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arXiv 2610.00085cs.HC

Critsly 与 StudioCrit:面向设计教育的工件感知 AI 评论工作空间及基于模拟的准备度研究

Critsly and StudioCrit: An Artefact-Aware AI Critique Workspace and Simulation-Based Readiness Study for Design Education

  • Singapore University of Technology and Design(新加坡科技设计大学)

机构由 AI 辅助整理,请以论文原文为准。

Nizam Kadir

AI总结:

本文提出工件感知 AI 评论工作空间 Critsly 及其研究模式 StudioCrit,通过模拟实验验证其在设计教育中支持评论到证据转化的准备度,为后续受控评估奠定基础。

AI中文摘要:

设计教育中的评论依赖于对进行中作品的解读、意图的阐述以及将反馈转化为修改。本技术报告介绍了 Critsly,一个工件感知的 AI 评论工作空间,以及 StudioCrit,其架构工作室研究模式。Critsly 结合了可视化看板、设计意图字段、引导式反思、基于视角的评论和行动计划。StudioCrit 增加了工作室/班级组织、基于角色的访问、认知与架构分类、教育者分析以及可导出的证据。本报告整合了在 2026 年 7 月提交的研究项目中记录的实现与模拟证据。三个模拟工作室场景产生了 109 条分类证据记录,其中 85 条被归入高阶 Bloom 类别。一次使用 50 个一次性学习者账户的单独排练产生了 56 条证据记录,其中 46 条被归入高阶类别。随后的一次加固排练记录了 50 个完成的会话、50 次成功的看板拉取以及 50 次对学生访问分析的拒绝。这些是软件和合成轨迹的观察结果,而非学习收益或人类认知表现的测量。自动分类仍属临时性质,源报告未确立分类器准确性或评分者间信度。其贡献在于一个已实现的从评论到证据的工作流程,以及对其进一步受控评估准备度的有限说明。

英文摘要:

Critique in design education depends on interpreting work in progress, articulating intentions and translating feedback into revisions. This technical report presents Critsly, an artefact-aware AI critique workspace, and StudioCrit, its architecture-studio research mode. Critsly combines a visual board, design-intention fields, guided reflection, perspective-based critique and action planning. StudioCrit adds studio/class organisation, role-based access, cognitive and architectural classification, educator analytics and exportable evidence. The report consolidates implementation and simulation evidence recorded in a research project submitted in July 2026. Three simulated studio scenarios yielded 109 classified evidence rows, including 85 assigned to higher-order Bloom categories. A separate rehearsal using 50 disposable learner accounts yielded 56 evidence rows, including 46 assigned to higher-order categories. A subsequent hardening rehearsal recorded 50 completed sessions, 50 successful board pulls and 50 denials of student access to analytics. These are software and synthetic-trace observations, not measurements of learning gains or human cognitive performance. Automated classifications remain provisional, and the source report does not establish classifier accuracy or inter-rater reliability. The contribution is an implemented critique-to-evidence workflow and a bounded account of its readiness for further controlled evaluation.

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