Tracing the Invisible: Understanding Students' Judgment in AI-Supported Design Work

TL;DR

The paper addresses how students judge AI supported design work by analyzing reflections from 33 student teams in an undergraduate UX design course. Using thematic analysis and a three part codebook, the authors identify six core judgments and two emergent forms, agency distribution and reliability judgments, linking them to established design judgment theory. The findings extend the framework of design judgment to co creative human AI contexts and offer actionable implications for AI literacy and design education. The study highlights the cognitive and ethical dimensions of co designing with AI, underscoring the need for instructional practices that foster critical reflection and autonomous, responsible collaboration with AI tools.

Abstract

As generative AI tools become integrated into design workflows, students increasingly engage with these tools not just as aids, but as collaborators. This study analyzes reflections from 33 student teams in an HCI design course to examine the kinds of judgments students make when using AI tools. We found both established forms of design judgment (e.g., instrumental, appreciative, quality) and emergent types: agency-distribution judgment and reliability judgment. These new forms capture how students negotiate creative responsibility with AI and assess the trustworthiness of its outputs. Our findings suggest that generative AI introduces new layers of complexity into design reasoning, prompting students to reflect not only on what AI produces, but also on how and when to rely on it. By foregrounding these judgments, we offer a conceptual lens for understanding how students engage in co-creative sensemaking with AI in design contexts.