Visualizing Tree-of-Analysis: Facilitating Conversational Visual Analytics for Novices

Conversational visual analytics (CVA) make data exploration accessible to novices but often leave users disoriented during multi-turn conversations. Previous approaches provide data-centric recommendations, but fail to help users regain orientations. To bridge this gap, we conducted a formative study (N = 12) revealing that novices are insensitive to analytical cues and rely on vague queries, leading to disorientation and task failures. In contrast, experts are sensitive to two types of analytical cues and use seven types of queries to organize workflows. Based on these findings, we propose ToA, a novel approach that structures the CVA process as an interactive analysis tree. Moreover, we visualize this tree, with AI outputs as nodes (containing two cue types) and user queries as edges (categorized by seven query types), to provide novices with an overview of their analysis journey. We evaluated ToA through user studies (N = 12) and expert interviews (N = 3). The results suggest that ToA eliminates task failure and increases per-turn insights (+58.3%), despite longer per-turn thinking time (+17.7%). Expert interviews further confirm its potential to democratize visual analytics.