We use the process and findings from a case study of design educators' practices of assessment and feedback to fuel theorizing about how to make AI useful in service of human experience. We build on Suchman's theory of situated actions. We perform a qualitative study of 11 educators in 5 fields, who teach design processes situated in project-based learning contexts. Through qualitative data gathering and analysis, we derive codes: design process; assessment and feedback challenges; and computational support. We twice invoke creative cognition's family resemblance principle. First, to explain how design instructors already use assessment rubrics and second, to explain the analogous role for design creativity analytics: no particular trait is necessary or sufficient; each only tends to indicate good design work. Human teachers remain essential. We develop a set of situated design creativity analytics--Fluency, Flexibility, Visual Consistency, Multiscale Organization, and Legible Contrast--to support instructors' efforts, by providing on-demand, learning objectives-based assessment and feedback to students. We theorize a methodology, which we call situating analytics, firstly because making AI support living human activity depends on aligning what analytics measure with situated practices. Further, we realize that analytics can become most significant to users by situating them through interfaces that integrate them into the material contexts of their use. Here, this means situating design creativity analytics into actual design environments. Through the case study, we identify situating analytics as a methodology for explaining analytics to users, because the iterative process of alignment with practice has the potential to enable data scientists to derive analytics that make sense as part of and support situated human experiences.

翻译：我们通过一项关于设计教育者评估与反馈实践的案例研究的过程与发现，来推进关于如何使AI服务于人类体验的理论思考。我们借鉴Suchman的情境行动理论，对来自5个领域的11名教育者开展质性研究，这些教育者在项目式学习情境中教授设计过程。通过质性数据收集与分析，我们提炼出以下编码：设计过程、评估与反馈挑战，以及计算支持。我们两次运用创造性认知的家族相似性原则：首先解释设计指导教师如何实际使用评估量规，其次阐释设计创造力分析工具的类比角色——没有任何特定特征是必要或充分的，每个特征仅倾向于表明优秀的设计工作，而人类教师仍不可或缺。我们开发了一套情境化设计创造力分析指标——流畅性、灵活性、视觉一致性、多尺度组织性与可读对比度——通过按需提供基于学习目标的评估与反馈来支持教育工作。我们提出一种名为"情境化分析"的方法论，原因有二：首先，使AI支持活生生的人类活动，取决于分析指标与情境实践的校准；其次，我们认识到分析工具可通过将其嵌入使用场景的界面中来实现情境化，从而对用户产生最大意义。在本研究中，这意味着将设计创造力分析工具嵌入实际设计环境。通过该案例研究，我们将情境化分析确立为向用户阐释分析工具的方法论，因为这种与实践迭代校准的过程，有可能使数据科学家推导出能融入并支持情境化人类体验的分析工具。