"The Diagram is like Guardrails": Structuring GenAI-assisted Hypotheses Exploration with an Interactive Shared Representation
This addresses the problem of supporting hypothesis exploration in human-AI collaborative data analysis for data analysts, though it appears incremental as it builds on existing interface designs with AI augmentation.
The paper tackled the challenge of assisting conceptual reasoning in data analysis by designing an interactive node-link tree interface with AI-generated hints and visualizations, finding that it enabled participants to generate an average of 21.82 hypotheses and acted as 'guardrails' to structure workflows and reduce cognitive load.
Data analysis encompasses a spectrum of tasks, from high-level conceptual reasoning to lower-level execution. While AI-powered tools increasingly support execution tasks, there remains a need for intelligent assistance in conceptual tasks. This paper investigates the design of an ordered node-link tree interface augmented with AI-generated information hints and visualizations, as a potential shared representation for hypothesis exploration. Through a design probe (n=22), participants generated diagrams averaging 21.82 hypotheses. Our findings showed that the node-link diagram acts as "guardrails" for hypothesis exploration, facilitating structured workflows, providing comprehensive overviews, and enabling efficient backtracking. The AI-generated information hints, particularly visualizations, aided users in transforming abstract ideas into data-backed concepts while reducing cognitive load. We further discuss how node-link diagrams can support both parallel exploration and iterative refinement in hypothesis formulation, potentially enhancing the breadth and depth of human-AI collaborative data analysis.