用AI捕捉设计决策链条，让隐性思考变可见
ClearFairy: Capturing Creative Workflows through Decision Structuring, In-Situ Questioning, and Rationale Inference
- 将设计决策拆解为可追踪的动作-产物-解释单元
- 85%的推断理由被专业人士认可，强解释率从14%提至83%
- 适合设计师、AI训练者，提升协作与生成效果
捕捉专业人员在创意工作流（如UI/UX）中的决策过程对反思、协作和知识共享至关重要，但现有方法常导致理由不完整且隐藏了隐性决策。为此，我们提出CLEAR方法，将推理结构化为关联动作、产物和解释的认知决策步骤，使决策过程可通过生成式AI追溯。基于CLEAR，我们开发ClearFairy——一种用于UI设计的实时思考型AI助手，能识别薄弱解释，提出轻量澄清问题，并推断缺失理由。在12位专业人士的实验中，85%的推断理由被接受（原样或经修改）。系统显著提升了“强解释”比例——即提供充分因果推理的理由，从14%增至83%，且未增加认知负担。探索性应用显示，所捕获的决策步骤可增强Figma中的生成式AI代理，使其预测更符合专业人士意图，产出更连贯的结果。我们发布包含417个决策步骤的数据集以支持后续研究。
原文摘要 · Abstract (English)
Capturing professionals' decision-making in creative workflows (e.g., UI/UX) is essential for reflection, collaboration, and knowledge sharing, yet existing methods often leave rationales incomplete and implicit decisions hidden. To address this, we present the CLEAR approach, which structures reasoning into cognitive decision steps-linked units of actions, artifacts, and explanations making decisions traceable with generative AI. Building on CLEAR, we introduce ClearFairy, a think-aloud AI assistant for UI design that detects weak explanations, asks lightweight clarifying questions, and infers missing rationales. In a study with twelve professionals, 85% of ClearFairy's inferred rationales were accepted (as-is or with revisions). Notably, the system increased "strong explanations"-rationales providing sufficient causal reasoning-from 14% to 83% without adding cognitive demand. Furthermore, exploratory applications demonstrate that captured steps can enhance generative AI agents in Figma, yielding predictions better aligned with professionals and producing coherent outcomes. We release a dataset of 417 decision steps to support future research.
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