发表机构
Arizona State University（亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对大语言模型下可视化创作工具线性界面不适用于探索性分析工作流的问题，介绍基于节点的VisCanvas界面，通过用户研究表明其能在维持性能水平时促进多样数据交互，还提炼了未来AI辅助可视化创作环境的设计原则。
AI 中文摘要
可视化数据分析涉及开放式探索和针对性问答。可视化创作工具通过让用户为这些任务创建可视化来支持此过程。随着大语言模型的兴起，人们致力于开发使用自然语言指令的可视化创作工具。然而，现有系统通常基于线性聊天界面，不太适合探索性视觉分析工作流程。本文介绍了VisCanvas，一种用于基于大语言模型的探索性可视化创作的基于节点的界面。它允许用户以非线性方式创建、修改、分支和合并可视化，实现对多个分析方向更高效的探索。我们对20名参与者进行了用户研究，以评估VisCanvas与基于聊天的基线界面相比的有效性。结果表明，VisCanvas在保持与当前主流方法难以区分的性能水平（即认知负荷和可用性）的同时，促进了更多样化的数据交互。我们还提炼了未来人工智能辅助可视化创作环境的设计原则。所有重现该研究所需的补充材料可在该https网址获取。
英文摘要
Visual data analysis involves both open-ended exploration and targeted question answering. Visualization authoring tools support this process by enabling users to create visualizations for these tasks. With the rise of large language models (LLMs), substantial effort has been devoted to developing visualization authoring tools that use natural language instructions. However, existing systems are typically based on a linear chat interface, which is not well suited to exploratory visual analysis workflows. In this paper, we introduce VisCanvas, a node-based interface for exploratory visualization authoring with LLMs. VisCanvas allows users to create, revise, branch, and merge visualizations in a non-linear way, enabling more efficient exploration of multiple analytical directions. We conducted a user study with 20 participants to evaluate the effectiveness of VisCanvas compared to a baseline chat-based interface. The results show that VisCanvas facilitates more diverse data interaction while maintaining performance levels (i.e., cognitive load and usability) that are indistinguishable from current prevailing methods. We then distill design principles for future AI-assisted visualization authoring environments. All supplemental materials required to reproduce the study are available at https://osf.io/gsxhn.
CommentsIEEE VIS 2026 & IEEE Transactions on Visualization and Computer Graphics (TVCG)
