Smart "Error"! Exploring Imperfect AI to Support Creative Ideation
Fang Liu, Junyan Lv, Shenglan Cui, Zhilong Luan, Kui Wu, Tongqing Zhou
Abstract
Designers widely accept AI as a partner in the design process for its efficient and intelligent decision-making. However, AI is often not perfect, and AI error often makes humans dumbfounded. Literature has pointed out the value of such AI error, while still leaving its inspiration essence and application strategies uncharted from the practice perspective. This work focuses on bridging the practice gap by looking into and exploiting the imaginative "mislabeled" objects of object detection models. To gain insights into the inspiration of AI "error", we collected a dedicated AI "error" dataset from object detection and invited eight designers to share divergent comments on the "mislabeled" objects. Coding was then performed on the comments, which summarizes the inspiration of AI "error" into six atomic dimensions. Subsequently, we took a step further to an exploratory study, a comparative ideation experiment with 20 designers, investigating how to apply these inspiration dimensions to create ideas. Questionnaire and interview results revealed that essential inspiration of AI "error" could positively activate creativity, especially the "Outline" dimension. A design model CETR is then formulated by summarizing the application of atomic inspiration of "error" into four forms of creativity, which could be taken as a guideline for cooperative design with AI "error". In addition, we also sketch two approaches to generate more inspiring and applicable AI "error", elaborate on two principal characteristics of AI "error" for promoting creativity, and propose three strategies for better co-creating with AI "error". Finally, we provide insight into design research about AI self-awareness and human-AI collaboration.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Cited by top-tier papers4
- The AI Memory Gap: Users Misremember What They Created With AI or WithoutTim Zindulka, Sven Goller, Daniela Fernandes, Robin Welsch et al.CHI 2026 · 4 citations
- Reflective AI: A Slow Technology Approach for Design EducationVera van der Burg, Gijs de Boer, Jesse Josua Benjamin, Brett A. Halperin et al.CHI 2026 · 2 citations
- DesignMemo: Integrating Discussion Context into Online Collaboration with Enhanced Design Rationale TrackingBoyu Li, Linjie Qiu, Duotun Wang, Qianxi Liu et al.CSCW 2025 · 1 citation
- Participatory, not Punitive: Student-Driven AI Policy Recommendations in a Design ClassroomKaoru Seki, Manisha Vijay, Yasmine KotturiCHI 2026
Related papers
- Understanding Design Collaboration Between Designers and Artificial Intelligence: A Systematic Literature ReviewYang Shi, Tian Gao, Xiaohan Jiao, Nan CaoCSCW 2023 · 170 citations
- From Bias to Repair: Error as a Site of Collaboration and Negotiation in Applied Data Science WorkCindy Kaiying Lin, Steven J. JacksonCSCW 2023 · 22 citations
- Creativity from Surprise: Bridging the Gap Between Fashion Designers' Inspiration Work and AI Creative Support ToolsYu Jin, Yousang Kwon, Juhyeok Yoon, Bowen Zhan et al.CHI 2026 · 1 citation
- Owning Mistakes Sincerely: Strategies for Mitigating AI ErrorsAmama Mahmood, Jeanie W. Fung, Isabel Won, Chien-Ming HuangCHI 2022 · 50 citations
- Humanizing Machines: Rethinking LLM Anthropomorphism Through a Multi-Level Framework of DesignYunze Xiao, Lynnette Hui Xian Ng, Jiarui Liu, Mona T. DiabEMNLP 2025 · 2 citations
