← 研踪 PaperTrailarXiv:2606.08914 · v2
Vibe Visualizing: How Visualization Novices Try (and Fail) to Generate and Interpret Visualizations with Conversational AI
首次提交 2026-06-08 09:29
版本更新 2026-11-07 09:43
雷达观测 2026-11-10 10:11
摘要
Conversational AI lowers technical barriers to generating and interpreting visualizations. People without visualization training or practical experience can therefore perform these tasks, but may lack the expertise needed to evaluate the resulting charts and interpretations. Through a study with 20 visualization novices, we examine conversation logs from 60 sessions with ChatGPT and develop a codebook that characterizes participant and AI behaviors in AI-assisted visualization. Paired with interview transcripts and survey responses, we derive three themes under outcome quality, AI non-compliance, and trust, verification, and repair. An exploratory role analysis identifies two session-level roles distinguished by user initiative and associated with different failure patterns. A comparative analysis with Gemini and Claude further reveals distinct system-specific behaviors. Based on these findings, we derive five design recommendations for AI-assisted visualization design and discuss implications for visualization literacy, human-AI roles, accountability, and agentic visualization.
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v2 · 2026-11-07 09:43 · 最新观测版本
Vibe Visualizing: How Visualization Novices Try (and Fail) to Generate and Interpret Visualizations with Conversational AI
Conversational AI lowers technical barriers to generating and interpreting visualizations. People without visualization training or practical experience can therefore perform these tasks, but may lack the expertise needed to evaluate the resulting charts and interpretations. Through a study with 20 visualization novices, we examine conversation logs from 60 sessions with ChatGPT and develop a codebook that characterizes participant and AI behaviors in AI-assisted visualization. Paired with interview transcripts and survey responses, we derive three themes under outcome quality, AI non-compliance, and trust, verification, and repair. An exploratory role analysis identifies two session-level roles distinguished by user initiative and associated with different failure patterns. A comparative analysis with Gemini and Claude further reveals distinct system-specific behaviors. Based on these findings, we derive five design recommendations for AI-assisted visualization design and discuss implications for visualization literacy, human-AI roles, accountability, and agentic visualization.
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