Cited by 190Digital Media and Visual ArtGenerative Adversarial Networks and Image SynthesisArt, Technology, and Culture

The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.

Large-scale Text-to-Image Generation Models for Visual Artists’ Creative Works

Open access

Ko, Hyung-Kwon, Park, Gwanmo, Jeon, Hyeon, Jo, Jaemin, Kim, Juho, Seo, Jinwook·2023·Proceedings of the 28th International Conference on Intelligent User Interfaces

DOI 10.1145/3581641.3584078

Open this paper in CoChat — free account Open PDF

Abstract

Large-scale Text-to-image Generation Models (LTGMs) (e.g., DALL-E), self-supervised deep learning models trained on a huge dataset, have demonstrated the capacity for generating high-quality open-domain images from multi-modal input. Although they can even produce anthropomorphized versions of objects and animals, combine irrelevant concepts in reasonable ways, and give variation to any user-provided images, we witnessed such rapid technological advancement left many visual artists disoriented in leveraging LTGMs more actively in their creative works. Our goal in this work is to understand how visual artists would adopt LTGMs to support their creative works. To this end, we conducted an interview study as well as a systematic literature review of 72 system/application papers for a thorough examination. A total of 28 visual artists covering 35 distinct visual art domains acknowledged LTGMs’ versatile roles with high usability to support creative works in automating the creation process (i.e., automation), expanding their ideas (i.e., exploration), and facilitating or arbitrating in communication (i.e., mediation). We conclude by providing four design guidelines that future researchers can refer to in making intelligent user interfaces using LTGMs.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1145/3581641.3584078

Cites

80 of 80 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01042 · Cited by 20,302
Snowball Sampling
Goodman, L. A. (1961) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177705148 · Cited by 3,606
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676
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
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
(2020) · doi:10.1145/3313831 · Cited by 1,951
Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy
Shneiderman, B. (2020) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2020.1741118 · Cited by 1,743
Human-Centered AI
Shneiderman, B. (2022) · doi:10.1093/oso/9780192845290.001.0001 · Cited by 1,185
Mapping the landscape of sustainable HCI
DiSalvo, C. F., Sengers, P., & Brynjarsdóttir, H. (2010) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753625 · Cited by 838
What is Mixed Reality?
Speicher, M., Hall, B. D., & Nebeling, M. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300767 · Cited by 765

Show all 71 references

Research works printed but not linked: 22

Toshiki Aoki, Rintaro Chujo, Katsufumi Matsui, Saemi Choi, and Ari Hautasaari. 2022. EmoBalloon-Conveying Emotional Arousal in Text Chats with Speech Balloons. In CHI Conference on Human Factors in Computing Systems. 1–16.
Renaud Bougueng Tchemeube, Jeffrey John Ens, and Philippe Pasquier. 2022. Calliope: A Co-creative Interface for Multi-Track Music Generation. In Creativity and Cognition. 608–611.
Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology 3, 2 (2006), 77–101.
Dilrukshi Gamage, Piyush Ghasiya, Vamshi Bonagiri, Mark E Whiting, and Kazutoshi Sasahara. 2022. Are Deepfakes Concerning? Analyzing Conversations of Deepfakes on Reddit and Exploring Societal Implications. In CHI Conference on Human Factors in Computing Systems. 1–19.
WOMBO Inc.2022. Dream by WOMBO. Retrieved October 5, 2022 from https://www.wombo.art
Ellen Jiang, Edwin Toh, Alejandra Molina, Kristen Olson, Claire Kayacik, Aaron Donsbach, Carrie J Cai, and Michael Terry. 2022. Discovering the Syntax and Strategies of Natural Language Programming with Generative Language Models. In CHI Conference on Human Factors in Computing Systems. 1–19.
10.1145/3377325.3377522 · DOI
Philippe Laban, Elicia Ye, Srujay Korlakunta, John Canny, and Marti Hearst. 2022. NewsPod: Automatic and Interactive News Podcasts. In 27th International Conference on Intelligent User Interfaces. 691–706.
Vivian Liu and Lydia B Chilton. 2022. Design Guidelines for Prompt Engineering Text-to-Image Generative Models. In CHI Conference on Human Factors in Computing Systems. 1–23.
Vivian Liu, Han Qiao, and Lydia Chilton. 2022. Opal: Multimodal Image Generation for News Illustration. In The 35th Annual ACM Symposium on User Interface Software and Technology. 1–17.
Yihe Liu, Anushk Mittal, Diyi Yang, and Amy Bruckman. 2022. Will AI Console Me when I Lose my Pet? Understanding Perceptions of AI-Mediated Email Writing. In CHI Conference on Human Factors in Computing Systems. 1–13.
Mohammad Amin Mozaffari, Xinyuan Zhang, Jinghui Cheng, and Jin LC Guo. 2022. GANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial Network. In CHI Conference on Human Factors in Computing Systems. 1–15.
Aadarsh Padiyath and Brian Magerko. 2021. desAIner: Exploring the Use of” Bad” Generative Adversarial Networks in the Ideation Process of Fashion Design. In Creativity and Cognition. 1–3.
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. Zero-shot text-to-image generation. In International Conference on Machine Learning. PMLR, 8821–8831.
Margarete Sandelowski. 1995. Sample size in qualitative research. Research in nursing & health 18, 2 (1995), 179–183.
Oliver Schmitt and Daniel Buschek. 2021. Characterchat: Supporting the creation of fictional characters through conversation and progressive manifestation with a chatbot. In Creativity and Cognition. 1–10.
Ben Shneiderman and Catherine Plaisant. 2010. Designing the user interface: Strategies for effective human-computer interaction. Pearson Education India.
Michihiko Ueno and Shin’ichi Satoh. 2021. Continuous and Gradual Style Changes of Graphic Designs with Generative Model. In 26th International Conference on Intelligent User Interfaces. 280–289.
Fenna Van Nes, Tineke Abma, Hans Jonsson, and Dorly Deeg. 2010. Language differences in qualitative research: is meaning lost in translation?European journal of ageing 7, 4 (2010), 313–316.
Maarten Van Someren, Yvonne F Barnard, and J Sandberg. 1994. The think aloud method: a practical approach to modelling cognitive. London: AcademicPress 11(1994).
Ann Yuan, Andy Coenen, Emily Reif, and Daphne Ippolito. 2022. Wordcraft: Story Writing With Large Language Models. In 27th International Conference on Intelligent User Interfaces. 841–852.
Chao Zhang, Cheng Yao, Jiayi Wu, Weijia Lin, Lijuan Liu, Ge Yan, and Fangtian Ying. 2022. StoryDrawer: A Child–AI Collaborative Drawing System to Support Children’s Creative Visual Storytelling. In CHI Conference on Human Factors in Computing Systems. 1–15.

