Reproduce “Tracing the Invisible: Understanding Students' Judgment in AI-Supported Design Work” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Suchismita Naik, Prakash Shukla, Ike Obi, Jessica Backus, Nancy Rasche, Paul Parsons
This research from Purdue University investigates the cognitive processes undergraduate design students employ when using generative AI tools, identifying six distinct categories of judgment. The study provides a framework for understanding how students evaluate AI outputs, make decisions about AI integration, and conceptualize their collaborative relationship with AI in design work.
Key insights
- Students engage in six distinct categories of judgment when working with AI: reliability, appropriateness, output quality, calibration and refinement, ethical and epistemic considerations, and agency distribution.
- Effective integration of AI into design workflows requires iterative interaction, critical evaluation, and a metacognitive understanding of AI's capabilities and limitations.
- Students' conceptualizations of AI (as a tool, partner, or authority) influence how they integrate AI and distribute creative agency in their design process.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.