用交互式结构图引导AI辅助的假设探索，提升分析效率。
"The Diagram is like Guardrails": Structuring GenAI-assisted Hypotheses Exploration with an Interactive Shared Representation
- 设计节点链接图作为共享思维框架，支持结构化假设生成。
- 用户平均生成21.82个假设，图示显著减少认知负担。
- 适合需要系统性探索与迭代优化的科研人员使用。
数据分析涵盖从高层次概念推理到低层次执行的多种任务。尽管AI工具在执行层面日益普及，但在概念性任务中仍缺乏智能支持。本文研究了一种增强型节点链接树界面，通过AI生成的信息提示与可视化，作为假设探索的共享表示。通过设计探针实验（n=22），参与者平均生成21.82个假设。结果表明，节点链接图起到了“护栏”作用，促进结构化工作流，提供全局概览，并支持高效回溯。特别是可视化提示帮助用户将抽象想法转化为有数据支撑的概念，显著降低认知负荷。此外，该图示支持并行探索与迭代优化，有望提升人机协作数据分析的广度与深度。
原文摘要 · Abstract (English)
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.
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