Sorted by citations

A Survey on Multimodal Large Language Models for Autonomous Driving
Cui, C., Ma, Y., Cao, X., Ye, W., Zhou, Y., Liang, K., Chen, J., Lu, J., Yang, Z., Liao, K.-D., et al. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00106 · Cited by 369
“These works high-light the potential of multimodal learning in capturing complex relationships between different types of data, leading to richer and more accurate representations.”
Background
CreativeConnect: Supporting Reference Recombination for Graphic Design Ideation with Generative AI
Choi, D., Hong, S., Park, J., Chung, J. J. Y., & Kim, J. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642794 · Cited by 124
Self-citation
PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement
Wang, Z., Huang, Y., Song, D., Ma, L., & Zhang, T. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642803 · Cited by 115
“As a complementary study to the prior research, Ko et al. conducted an interview study with 28 visual artists to help the research community understand the potential and design guidelines of using large-scale text-to-image models for visual art creations [31].”
BackgroundMethodResultInfluential
From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape
McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102
“…including images, audio, and video, by engaging in the generation of artistic content, encompassing applications in idea generation, storytelling, poetry, music composition, visual arts, and creative writing, and has resulted in commercial success like MidJourney and DALL-E [210], [211], [212].”
Background
Is It AI or Is It Me? Understanding Users’ Prompt Journey with Text-to-Image Generative AI Tools
Goloujeh, A. M., Sullivan, A., & Magerko, B. S. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642861 · Cited by 122
NeuralLift-360: Lifting an in-the-Wild 2D Photo to A 3D Object with 360° Views
Xu, D., Jiang, Y., Wang, P., Fan, Z., Wang, Y., & Wang, Z. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00435 · Cited by 112
DesignPrompt: Using Multimodal Interaction for Design Exploration with Generative AI
Peng, X., Koch, J., & Mackay, W. E. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3661588 · Cited by 110
Creativity Support in the Age of Large Language Models: An Empirical Study Involving Professional Writers
Chakrabarty, T., Padmakumar, V., Brahman, F., & Muresan, S. (2024) · Creativity and Cognition · doi:10.1145/3635636.3656201 · Cited by 61
How do people experience the images created by generative artificial intelligence? An exploration of people's perceptions, appraisals, and emotions related to a Gen-AI text-to-image model and its creations
Rapp, A., Lodovico, C. D., Torrielli, F., & Di, L. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103375 · Cited by 80
“Likewise, Ko et al. (2023) interviewed visual artists and discovered that text-to-image models could help them think out of the box.”
Influential
GenAssist: Making Image Generation Accessible
Huh, M., Peng, Y.-H., & Pavel, A. (2023) · Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3586183.3606735 · Cited by 67
“Advances in large-scale generative models enable people to create new types of content, yet no existing research has explored people with disabilities as the users of these tools [28].”
Background
Synthetic meets authentic: Leveraging LLM generated datasets for YOLO11 and YOLOv10-based apple detection through machine vision sensors
Sapkota, R., Meng, Z., & Karkee, M. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100614 · Cited by 58
GenQuery: Supporting Expressive Visual Search with Generative Models
Son, K., Choi, D., Kim, T. S., Kim, Y., & Kim, J. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642847 · Cited by 71
Self-citation
Large language models: an overview of foundational architectures, recent trends, and a new taxonomy
Mienye, I. D., Jere, N., Obaido, G., Ogunruku, O. O., Esenogho, E., & Modisane, C. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07668-w · Cited by 46
“Ko et al. [128] proposed a method for text-to-image for visual artists’ creative works.”
AIdeation: Designing a Human-AI Collaborative Ideation System for Concept Designers
Wang, W.-F., Lu, C.-T., Ponsa i Campanyà, N., Chen, B.-Y., & Chen, M. Y. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714148 · Cited by 50
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
“To address this issue, future user studies would involve recruiting game designers and experienced illustrators to work together on a task, as found in the We-toon study, which recruited webtoon writers to use the tool and artists to do evaluations [35].”
PlantoGraphy: Incorporating Iterative Design Process into Generative Artificial Intelligence for Landscape Rendering
Huang, R., Lin, H., Chen, C., Kang Zhang, A., & Zeng, W. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642824 · Cited by 59
"Being Eroded, Piece by Piece": Enhancing Engagement and Storytelling in Cultural Heritage Dissemination by Exhibiting GenAI Co-Creation Artifacts
