Large-scale Text-to-Image Generation Models for Visual Artists’ Creative Works
Open accessAbstract
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.
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People with art skills seem to have more concerns about AI-generated images.
“People with art skills seem to have more concerns about AI-generated images [15, 36].”
From Sketchar: Supporting Character Design and Illustration Prototyping Using Generative AI (2024) · cites this paper as [36]
The Introduction says rapid improvements in text-to-image models left many visual artists perplexed, and §7 reports that some feared losing their jobs. These passages support concerns among people with art skills, but the paper does not establish that they have more concerns than people without those skills. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPrivacy Preservation focuses on maintaining data confidentiality and integrity, employing strategies like anonymization and federated learning to minimize direct data exposure, especially as generative AI poses risks of user profiling.
“This domain focuses on maintaining data confidentiality and integrity, employing strategies like anonymization and federated learning to minimize direct data exposure, especially when the rise of generative AI poses risks of user profiling [238], [239].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [239]
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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 · see citing works
“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.”
BackgroundCreativeConnect: 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 · see citing works
Self-citationPromptCharm: 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 · see citing works
“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].”
BackgroundMethodResultInfluentialMcIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
“…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].”
BackgroundIs It AI or Is It Me? Understanding Users’ Prompt Journey with Text-to-Image Generative AI Tools
Goloujeh, A. M., Sullivan, A., & Magerko, B. (2024) · International Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642861 · Cited by 132 · see citing works
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 93 · see citing works
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 87 · see citing works
Chakrabarty, T., Padmakumar, V., Brahman, F., & Muresan, S. (2024) · Creativity and Cognition · doi:10.1145/3635636.3656201 · Cited by 61 · see citing 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 · see citing works
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 67 · see citing works
“Likewise, Ko et al. (2023) interviewed visual artists and discovered that text-to-image models could help them think out of the box.”
InfluentialGenAssist: 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 · see citing works
“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].”
BackgroundSapkota, R., Meng, Z., & Karkee, M. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100614 · Cited by 58 · see citing works
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 53 · see citing works
Self-citationLarge 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 · see citing works
“Ko et al. [128] proposed a method for text-to-image for visual artists’ creative works.”
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 · see citing works
“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].”
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 51 · see citing works
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 · see citing works
“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].”
BackgroundInfluentialMim, 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 · see citing works
“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].”
BackgroundTowards 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 · see citing works
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 · see citing works
Park, H., Eirich, J., Luckow, A., & Sedlmair, M. (2024) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3679318.3685370 · Cited by 30 · see citing works
“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.”
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 · see citing works
“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].”
BackgroundResultPan, S., Anwar, R. B., Awang, N. N. B., & He, Y.-N. (2025) · Sustainability · doi:10.3390/su17030910 · Cited by 36 · see citing works
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 36 · see citing works
“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 · see citing works
Yi, Z., Xiao, T., & Albert, M. V. (2025) · Inf. · doi:10.3390/info16020136 · Cited by 39 · see citing works
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 29 · see citing works
(2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642476 · Cited by 30 · see citing works
“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-citationZhang, S., Qi, Y., & Wu, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-95819-9 · Cited by 38 · see citing works
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 · see citing works
“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].”
BackgroundCaires, 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 · see citing works
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 24 · see citing works
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 25 · see citing works
Alcaide-Marzal, J., & Diego-Más, J. A. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104168 · Cited by 26 · see citing works
“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 23 · see citing works
“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).”
Panchanadikar, R., & Freeman, G. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3677082 · Cited by 22 · see citing works
“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 31 · see citing works
“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 · see citing works
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 20 · see citing works
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 · see citing works
“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 · see citing works
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 19 · see citing works
“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].”
BackgroundVaranasi, R. A., Wiesenfeld, B. M., & Nov, O. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714035 · Cited by 18 · see citing works
“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 17 · see citing works
“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-citationLin, H., Ye, Y., Xia, J., & Zeng, W. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713801 · Cited by 18 · see citing works
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 · see citing works
“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].”
BackgroundKatirai, A., García, N., Ide, K., Nakashima, Y., & Kishimoto, A. (2024) · AI and Ethics · doi:10.1007/s43681-024-00517-3 · Cited by 17 · see citing works
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 · see citing works
“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].”
Elsharif, W., Alzubaidi, M. S., & Agus, M. (2025) · IEEE Access · doi:10.1109/access.2025.3585745 · Cited by 18 · see citing works
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 13 · see citing works
“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 13 · see citing works
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 13 · see citing works
Self-citationThe 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 · see citing works
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 14 · see citing works
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 · see citing works
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 · see citing works
“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].”
Lodovico, C. D., Torrielli, F., Di, L., & Rapp, A. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2491009 · Cited by 12 · see citing works
“…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 11 · see citing works
“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.”
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 13 · see citing works
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 10 · see citing works
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 11 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
“…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].”
BackgroundChatHAP: 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 · see citing works
Self-citationA 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 9 · see citing works
“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. (2024) · CHI Extended Abstracts · doi:10.1145/3613905.3650947 · Cited by 17 · see citing works
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 8 · see citing works
“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 · see citing works
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 · see citing works
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 11 · see citing works
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 · see citing works
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 · see citing works
Zhang, J., Wang, Y., Tohidypour, H. R., & Nasiopoulos, P. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10221905 · Cited by 6 · see citing works
“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].”
BackgroundMatsuhira, 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 · see citing works
“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].”
BackgroundInfluentialUnderstanding 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 5 · see citing works
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 7 · see citing works
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 5 · see citing works
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 · see citing works
“The dependence on text prompting in text-to-image generative models further limits the generation of truly novel images, constraining creative possibilities [54].”
InfluentialHan, 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 5 · see citing works
“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 5 · see citing works
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 5 · see citing works
Ogawa, N., Okafuji, Y., Hatada, Y., & Baba, J. (2025) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3757686 · Cited by 7 · see citing works
Gentet, P., Coffin, M., Gentet, Y., & Lee, S.-H. (2024) · Photonics · doi:10.3390/photonics11090787 · Cited by 4 · see citing works
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 · see citing works
How Do Generative Models Draw a Software Engineer? A Case Study on Stable Diffusion Bias
Fadahunsi, T., d’Aloisio, G., Marco, A. D., & Sarro, F. (2025) · IEEE International Conference on Software Analysis, Evolution and Reengineering - Companion (SANER-C) · doi:10.1109/saner-c66551.2025.00009 · Cited by 4 · see citing works
Xu, L., Dhonnchadha, E. U., & Ward, M. (2023) · EUROCALL Conference · doi:10.4995/eurocall2023.2023.16950 · Cited by 4 · see citing works
Palmini, M.-T. D. R., & Cetinić, E. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732158 · Cited by 3 · see citing works
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 3 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
“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 · see citing works
“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
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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 3 · see citing works
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 3 · see citing works
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 · see citing works
Wang, Z., Fernández, N., & Vines, J. (2025) · Designing Interactive Systems Conference · doi:10.1145/3715336.3735684 · Cited by 3 · see citing works
Inteligencia Artificial. Conceptos clave y tendencias para la innovación educativa
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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 · see citing works
- 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)
- Venue
- Proceedings of the 28th International Conference on Intelligent User Interfaces
- Publisher
- ACM
- Published
- 2023-03-27
- Pages
- 919-933
- 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
- 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.
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