  We investigate how to use AI-based analytics to support design education. The
analytics at hand measure multiscale design, that is, students' use of space
and scale to visually and conceptually organize their design work. With the
goal of making the analytics intelligible to instructors, we developed a
research artifact integrating a design analytics dashboard with design
instances, and the design environment that students use to create them. We
theorize about how Suchman's notion of mutual intelligibility requires
contextualized investigation of AI in order to develop findings about how
analytics work for people. We studied the research artifact in 5 situated
course contexts, in 3 departments. A total of 236 students used the multiscale
design environment. The 9 instructors who taught those students experienced the
analytics via the new research artifact.
  We derive findings from a qualitative analysis of interviews with instructors
regarding their experiences. Instructors reflected on how the analytics and
their presentation in the dashboard have the potential to affect design
education. We develop research implications addressing: (1) how indexing design
analytics in the dashboard to actual design work instances helps design
instructors reflect on what they mean and, more broadly, is a technique for how
AI-based design analytics can support instructors' assessment and feedback
experiences in situated course contexts; and (2) how multiscale design
analytics, in particular, have the potential to support design education. By
indexing, we mean linking which provides context, here connecting the numbers
of the analytics with visually annotated design work instances.