Fu, K., Wu, R., Tang, Y., Chen, Y., Liu, B., & Lc, R. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3660711 · Cited by 45
“Compared to text-to-text generation tools, text-to-image generators are convenient in ideation processes because they can quickly produce images [53], which enables real-time visual communication beyond the vagueness of text output [40].”
BackgroundInfluential
In-Between Visuals and Visible: The Impacts of Text-to-Image Generative AI Tools on Digital Image-making Practices in the Global South
Mim, N. J., Nandi, D., Khan, S. S., Dey, A., & Ahmed, S. I. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641951 · Cited by 42
“For instance, Ko et al. have documented large-scale text-to-image generation models’ limitations in supporting personalization in image-making and text promptings-induced restrictions in creativity [74].”
Background
Towards a Diffractive Analysis of Prompt-Based Generative AI
Rajcic, N., Llano Rodriguez, M. T., & McCormack, J. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641971 · Cited by 37
Agency in Human-AI Collaboration for Image Generation and Creative Writing: Preliminary Insights from Think-Aloud Protocols
Rafner, J., Zana, B., Bang Hansen, I., Ceh, S., Sherson, J., Benedek, M., & Lebuda, I. (2025) · Creativity Research Journal · doi:10.1080/10400419.2025.2587803 · Cited by 38
"We Are Visual Thinkers, Not Verbal Thinkers!": A Thematic Analysis of How Professional Designers Use Generative AI Image Generation Tools
Park, H., Eirich, J., Luckow, A., & Sedlmair, M. (2024) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3679318.3685370 · Cited by 30
“Additionally, Ko et al. [28] interviewed 28 visual artists across various art domains to explore the potential adoption of large-scale text-to-image generation models in the creative processes.”
Sound Designer-Generative AI Interactions: Towards Designing Creative Support Tools for Professional Sound Designers
Kamath, P., Morreale, F., Bagaskara, P. L., Wei, Y., & Nanayakkara, S. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642040 · Cited by 37
“This fnding is in line with some recent research on CSTs in the visual domain, in music composition, and in storytelling where algorithmic tools were used predominantly for idea generation [14, 20, 22, 53, 61, 62].”
BackgroundResult
Constructing a Sustainable Evaluation Framework for AIGC Technology in Yixing Zisha Pottery: Balancing Heritage Preservation and Innovation
Pan, S., Anwar, R. B., Awang, N. N. B., & He, Y.-N. (2025) · Sustainability · doi:10.3390/su17030910 · Cited by 36
RealtimeGen: An Intervenable AI Image Generation System for Commercial Digital Art Asset Creators
Li, Z., Zhang, Y., Zhou, S., Liu, Q., Zhang, J., Xu, H., Chen, S., Chen, X., & Sun, L. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2382508 · Cited by 39
“On the other hand, AI tools can perform efficient automated creation in a short time, greatly improving painting productivity (Chiou et al., 2023; Ko et al., 2023; Liu et al., 2022).”
Diffusion-Based Visual Art Creation: A Survey and New Perspectives
Wang, B., Chen, Q., & Wang, Z. (2025) · ACM Computing Surveys · doi:10.1145/3728459 · Cited by 40
A Survey on Multimodal Large Language Models in Radiology for Report Generation and Visual Question Answering
Yi, Z., Xiao, T., & Albert, M. V. (2025) · Information · doi:10.3390/info16020136 · Cited by 43
From Paper to Card: Transforming Design Implications with Generative AI
Shin, D.-H., Wang, L. L., & Hsieh, G. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642266 · Cited by 35
Unlocking creator-AI synergy: Challenges, requirements, and design opportunities in AI-powered short-form video production
Kim, J., & Kim, H. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642476 · Cited by 30
“Recent works in Human-Computer Interaction (HCI) and Computer-Supported Cooperative Work (CSCW) investigate the impact of AI (Artificial Intelligence) on creative work [27, 29, 75, 76].”
BackgroundSelf-citation
Applying deep learning for style transfer in digital art: enhancing creative expression through neural networks
Zhang, S., Qi, Y., & Wu, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-95819-9 · Cited by 38
Artinter: AI-powered Boundary Objects for Commissioning Visual Arts
Chung, J. J. Y., & Adar, E. (2023) · Proceedings of the 2023 ACM Designing Interactive Systems Conference · doi:10.1145/3563657.3595961 · Cited by 33
“many of these do not necessarily consider the user’s style, which can introduce friction in the art commission setting, as artists would like to maintain their own unique styles, unless they are open to new ideas and styles [14, 40].”
Background
Design Thinking Methodology and Text-To-Image Artificial Intelligence: A Case Study in the Context of Furniture Design Education
Caires, C. S., Estadieu, G. V., & Man, S. O. N. K. (2023) · Springer series in design and innovation · doi:10.1007/978-3-031-41770-2_7 · Cited by 25
AI-Assisted Causal Pathway Diagram for Human-Centered Design
Zhong, R., Shin, D.-H., Meza, R. D., Klasnja, P., Colusso, L. F., & Hsieh, G. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642179 · Cited by 28
