The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Artistic User Expressions in AI-powered Creativity Support Tools
Abstract
Novel AI algorithms introduce a new generation of AI-powered Creativity Support Tools (AI-CSTs). These tools can inspire and surprise users with algorithmic outputs that the users could not expect. However, users can struggle to align their intentions with unexpected algorithmic behaviors. My dissertation research studies how user expressions in art-making AI-CSTs need to be designed. With an interview study with 14 artists and a literature survey on 111 existing CSTs, I first isolate three requirements: 1) allow users to express under-constrained intentions, 2) enable the tool and the user to co-learn the user expressions and the algorithmic behaviors, and 3) allow easy and expressive iteration. Based on these requirements, I introduce two tools, 1) Artinter, which learns how the users express their visual art concepts within their communication process for art commissions, and 2) TaleBrush, which facilitates the under-constrained and iterative expression of user intents through sketching-based story generation. My research provides guidelines for designing user expression interactions for AI-CSTs while demonstrating how they can suggest new designs of AI-CSTs.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Previous work proposed three requirements for the Creativity Support Tool (CST): 1) allow users to express under-constrained intentions; 2) enable the tool and the user to co-learn user expressions; 3) allow easy and expressive iteration.
“Previous work proposed three requirements for the Creativity Support Tool (CST) [14]: 1) allow users to express under-constrained intentions; 2) enable the tool and the user to co-learn user expressions; 3) allow easy and expressive iteration.”
From Sketchar: Supporting Character Design and Illustration Prototyping Using Generative AI (2024) · cites this paper as [14]
Citations
Cites
Hierarchical Text-Conditional Image Generation with CLIP Latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022) · arXiv · doi:10.48550/arxiv.2204.06125 · Cited by 2,511 · cited 1×
“Recent natural language prompt-based input approaches [1, 6] would allow users to express intentions roughly, but they also have limitations that the user might disagree with the tool on how they use verbal concepts.”
Background
Flagged first, then by citations
Research works printed but not linked: 6
- Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.). Vol. 33. Curran Associates, Inc., 1877–1901. https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
- 10.1145/3461778.3462050 · DOI
- 10.1145/3532106.3533505 · DOI · Paper page
- 10.1145/3491102.3501819 · DOI · Paper page
- Ian J. Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative Adversarial Networks. http://arxiv.org/abs/1406.2661 cite arxiv:1406.2661. · arXiv
- 10.1145/3491102.3502075 · DOI
Cited by
Sorted by citations
The Effects of Generative AI on Design Fixation and Divergent Thinking
Wadinambiarachchi, S., Kelly, R. M., Pareek, S., Zhou, Q., & Velloso, E. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642919 · Cited by 320
Sketchar: Supporting Character Design and Illustration Prototyping Using Generative AI
Ling, L., Chen, X., Wen, R., Li, T. J.-J., & LC, R. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3677102 · Cited by 52
“For example, when drawing an image of a brave man from the Maori community in New Zealand, it ended up showing a white male with indigenous-style tattoos (14).”
Zhang, S., Wang, H., & Yi, X. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757594 · Cited by 36
Metadata
- Authors
- Chung, J. J. Y.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Chung, John Joon Young
- Venue
- Adjunct Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology
- Publisher
- ACM
- Published
- 2022-10-28
- Pages
- 1-4
- OpenAlex
- W4307485554
- Cited by
- 14
- References
- 1
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 7 · 2023: 1
- FWCI
- 3.11
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Symposium on User Interface Software and Technology (UIST) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Digital Media and Visual Art
- Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Technology, and Culture
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- artistic creation, sketching, story generation, iterative design, art commissions, design guidelines
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 ›
Are you an author, or do you see a problem with this page? Request a review
