AI (GenAI) agent responses (visualization, text). We describe the capabilities and limitations of GenAI agents according to each state and transitions between states as three co-occurring loops: analysis elaboration, refinement, and explanation. We discuss our findings as future research trajectories to improve the experiences of data workers using GenAI. The code and data are available at https://osf.io/6wxpa .