World and Human Action Models towards gameplay ideation
Kanervisto, A., Bignell, D., Wen, L., Grayson, M., Georgescu, R., Macua, S. V., Tan, S. Z., Rashid, T., Pearce, T., Cao, Y., et al. (2025) · Nature · doi:10.1038/s41586-025-08600-3 · Cited by 36
Computers as co-creative assistants. A comparative study on the use of text-to-image AI models for computer aided conceptual design
Alcaide-Marzal, J., & Diego-Más, J. A. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104168 · Cited by 29
“Journey of Finding the Best Query”: Understanding the User Experience of AI Image Generation System
Kim, S., Eun, J., Oh, C., & Lee, J. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2307670 · Cited by 29
“Recent research on AI for creativity has explored how AI-based image generation systems can help visual artists in the field (Ko et al., 2022).”
"I'm a Solo Developer but AI is My New Ill-Informed Co-Worker": Envisioning and Designing Generative AI to Support Indie Game Development
Panchanadikar, R., & Freeman, G. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3677082 · Cited by 43
“Game Development While there has been substantial discourse surrounding the utilization of generative AI in various creative fields [8, 18, 20, 24, 27, 32, 36, 39, 63, 67, 74], research specifically focusing on its application within the game development domain has been notably scarce.”
The Value of Creativity: Human Produced Art vs. AI-Generated Art
Hall, J., & Schofield, D. (2025) · Art and Design Review · doi:10.4236/adr.2025.131005 · Cited by 22
“With algorithms capable of producing complex and aesthetically pleasing pieces, AI has emerged as both a tool and a collaborator in the artistic process (Boden, 2016; Elgammal, 2017; Ko et al., 2023).”
Designing the Future: A Case Study on Human-AI Co-Innovation
Chen, J.-F., Ni, C.-C., Lin, P.-H., & Lin, R. (2024) · Creative Education · doi:10.4236/ce.2024.153028 · Cited by 21
PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content Creation
Reza, M., Anastasopoulos, I., Bhandari, S., & Pardos, Z. A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714051 · Cited by 32
Deepfakes on Demand: The rise of accessible non-consensual deepfake image generators
Hawkins, W., Mittelstadt, B., & Russell, C. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732107 · Cited by 20
“Initial models such as DALL-E and Midjourney were popular among artistic communities as a way to create and share AI-generated artworks [29].”
Crafting user-centric prompts for UI generations based on Kansei engineering and knowledge graph
Feng, X., Du, H., Ma, J., Wang, H., Zhou, L., & Wang, M. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103217 · Cited by 20
Embodying an Interactive AI for Dance Through Movement Ideation
Wallace, B., Hilton, C., Nymoen, K., Tørresen, J., Martin, C. P., & Fiebrink, R. A. (2023) · Creativity and Cognition · doi:10.1145/3591196.3593336 · Cited by 20
“While the potential of these models can be viewed as a means of democratizing artistic fields, there also exist concerns among the creative practitioners in these fields regarding their future job security [30].”
Background
AI Rivalry as a Craft: How Resisting and Embracing Generative AI Are Reshaping the Writing Profession
Varanasi, R. A., Wiesenfeld, B. M., & Nov, O. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714035 · Cited by 23
“These insights contribute a novel perspective to understanding Human-AI (dis)engagement, a field of literature that has predominantly focused on cooperation and collaboration practices [63, 84, 108].”
The Affective Nature of AI-Generated News Images: Impact on Visual Journalism
Paik, S., Bonna, S., Novozhilova, E., Gao, G., Kim, J., Wijaya, D. T., & Betke, M. (2023) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii59096.2023.10388166 · Cited by 23
“In recent years, text-to-image generative AI systems based on multimodal deep learning models have shown advancements in their ability to produce affective images that are of comparable quality to human photographers and artists [1], [2], [3].”
BackgroundSelf-citation
SketchFlex: Facilitating Spatial-Semantic Coherence in Text-to-Image Generation with Region-Based Sketches
Lin, H., Ye, Y., Xia, J., & Zeng, W. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713801 · Cited by 28
Dreamscaping: Supporting Creativity By Drawing Inspiration from Dreams
Liu, S., Lc, R., Fu, K., Wan, Q., Liu, P., & Holopainen, J. P. (2024) · Creativity and Cognition · doi:10.1145/3635636.3660762 · Cited by 16
“Generative AI (GenAI) provides a tool that even entry-level users can use for converting natural language prompts[18] into visualizations and representations of abstract and vague concepts[12–14].”
Background
Situating the social issues of image generation models in the model life cycle: a sociotechnical approach
Katirai, A., García, N., Ide, K., Nakashima, Y., & Kishimoto, A. (2024) · AI and Ethics · doi:10.1007/s43681-024-00517-3 · Cited by 19
