Generative adversarial networks–enabled human–artificial intelligence collaborative applications for creative and design industries: A systematic review of current approaches and trends.
Open accessAbstract
The future of work and workplace is very much in flux. A vast amount has been written about artificial intelligence (AI) and its impact on work, with much of it focused on automation and its impact in terms of potential job losses. This review will address one area where AI is being added to creative and design practitioners' toolbox to enhance their creativity, productivity, and design horizons. A designer's primary purpose is to create, or generate, the most optimal artifact or prototype, given a set of constraints. We have seen AI encroaching into this space with the advent of generative networks and generative adversarial networks (GANs) in particular. This area has become one of the most active research fields in machine learning over the past number of years, and a number of these techniques, particularly those around plausible image generation, have garnered considerable media attention. We will look beyond automatic techniques and solutions and see how GANs are being incorporated into user pipelines for design practitioners. A systematic review of publications indexed on ScienceDirect, SpringerLink, Web of Science, Scopus, IEEExplore, and ACM DigitalLibrary was conducted from 2015 to 2020. Results are reported according to PRISMA statement. From 317 search results, 34 studies (including two snowball sampled) are reviewed, highlighting key trends in this area. The studies' limitations are presented, particularly a lack of user studies and the prevalence of toy-examples or implementations that are unlikely to scale. Areas for future study are also identified.
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Datasets for generative AI need to contain sufficient data and high-quality labeled data to improve the accuracy and robustness of the algorithm.
“These datasets need to contain sufficient data and have high-quality labeled data to improve the accuracy and robustness of the algorithm (Hughes et al., 2021).”
From Generative artificial intelligence empowers educational reform: current status, issues, and prospects (2023) · cites this paper as Hughes et al., 2021
Generative AI needs to be trained on a large dataset to ensure its effectiveness and reliability.
“Generative AI needs to be trained on a large dataset to ensure its effectiveness and reliability.”
From Generative artificial intelligence empowers educational reform: current status, issues, and prospects (2023) · cites this paper as Hughes et al., 2021
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Newman, M., Sun, K., Dalla Gasperina, I. B., Shin, G. Y., Pedraja, M. K., Kanchi, R., Song, M. B., Li, R., Lee, J. H., & Yip, J. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642492 · Cited by 59 · see citing works
Vision Transformers (ViTs) for Feature Extraction and Classification of AI-Generated Visual Designs
Yun, Q. (2025) · IEEE Access · doi:10.1109/access.2025.3562130 · Cited by 13 · see citing works
Li, X., Li, T., Wang, M., Tao, S., Zhou, X., Wei, X., & Guan, N. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3539104 · Cited by 8 · see citing works
- Authors
- Hughes, R. T., Zhu, L., & Bednarz, T.
- Affiliations
- UNSW Sydney (AU)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Corresponding author
- Hughes, Rowan T.
- Venue
- Frontiers in Artificial Intelligence
- Publisher
- Frontiers Media SA
- Published
- 2021-04-28
- Volume
- 4
- Pages
- 604234-604234
- ISSN
- 2624-8212 (ISSN-L)
- PMC
- PMC8113684
- OpenAlex
- W3157327767
- Cited by
- 178
- References
- 91
- Citations by year
- 2026: 29 · 2025: 68 · 2024: 52 · 2023: 22 · 2022: 5 · 2021: 2
- FWCI
- 1.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Artificial Intelligence (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Commonwealth Scientific and Industrial Research Organization
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- 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
- Keywords
- generative adversarial networks, human-AI collaboration, creative design, design professionals, image generation, AI-assisted design, future of work, design productivity, generative design
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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