Tracing the Invisible: Understanding Students' Judgment in AI-Supported Design Work
It addresses the problem of how students navigate AI collaboration in design education, offering a conceptual lens for understanding co-creative sensemaking, but it is incremental as it builds on existing design judgment frameworks.
This study analyzed reflections from 33 student teams in an HCI design course to understand the judgments students make when using generative AI tools in design work, finding both established and new types of judgment such as agency-distribution and reliability judgment.
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.