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

2025-05-13

AI Summary

This study investigates how students exercise judgment in generative AI–augmented design, focusing on two core issues: the allocation of creative responsibility between human and AI, and the assessment of output credibility. Drawing on reflective data from 33 student teams in an HCI design course, we conducted qualitative content analysis and grounded theory coding, informed by design cognition and human–AI collaboration theories. We identify and introduce— for the first time—two novel categories of design judgment: *responsibility-allocation judgment*, which determines the relative creative agency of human and AI, and *reliability judgment*, which evaluates the trustworthiness of AI-generated outputs. Building upon these findings, we propose a taxonomy of judgment types specific to AI-augmented design, revealing how students dynamically negotiate roles and trust in human–AI partnerships. This work advances theoretical foundations for cultivating AI literacy and critical human–AI collaboration competencies, with direct implications for pedagogy and design practice.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Examining students' judgment in AI-supported design workflows
• Identifying new judgment types in AI-human design collaboration
• Understanding trust and responsibility in AI co-creation processes

Innovation

• Analyzing student reflections on AI collaboration
• Identifying new judgment types in AI design
• Conceptualizing co-creative sensemaking with AI