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Visualizationary: Automating Design Feedback for Visualization Designers Using Large Language Models
IEEE Transactions on Visualization and Computer Graphics
|June 13, 2025
Summary
Large language models (LLMs) offer customized feedback for visualization design. This study shows that even expert designers benefit from LLM-generated guidance to refine their visual communication strategies.
Area of Science:
- Human-Computer Interaction
- Data Visualization
- Artificial Intelligence
Background:
- Interactive visualization editors lack guidance on effective visual communication.
- Large language models (LLMs) present an opportunity to provide design assistance.
Purpose of the Study:
- To explore the potential of LLMs for providing actionable, customized feedback to visualization designers.
- To implement and evaluate a system (Visualizationary) that uses LLMs for visualization design support.
Main Methods:
- Developed Visualizationary, integrating ChatGPT with visualization design guidelines and perceptual filters.
- Conducted a longitudinal user study with 13 visualization designers (novices, intermediates, experts) creating visualizations over several days.
Main Results:
- LLM-generated natural language guidance assisted designers in refining their visualizations.
- Feedback proved valuable across different experience levels, including seasoned professionals.
Conclusions:
- LLMs can effectively augment the visualization design process by offering tailored, natural language feedback.
- This approach enhances the creation of effective visual communication, even for experienced designers.
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