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

arXiv: https://arxiv.org/abs/2505.08939 · HTML: https://mlsanity.com/paper/2505.08939/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 13 May 2025.

## Builds on (most cited first, 6 of 6)

- [Impact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perception](https://mlsanity.com/paper/2310.13712/) (2023)
- [De-skilling, Cognitive Offloading, and Misplaced Responsibilities: Potential Ironies of AI-Assisted Design](https://mlsanity.com/paper/2503.03924/) (2025)
- [Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Students](https://mlsanity.com/paper/2401.14915/) (2024)
- [Coping with Uncertainty in UX Design Practice: Practitioner Strategies and Judgment](https://mlsanity.com/paper/2504.21397/) (2025)
- [Design Judgment in Data Visualization Practice](https://mlsanity.com/paper/2009.02628/) (2020)
- [Exploring the Nuances of Designing (with/for) Artificial Intelligence](https://mlsanity.com/paper/2010.15578/) (2020)

## Abstract (arXiv)

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.