Published: 2025-04-01
Generative Artificial Intelligence: Trends, Prospects, and Implications for the Creative Industry and Synthetic Data
DOI: 10.35870/ijsecs.v5i1.3274
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Abstract
Generative AI Generative AI has gained traction in both the creative industry and synthetic data generation. Trends and Directions in Generative Artificial Intelligence Research Aims and Research Questions This study explores the trends, possibilities and consequences of synthetic AI using both quantitative and qualitative methods. Methods We will collect information by reviewing literature, surveying 150 professionals and academic researchers, and conducting 20 semi-structured interviews with experts and industry leaders. Analysis of the reviewed literature reveals the large and growing popularity of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) as the two most prominent methods, but also the hike in popularity for transformers. Most respondents in the survey have used generative AI. The study found that 85% of respondents have experience with such AI, and eight-out-10 see it enabling increased creativity and innovation (90%), efficiency and scalability (80%) and the algorithmic provision of data as training sets for model training (75%). However, generative AI also faces challenges such as output quality (60%), complexity and computation (55%), and ethical and legal implications (70%). Interviews with experts added a deeper perspective, emphasizing the importance of transparency, accountability, and clear regulation. Quantitative and qualitative data analysis shows that generative AI has significant potential but needs improvement in technical and ethical aspects. The study's recommendations include improving output quality, computational efficiency, developing regulations, and committing to transparency
Keywords
Generative AI ; GANs (Generative Adversarial Networks) ; VAEs (Variational Autoencoders) ; Transformers ; Synthetic Data ; Creative Industries
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 5 No. 1 (2025)
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Section: Articles
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Published: %750 %e, %2025
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License: CC BY 4.0
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Copyright: © 2025 Authors
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DOI: 10.35870/ijsecs.v5i1.3274
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Alkautsar Rahman
Informatics Study Program, Faculty of Computer Science and Information Systems, Universitas Kebangsaan Republik Indonesia, Bandung City, West Java Province, Indonesia
Caroline
Development Economics Study Program, Faculty of Economics and Social Sciences, Universitas Sultan Fatah, Demak Regency, Central Java Province, Indonesia
Novita Souisa
Management Economics Study Program, STIE Bukit Zaitun Sorong, Sorong City, West Papua Province, Indonesia
-
Kalota, F. (2024). A primer on generative artificial intelligence. Education Sciences, 14(2), 172. https://doi.org/10.3390/educsci14020172
-
Oluwagbenro, M. (2024). Generative AI: Definition, concepts, applications, and future prospects. https://doi.org/10.36227/techrxiv.171746875.59016695/v1
-
Carnovalini, F., & Rodà, A. (2020). Computational creativity and music generation systems: An introduction to the state of the art. Frontiers in Artificial Intelligence, 3. https://doi.org/10.3389/frai.2020.00014
-
Mungoli, N. (2023). Revolutionizing industries: The impact of artificial intelligence technologies. Journal of Electrical Electronics Engineering, 2(3). https://doi.org/10.33140/jeee.02.03.03
-
Baidya, M., & Kumar, C. A. (2024). Artificial intelligence in education: Understanding benefits, limitations, and prospects for the future. International Journal for Multidisciplinary Research, 6(2), 1-9. https://doi.org/10.36948/ijfmr.2024.v06i02.16699
-
Charles, F. (2023). AI-Powered Personalized Mobile Education for New Zealand Students. International Journal Software Engineering and Computer Science (IJSECS), 3(1), 33–39. https://doi.org/10.35870/ijsecs.v3i1.1115
-
Dmitrieva, E., Balmiki, V., Lakhanpal, S., Lavanya, G., & Bhandari, P. (2024). AI evolution in Industry 4.0 and Industry 5.0: An experimental comparative assessment. Bio Web of Conferences, 86, 01069. https://doi.org/10.1051/bioconf/20248601069
-
Farhan, A. (2023). The impact of artificial intelligence on human workers. Journal of Communication Education, 17(2). https://doi.org/10.58217/joce-ip.v17i2.350
-
Derev’yanko, N., & Zalevska, O. (2023). Comparative analysis of neural networks MidJourney, Stable Diffusion, and DALL-E and ways of their implementation in the educational process of students of design specialities. Scientific Bulletin of Mukachevo State University Series “Pedagogy and Psychology”, 9(3), 36-44. https://doi.org/10.52534/msu-pp3.2023.36
-
Lee, H., & Luo, M. (2024). Transforming creative process: A systematic literature review of discourse on AI image generators. Proceedings of the Association for Information Science and Technology, 61(1), 986-988. https://doi.org/10.1002/pra2.1162
-
Chung, J. (2023). AI luddites: Consumers penalize creative work output generated by artificial intelligence. https://doi.org/10.21203/rs.3.rs-3444321/v1
-
Zubala, A., Pease, A., Lyszkiewicz, K., & Hackett, S. (2025). Art psychotherapy meets creative AI: An integrative review positioning the role of creative AI in art therapy process. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1548396
-
Fathoni, A. (2023). Leveraging generative AI solutions in art and design education: Bridging sustainable creativity and fostering academic integrity for innovative society. E3s Web of Conferences, 426, 01102. https://doi.org/10.1051/e3sconf/202342601102
-
Rezwana, J., & Maher, M. (2023). Designing creative AI partners with COFI: A framework for modeling interaction in human-AI co-creative systems. ACM Transactions on Computer-Human Interaction, 30(5), 1-28. https://doi.org/10.1145/3519026
-
Arbaiza, F., Arias, J., & Robledo-Dioses, K. (2024). AI-driven advertising activity: Perspectives from Peruvian advertisers. Communication & Society, 273-292. https://doi.org/10.15581/003.37.4.273-292
-
Doshi, A., & Hauser, O. (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(28). https://doi.org/10.1126/sciadv.adn5290
-
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems (NIPS), 27, 2672-2680.
