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DesignWeaver: Dimensional Scaffolding for Text-to-Image Product Design

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Tao, Sirui, Liang, Ivan, Peng, Cindy, Wang, Zhiqing, Palani, Srishti, Dow, Steven P.·2025·Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3706598.3714211
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Abstract

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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  • The selection process resulted in 16 interviews, a sample size consistent with prior exploratory research with hard-to-reach expert populations.

    “The selection process resulted in 16 interviews, a sample size consistent with prior exploratory research with hard-to-reach expert populations [see e.g. 4, 32, 154] (§7.5).”

    From “It’s Just a Wild, Wild West”: Harnessing Public Procurement as an AI Governance Mechanism (2026) · cites this paper as [154]

    Section 3 (Formative Study; page unavailable) reports 12 interviews with experienced designers and says that sample size aligns with prior formative HCI studies, supporting the comparison to a 16-interview exploratory study. It does not characterize its expert participants as hard to reach, so it does not establish that part of the comparison. The 16 interviews are the citing study’s own count, not a count attributed to DesignWeaver. (LLM, conf=0.79)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Tao, S., Liang, I., Peng, C., Wang, Z., Palani, S., & Dow, S. P.
Affiliations
  • University of California San Diego (US)
  • Tableau Research, Palo Alto, CA, USA
ORCID
Venue
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2025-04-25
Pages
1-26
DOI
10.1145/3706598.3714211
OpenAlex
W4407632381
Cited by
21
References
9
Citations by year
2026: 19 · 2025: 2
FWCI
78.13
Type
conference paper
Language
en
Open access
Licence
Locations
  • CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by-nc)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc)
  • arXiv (Cornell University)
Funders
NSF (2009003)
Topics
  • Physical Sciences › Engineering › Mechanical Engineering › Design Education and Practice
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Digital Media and Visual Art
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
Keywords
text-to-image generation, generative AI in design, novice designers, prompt engineering, design dimensions, visual references, co-design, design space exploration, product design, design support tools

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