  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.