-
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684-10695).
-
Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4401-4410).
-
Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
-
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., ... & Lerchner, A. (2017). Beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. International Conference on Learning Representations (ICLR).
-
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
-
Chen, Y., Zhang, Y., & Wang, Y. (2021). Synthetic data for training deep learning models in healthcare. IEEE Journal of Biomedical and Health Informatics.
-
Zhang, Y., Chen, Y., & Wang, Y. (2020). Generating synthetic datasets for evaluating machine learning models. Journal of Machine Learning Research.
-
Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image style transfer using convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2414-2423).
-
Boulanger-Lewandowski, N., Bengio, Y., & Vincent, P. (2012). Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. Proceedings of the 29th International Conference on Machine Learning (ICML). https://doi.org/10.48550/arXiv.1206.6392
-
Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538(7625), 311-313. https://doi.org/10.1038/538311a
-
Friedman, A., & Nissim, K. (2020). Privacy and security in synthetic data generation. ACM Transactions on Privacy and Security.
-
Zhang, Y., Chen, Y., & Wang, Y. (2018). Personalized content generation with deep learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT).
-
Mateas, M., & Sengers, P. (2003). Chapter 1. Narrative intelligence. In Narrative Intelligence (pp. 1-25). John Benjamins Publishing Company. https://doi.org/10.1075/aicr.46.01mat
-
Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784. https://doi.org/10.48550/arXiv.1411.1784
-
Kingma, D. P., & Welling, M. (2013). Efficient training of generative models with variational autoencoders. arXiv preprint arXiv:1312.6114.
-
Gatys, L. A., Ecker, A. S., & Bethge, M. (2017). Neural style transfer: A review. arXiv preprint arXiv:1705.04058.
-
Boulanger-Lewandowski, N., Bengio, Y., & Vincent, P. (2012). Deep learning for music generation: A review. arXiv preprint arXiv:1206.6392.
-
Wang, X., Liu, Q., & Jiang, J. (2020). Deep learning for video generation: A review. arXiv preprint arXiv:2004.07320.
-
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., & Xiao, J. (2016). Deep learning for 3D object generation. arXiv preprint arXiv:1612.00101.
-
Zhang, Y., Chen, Y., & Wang, Y. (2021). Synthetic data for machine learning: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering.
-
Chen, Y., Zhang, Y., & Wang, Y. (2021). Synthetic data for healthcare: A review. IEEE Journal of Biomedical and Health Informatics.
-
Crawford, K., & Calo, R. (2020). Ethical considerations in generative AI: A comprehensive review. Journal of Ethics and Information Technology.
-
Friedman, A., & Nissim, K. (2021). Privacy and security in generative AI: A comprehensive review. ACM Transactions on Privacy and Security.
-
Zhang, Y., Chen, Y., & Wang, Y. (2020). Personalized marketing with generative AI: A comprehensive review. Journal of Marketing Research.
-
Mateas, M., & Sengers, P. (2005). Interactive entertainment with generative AI: A comprehensive review. Entertainment Computing.
-
Wang, X., Liu, Q., & Jiang, J. (2021). Personalized learning with generative AI: A comprehensive review. Educational Technology Research and Development.
-
Lee, H., & Luo, M. (2024). Transforming creative process: A systematic literature review of discourse on AI image generators. Proceedings of the Association for Information Science and Technology, 61(1), 986-988. https://doi.org/10.1002/pra2.1162
-
Anantrasirichai, N., & Bull, D. (2021). Artificial intelligence in the creative industries: A review. Artificial Intelligence Review, 55(1), 589-656. https://doi.org/10.1007/s10462-021-10039-7
-
Cai, D. (2024). Enhancing capabilities of generative models through VAE-GAN integration: A review. Applied and Computational Engineering, 67(1), 239-246. https://doi.org/10.54254/2755-2721/67/2024ma0070
-
Inie, N., Falk, J., & Tanimoto, S. (2023). Designing participatory AI: Creative professionals’ worries and expectations about generative AI. Proceedings of the ACM on Human-Computer Interaction, 1-8. https://doi.org/10.1145/3544549.3585657
-
Xue, F. (2024). AI integration in creative industries: Challenges and opportunities. Applied and Computational Engineering, 104(1), 21-27. https://doi.org/10.54254/2755-2721/104/20240906
-
Sturm, B., Iglesias, M., Ben-Tal, O., Miron, M., & Gómez, E. (2019). Artificial intelligence and music: Open questions of copyright law and engineering praxis. Arts, 8(3), 115. https://doi.org/10.3390/arts8030115
-
Corrêa, N., Galvão, C., Santos, J., Pino, C., Pinto, E., Barbosa, C., & Oliveira, N. (2023). Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance. Patterns, 4(10), 100857. https://doi.org/10.1016/j.patter.2023.100857
-
Röchner, P. (2024). On the fidelity-privacy tradeoff of synthetic cancer registry data. Studies in Health Technology and Informatics, 287, 104-111. https://doi.org/10.3233/SHTI240490
-
Howie, J., Balasubramanian, S., Bambi, J., Moselle, K., Srinivasan, V., & Thomo, A. (2024). Synthetic generation of patient service utilization data: A scalability study. Studies in Health Technology and Informatics, 287, 112-119. https://doi.org/10.3233/SHTI240511.
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