VizCrit: 探索在视觉设计工具中展示计算反馈的策略
VizCrit: Exploring Strategies for Displaying Computational Feedback in a Visual Design Tool
AI总结：
VizCrit通过算法问题检测和视觉注释生成，探索在视觉设计工具中实现可操作反馈的策略，发现以解决方案为中心的反馈能提升初学者的创造力感知和设计质量。
摘要：
视觉设计教师经常提供多模态反馈，将注释与文本混合。先前的理论强调了可操作反馈的重要性，其中
英文摘要：
Visual design instructors often provide multi-modal feedback, mixing annotations with text. Prior theory emphasizes the importance of actionable feedback, where "actionability" lies on a spectrum--from surfacing relevant design concepts to suggesting concrete fixes. How might creativity tools implement annotations that support such feedback, and how does the actionability of feedback impact novices' process-related behaviors, perceptions of creativity, learning of design principles, and overall outcomes? We introduce VizCrit, a system for providing computational feedback that supports the actionability spectrum, realized through algorithmic issue detection and visual annotation generation. In a between-subjects study (N=36), novices revised a design under one of three conditions: textbook-based, awareness-centered, or solution-centered feedback. We found that solution-centered feedback led to fewer design issues and higher self-perceived creativity compared with textbook-based feedback, although expert ratings on creativity showed no significant differences. We discuss the implications for AI in Creativity Support Tools, including the potential of calibrating feedback actionability to help novices balance productivity with learning, growth, and developing design awareness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