Lumina: A Software Tool for Fostering Creativity in Designing Chinese Shadow Puppets
Yao, Z., Lu, Y., Sun, Q., Lyu, S., Li, H., Yang, X.-D., Wang, X., Liu, G., & Mi, H. (2024) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3654777.3676426 · Cited by 15
“While efective in many diferent ways, many of the early works fall short of providing sufcient controls to users to better generate personalized details [9, 16, 27].”
Cultural Bias in Text-to-Image Models: A Systematic Review of Bias Identification, Evaluation, and Mitigation Strategies
Elsharif, W., Alzubaidi, M. S., & Agus, M. (2025) · IEEE Access · doi:10.1109/access.2025.3585745 · Cited by 26
Current and Future Use of Large Language Models for Knowledge Work
Brachman, M., El-Ashry, A., Dugan, C., & Geyer, W. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757403 · Cited by 39
“Increased automation of tasks can improve productivity, reducing tedious and manual tasks, but also has challenges.”
PromptMap: An Alternative Interaction Style for AI-Based Image Generation
Adamkiewicz, K., Woźniak, P. W., Dominiak, J., Romanowski, A., Karolus, J., & Frolov, S. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712150 · Cited by 17
Expandora: Broadening Design Exploration with Text-to-Image Model
Choi, D., Son, K., Jung, H., & Kim, J. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720189 · Cited by 21
Self-citation
The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores and Acceptance Rates
Russo, G., Horta Ribeiro, M., Davidson, T. R., Veselovsky, V., & West, R. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757667 · Cited by 12
MemoVis: A GenAI-Powered Tool for Creating Companion Reference Images for 3D Design Feedback
Chen, C., Nguyen, C., Groueix, T., Kim, V. G., & Weibel, N. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3694681 · Cited by 25
GeneyMAP: Exploring the Potential of GenAI to Facilitate Mapping User Journeys for UX Design
Mei, Y.-H., Wu, Z., Yu, J.-N., Li, W., & Zhou, Z.-B. (2025) · International Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713479 · Cited by 18
"Salt is the Soul of Hakka Baked Chicken": Reimagining Traditional Chinese Culinary ICH for Modern Contexts Without Losing Tradition
Liu, S., Zeng, X., Wu, F., Ye, S., Liu, B., Cheung, S. C. H., Allen, R. W., & Lc, R. (2025) · Creativity and Cognition · doi:10.1145/3698061.3726917 · Cited by 12
“Due to its recent advancements in reimagining and visualizing abilities [46, 73, 88], GenAI shows promise in expressively reinterpreting and recreating cultural heritage [32, 39, 41].”
How Do People Develop Folk Theories of Generative AI Text-to-Image Models? A Qualitative Study on How People Strive to Explain and Make Sense of GenAI
Lodovico, C. D., Torrielli, F., Di, L., & Rapp, A. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2491009 · Cited by 13
“…and utilization of these models among various user groups, including online communities (Buraga, 2022), creative professionals (Inie et al., 2023; Ko et al., 2023; Vimpari et al., 2023; Zhang, Wang, et al., 2023), and laypeo-ple (Wang et al., 2023), as well as people’s perceptions of the…”
Paratrouper: Exploratory Creation of Character Cast Visuals Using Generative AI
Leong, J., Ledo, D., Driscoll, T. R., Grossman, T., Fitzmaurice, G. W., & Anderson, F. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714242 · Cited by 15
“In interviews, Ko et al. [33] found that visual artists may find value in adopting the use of diffusion-based text-to-image models to automate the creation process, expand ideas, and support communication and collaboration.”
What Social Media Use Do People Regret? An Analysis of 34K Smartphone Screenshots with Multimodal LLM
Guo, L., Fu, Y., Lin, X., Xu, X., Chang, Y.-J., & Hiniker, A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713724 · Cited by 15
Do It For Me vs. Do It With Me: Investigating User Perceptions of Different Paradigms of Automation in Copilots for Feature-Rich Software
Khurana, A., Su, X., Wang, A. Y., & Chilana, P. K. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713431 · Cited by 20
StyleFactory: Towards Better Style Alignment in Image Creation through Style-Strength-Based Control and Evaluation
Zhou, M., Zhang, D., You, W., Yu, Z., Wu, Y., Pan, C., Liu, H., Lao, T., & Chen, P. (2024) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3654777.3676370 · Cited by 15
“Nevertheless, the former may involve obstacles in cross-modal translation [66], while the latter could bring about a more complex blend of style features [25].”
Palette, Purpose, Prototype: The Three Ps of Color Design and How Designers Navigate Them
Hegemann, L., & Oulasvirta, A. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641976 · Cited by 9
GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation
Gong, J., Li, S., d’Aloisio, G., Ding, Z., Ye, Y., Langdon, W. B., & Sarro, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-64573-0_7 · Cited by 9
Cultural Relevance Index: Measuring Cultural Relevance in AI-Generated Images
Elsharif, W., Agus, M., Alzubaidi, M., & She, J. P. M. (2024) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr62202.2024.00071 · Cited by 9
