CHI 2025 · When this paper helps
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.
Fit
This paper helps when a blank prompt box is asking novices to supply design vocabulary they have not had a chance to learn.
- You are designing dimensional scaffolds for text-to-image exploration.
- You want evidence about prompt language, visual diversity, novelty, and rising expectations.
What it adds
A palette that externalizes product-design dimensions, grounded in expert practice and evaluated with 52 novice designers.
Honest limit
The study used one chair-design task with novices, and richer prompts did not reliably improve satisfaction or expectation alignment.
Detailed evidence, contribution, and scope
Full citation fit
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 the paper contributes
- 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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