How Could AI Support Design Education? A Study Across Fields Fuels Situating Analytics

TL;DR

This paper explores how AI can support design education by developing situated design creativity analytics that align with educators' assessment practices and integrate into real design environments.

Contribution

It introduces a methodology called situating analytics, aligning AI tools with human practices in design education through a case study of educators' assessment methods.

Findings

Developed five design creativity analytics: Fluency, Flexibility, Visual Consistency, Multiscale Organization, Legible Contrast.

Proposed a methodology for aligning analytics with situated human practices.

Demonstrated the importance of integrating analytics into actual design environments.

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.