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AIdeation: Designing a Human-AI Collaborative Ideation System for Concept Designers

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Wang, Wen-Fan, Lu, Chien-Ting, Ponsa i Campanyà, Nil, Chen, Bing-Yu, Chen, Mike Y.·2025·Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3706598.3714148
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Abstract

Concept designers in the entertainment industry create highly detailed, often imaginary environments for movies, games, and TV shows. Their early ideation phase requires intensive research, brainstorming, visual exploration, and combination of various design elements to form cohesive designs. However, existing AI tools focus on image generation from user specifications, lacking support for the unique needs and complexity of concept designers' workflows. Through a formative study with 12 professional designers, we captured their workflows and identified key requirements for AI-assisted ideation tools. Leveraging these insights, we developed AIdeation to support early ideation by brainstorming design concepts with flexible searching and recombination of reference images. A user study with 16 professional designers showed that AIdeation significantly enhanced creativity, ideation efficiency, and satisfaction (all p<.01) compared to current tools and workflows. A field study with 4 studios for 1 week provided insights into AIdeation's benefits and limitations in real-world projects. After the completion of the field study, two studios, covering films, television, and games, have continued to use AIdeation in their commercial projects to date, further validating AIdeation's improvement in ideation quality and efficiency.

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  • Our sample of 26 exceeds the power analysis bound and aligns with prior HCI work on AI-supported evaluation and critique.

    “Our sample of 26 exceeds this bound and aligns with prior HCI work on AI-supported evaluation and critique [11, 17, 88].”

    From Criticmate: Stagewise Human–AI Co-Critique in Single-Screen UI Evaluation (2026) · cites this paper as [88]

    The Summative Study (p. 12) identifies this as HCI work evaluating an AI-supported ideation tool, so it supports the comparison to prior AI-related evaluation work. The Evaluation Approach (p. 13) describes participants assessing the tool, not AI-supported critique. The citing authors’ sample of 26 and power-analysis bound are their own claims, not findings attributed to this source. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Authors
Wang, W.-F., Lu, C.-T., Ponsa i Campanyà, N., Chen, B.-Y., & Chen, M. Y.
Affiliations
  • National Taiwan University (TW)
  • National Taiwan University, Taipei, Taiwan
ORCID
Venue
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2025-04-25
Pages
1-28
DOI
10.1145/3706598.3714148
OpenAlex
W4407814137
Cited by
50
References
14
Citations by year
2026: 36 · 2025: 14
FWCI
59.13
Type
conference paper
Language
en
Open access
Licence
Locations
  • CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Digital Media and Visual Art
  • Physical Sciences › Engineering › Mechanical Engineering › Design Education and Practice
  • Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
Keywords
conceptual design, idea generation, human-AI collaboration, brainstorming, creativity support, design workflow

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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