“…of prompts for speeding-up the refinement process [10], or by considering multimodal strategies like the usage of sketch-based image prompts as initial steps for the generation process [11], in a way to provide artists powerful expressive media able to conveys their ideas and emotions [12].”
Background
ChatHAP: A Chat-Based Haptic System for Designing Vibrations through Conversation
Lim, C., John, K., Jin, G., Seifi, H., & Park, G. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713441 · Cited by 9
Self-citation
A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image Synthesis
Hei, N., Guo, Q., Wang, Z., Wang, Y., Wang, H., & Zhang, W. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.27986 · Cited by 11
“Concurrently, recent studies have revealed that prompt design plays a cru-* cial role in determining the quality of the generated images (Ko et al. 2023; Liu, Qiao, and Chilton 2022).”
The Social Construction of Generative AI Prompts
Goloujeh, A. M., Sullivan, A., & Magerko, B. S. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3650947 · Cited by 17
Understanding the Potentials and Limitations of Prompt-based Music Generative AI
Choi, Y., Moon, J., Yoo, J., & Hong, J.-H. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713762 · Cited by 12
“Therefore, understanding the characteristics of users’ creative domains, their experience levels, and the goals of their creative processes greatly influences creator-GenAI communication [26, 36, 80].”
The effects of generative AI model type and visual stimuli type on design creativity
Ge, W., & Hou, G. (2025) · Artificial intelligence for engineering design analysis and manufacturing · doi:10.1017/s0890060425100061 · Cited by 7
How Disclosing Generative AI Use Impacts Human Creative Involvement in Human-GenAI CollaborationPreprint
Bauer, K., Jussupow, E., Heigl, R. M., Vogt, B., & Hinz, O. F. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4782554 · Cited by 7
Exploring Higher Education Faculty Insights on Generative AI in Creative Courses
Basty, R., Kropczynski, J., & Halse, S. E. (2025) · Journal of Information Technology Education Research · doi:10.28945/5546 · Cited by 14
Shapley visual transformers for image-to-text generation
Belhadi, A., Djenouri, Y., Belbachir, A. N., Michalak, T., & Srivastava, G. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112205 · Cited by 6
Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image Models
Ventura, M., Ben-David, E., Korhonen, A., & Reichart, R. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00732 · Cited by 10
Detecting Stable Diffusion Generated Images Using Frequency Artifacts: A Case Study on Disney-Style Art
Zhang, J., Wang, Y., Tohidypour, H. R., & Nasiopoulos, P. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10221905 · Cited by 7
“Although these powerful deep learning models can support artists in their works [5], they also became a topic of heated debates, as illustrators and content owners complain that their work is “stolen” for training and creating images, without their permission [6].”
Background
Interpolating the Text-to-Image Correspondence Based on Phonetic and Phonological Similarities for Nonword-to-Image Generation
Matsuhira, C., Kastner, M. A., Komamizu, T., Hirayama, T., Doman, K., Kawanishi, Y., & Ide, I. (2024) · IEEE Access · doi:10.1109/access.2024.3378095 · Cited by 7
“Several studies have explored this, indicating that the outputs of T2I generation models do not match human expectations especially when the text input contains words that have no definition within a language (which we term nonwords ) [6], [7], [8], [9].”
BackgroundInfluential
Understanding User Perceptions and the Role of AI Image Generators in Image Creation Workflows
Han, S.-J., & Fussell, S. R. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713227 · Cited by 7
Designer experiences and perspectives on the role of generative AI in industrial design
Süner-Pla-Cerdà, S., Şen, G., Kumbasar, E., Şahin, B., & Ünlü, C. E. (2025) · AI & Society · doi:10.1007/s00146-025-02504-6 · Cited by 9
GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design
Wang, W.-F., Lee, T.-Y., Lu, C.-T., Hsu, C., Campanyà, N. P. I., Chen, Y., Chen, M. Y., & Chen, B. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747774 · Cited by 10
Impacts of AI on Human Designers: A Systematic Literature Review
Sinlapanuntakul, P., & Zachry, M. R. (2025) · IEEE Transactions on Professional Communication · doi:10.1109/tpc.2025.3588655 · Cited by 6
“The dependence on text prompting in text-to-image generative models further limits the generation of truly novel images, constraining creative possibilities [54].”
Influential
POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generation
Han, E. X., Zhang, A. Q., Zhu, H., Shen, H., Liang, P. P., & Hsieh, J. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747710 · Cited by 6
