CHI 2025 · Source-reviewed citation guide
DesignWeaver: Dimensional Scaffolding for Text-to-Image Product Design
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
DesignWeaver turns dimensions derived from design briefs and generated images into a selectable palette that helps novice product designers write richer prompts and explore a broader text-to-image design space.
When to cite this work
Cite DesignWeaver when discussing dimensional scaffolding, novice prompt construction, or interface support for richer and more varied exploration in text-to-image product design.
- Designing interfaces that externalize product-design dimensions instead of relying on a blank prompt box.
- Studying how prompt scaffolds change vocabulary, iteration, visual diversity, novelty, and user expectations.
Contribution
What this paper adds
- Reports a formative study with 12 experienced product designers about how experts and clients communicate across a design space.
- Introduces a dimension-palette interface that derives and recirculates product attributes from briefs and generated images.
- Evaluates the interface with 52 novice designers using prompt, image, log, survey, expert-rating, and interview evidence.
Evidence
What the paper reports
- Participants wrote longer prompts (48.22 versus 23.73 words) and used more unique terms per prompt (24.48 versus 10.59); both Mann-Whitney comparisons report p < .001.
- Generated images were more diverse by CLIP similarity (0.863 versus 0.903, where lower means more diverse; p < .001), and expert-rated novelty was higher (4.09 versus 3.54; p = .002).
- Requirement alignment (p = .059), image satisfaction (p = .579), and expectation alignment (p = .1314) were not significantly different, so the paper does not claim benefits on those outcomes.
Boundary
What it does not establish
- The controlled study involved 52 novices, ages 19–31, completing a chair-design task; experienced-designer, collaborative, and other-domain use remain open questions.
- Preset dimensions may constrain creativity as well as scaffold it.
- Richer prompts can raise expectations beyond what current text-to-image models deliver reliably.
Paper abstract
The authors' summary
Generative AI has enabled novice designers to quickly create professional-looking visual representations for product concepts. However, novices have limited domain knowledge that could constrain their ability to write prompts that effectively explore a product design space. To understand how experts explore and communicate about design spaces, we conducted a formative study with 12 experienced product designers and found that experts—and their less-versed clients—often use visual references to guide co-design discussions rather than written descriptions. These insights inspired DesignWeaver, an interface that helps novices generate prompts for a text-to-image model by surfacing key product design dimensions from generated images into a palette for quick selection. In a study with 52 novices, DesignWeaver enabled participants to craft longer prompts with more domain-specific vocabularies, resulting in more diverse, innovative product designs. However, the nuanced prompts heightened participants' expectations beyond what current text-to-image models could deliver. We discuss implications for AI-based product design support tools.
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