Abstract
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.
Abstract (translated)
我们借鉴设计教育者实践案例中的评估和反馈过程和发现，来探讨如何让AI更好地服务于人类经验。我们建立在Suchman情境行动理论的基础上。我们对5个领域的11位教育者进行了一项定性研究，他们教授的是基于项目学习的设计过程。通过定性数据收集和分析，我们得出以下代码：设计过程；评估和反馈挑战；计算支持。我们两次运用了创意认知的类比原则。首先，解释设计教师如何已经使用评估量表；其次，阐述设计创意分析的作用：没有特定的特质是必要的或者充分的；每个特质都只是表明好的设计作品。人类教师仍然至关重要。我们开发了一套情境设计创造力分析——流利性、灵活性、视觉一致性、多尺度组织和易读对比——来支持教师的努力，通过提供基于需求的学习目标为基础的评估和反馈来帮助学生。我们理论化了一种方法，我们称之为情境分析，因为让AI支持人类活动取决于将分析指标与实践环境对齐。此外，我们还意识到，分析指标对用户来说可能变得非常重要，通过将它们置于使用界面中，使它们融入实际应用场景中。在这里，这意味着将设计创造力分析置于实际设计环境中。通过案例研究，我们发现情境分析是一种向用户提供解释分析的方法，因为与实践对齐的迭代过程有可能使数据科学家得出有意义的数据分析，以支持并促进情境人类体验。
URL
https://arxiv.org/abs/2404.17390