“Generative AI tools are revolutionizing creative domains such as de-sign[12,59,78],visualart[43],marketing[5,33,49],gamecharacter art[60],fashion[39,40],andjournalism[58],drivingaparadigm shiftforcreativeproductionprocesses.”
From Text to Pixels: Enhancing User Understanding through Text-to-Image Model Explanations
Evirgen, N., Wang, R., & Chen, X. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640543.3645173 · Cited by 7
Understanding the Use of AI-Based Audio Generation Models by End-Users
Han, J., Yang, E., & Oh, U. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3650785 · Cited by 7
Understanding Collaboration between Professional Designers and Decision-making AI: A Case Study in the Workplace
Ogawa, N., Okafuji, Y., Hatada, Y., & Baba, J. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757686 · Cited by 8
From Text to Hologram: Creation of High-Quality Holographic Stereograms Using Artificial Intelligence
Gentet, P., Coffin, M., Gentet, Y., & Lee, S.-H. (2024) · Photonics · doi:10.3390/photonics11090787 · Cited by 4
InkSpirit: An expert knowledge-driven approach for enhancing the visual logic of traditional Chinese painting text-to-image generation
Zhang, D., Chen, Y., Gao, X., Zeng, X., Xia, R., Jiang, Y., Feng, Y., Zhang, W., & Chen, W. (2025) · Computers & Graphics · doi:10.1016/j.cag.2025.104330 · Cited by 4
How Do Generative Models Draw a Software Engineer? A Case Study on Stable Diffusion Bias
Fadahunsi, T., d'Aloisio, G., di Marco, A., & Sarro, F. (2025) · IEEE International Conference on Software Analysis, Evolution, and Reengineering · doi:10.1109/saner-c66551.2025.00009 · Cited by 7
Harnessing the power of images in CALL: AI image generation for context specific visual aids in less commonly taught languages
Xu, L., Dhonnchadha, E. U., & Ward, M. (2023) · EUROCALL Conference · doi:10.4995/eurocall2023.2023.16950 · Cited by 4
Exploring Language Patterns of Prompts in Text-to-Image Generation and Their Impact on Visual Diversity
Palmini, M.-T. D. R., & Cetinic, E. (2025) · Conference on Fairness, Accountability and Transparency · doi:10.1145/3715275.3732158 · Cited by 6
PosterMate: Audience-driven Collaborative Persona Agents for Poster Design
Shin, D.-H., Lee, D. W., Hsieh, G., & Chan, G. Y.-Y. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747769 · Cited by 15
COMPARISON OF AI-GENERATED CONCEPTUAL INTERIOR DESIGNS USING STANDARDIZED PROMPTS IN VARIOUS AI MODELS
Kahraman, M. U., Şekerci, Y., & KILIÇ, E. C. (2025) · The Turkish Online Journal of Design Art and Communication · doi:10.7456/tojdac.1736051 · Cited by 3
T2IBias: Uncovering Societal Bias Encoded in the Latent Space of Text-to-Image Generative Models
Sufian, A., Distante, C., Leo, M., & Salam, H. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-16886-3_4 · Cited by 3
Research on the application and evaluation of Mongolian saddle culture in kumis packaging design based on AIGC
Zhao, Z., Wang, X., Wang, M., Zhang, X., & Song, W. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-06948-z · Cited by 3
“In recent years, Arti ﬁ cial Intelligence Content Generation (AIGC) technology has emerged as a transformative tool in the creative industries (Ko et al., 2023).”
Transforming Creative Process: A Systematic Literature Review of Discourse on AI Image Generators
Lee, H. S., & Luo, M. (2024) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.1162 · Cited by 3
“There is an ongoing conversation about the potential benefits of using this innovative technology and its available feature usefulness in the creative process (Ko et al., 2023; Tholander & Jonsson, 2023; Vimpari et al., 2023).”
Automatic granary sweeping strategy using visual large language model
Zhang, B., Yan, J., Gao, Y., Yang, G., Zhang, K., & Li, J. (2025) · Journal of Stored Products Research · doi:10.1016/j.jspr.2025.102619 · Cited by 3
MVPrompt: Building Music-Visual Prompts for AI Artists to Craft Music Video Mise-en-scène
Lee, C., Lee, D., & Hong, J.-H. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713876 · Cited by 6
The Impact of Artificial Intelligence on Graphic Design
Durusoy, O. (2025) · Technium Romanian Journal of Applied Sciences and Technology · doi:10.47577/technium.v30i.12868 · Cited by 5
Design Education in Human-AI Co-Creation: A Qualitative Analysis of Using Image Generative Models in Conceptual Design
Shou, Y., Zhang, X., Wang, C., Shen, A., Fan, H., Cai, R., Yang, Y., Zhou, J., Chai, C., & Sun, S. (2024) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid63852.2024.00014 · Cited by 3
How Does AI Represent Social Concepts? Examining the Visual Representation of Care in Text-to-Image Tools
Wang, Z., Fernández, N., & Vines, J. (2025) · Designing Interactive Systems Conference · doi:10.1145/3715336.3735684 · Cited by 3
Inteligencia Artificial. Conceptos clave y tendencias para la innovación educativa
Zermeño, M. G. G. (2023) · Editorial Transdigital eBooks · doi:10.56162/transdigitalb22 · Cited by 6
Designing with AI: Integrating Image-Generative AI into Conceptual Design in a CAD Class
Zhu, W., Guo, R., Wang, Y., Xing, W., Kim, E. M., & Li, C. (2024) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--47146 · Cited by 3

