AI 中文总结
本研究通过访谈19位可视化研究人员，梳理了可视化实验刺激全生命周期的实践与挑战，探讨了AI辅助实验设计的相关态度，并提出了未来的研究方向。
AI 中文摘要
可视化实验需要一组能有效回应研究问题与假设的“优质”刺激。创建、管理与部署刺激往往颇具挑战，因这些任务需格外谨慎；不当刺激会导致结果无效或无意义，浪费研究人员与参与者的资源。随着科研速度加快，对刺激相关任务的更好支持至关重要，但我们尚未深入了解可视化研究人员如何处理这些任务。为理解可视化实验人员的经验并指导未来改进，我们访谈了19位背景与经验各异的可视化研究人员。研究结果描述了刺激全生命周期的实践与挑战，涵盖从探索、选择到分发、部署及分析的全过程；例如，刺激管理与部署需繁琐的手动工作，这在具有多水平和复杂条件的实验中无法扩展。我们还讨论了对AI辅助可视化实验设计的担忧与乐观态度，最后提出了支持刺激创建、自动刺激检查及实验装置相关问题的未来研究机会。
英文摘要
Visualization experiments need a set of "good" stimuli that effectively address research questions and hypotheses. Creating, managing, and deploying stimuli are often challenging, as these tasks require tremendous care. Inappropriate stimuli can make the outcome invalid or uninteresting, wasting both researchers' and participants' resources. As the speed of science increases, better support for stimuli-related tasks is essential, yet we lack a closer look at how visualization researchers deal with them. To understand the experiences of visualization experimenters and guide future improvements, we interviewed 19 visualization researchers with diverse backgrounds and experiences. Our findings describe practices and challenges across the life cycle of stimuli, from exploration and selection through shipment, deployment, and analysis. For example, stimuli management and deployment require tedious manual effort, which does not scale for experiments with many levels and complex conditioning. We also discuss both concerns and optimism around AI-assisted visualization experiment design. We conclude with future research opportunities in supporting stimuli creation, automated stimuli inspection, and experimental apparatus concerns.
CommentsAccepted to IEEE VIS 2026 Full Paper, 11 pages, 2 figures, and 2 tables