Authors: Ko, H.-K., Park, G., Jeon, H., Jo, J., Kim, J., & Seo, J.
Affiliations: Korea Advanced Institute of Science and Technology (KR)
Seoul National University (KR)
Sungkyunkwan University (KR)
ORCID: Ko, Hyung-Kwon
Park, Gwanmo
Jeon, Hyeon
Jo, Jaemin
Kim, Juho
Seo, Jinwook
Venue: Proceedings of the 28th International Conference on Intelligent User Interfaces
Publisher: ACM
Published: 2023-03-27
Pages: 919-933
DOI: 10.1145/3581641.3584078
OpenAlex: W4321275178
Cited by: 189
References: 71
Citations by year: 2026: 43 · 2025: 70 · 2024: 61 · 2023: 15
FWCI: 55.32
Type: conference paper
Language: en
Open access: bronze
doi.org
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University) (cc-by)
Funders: National Research Foundation of Korea (NRF-2019R1A2C2089062,NRF-2019R1A2C1088900)
Information & communications Technology Planning & Evaluation(IITP) (2019-0-00075,2019-0-00421)
Topics: Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Digital Media and Visual Art
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Technology, and Culture
Keywords: text-to-image generation, visual artists, intelligent user interfaces, design guidelines, DALL-E

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

Large-scale Text-to-Image Generation Models for Visual Artists’ Creative Works

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks