Human–AI Co-Creation in Traditional Craft: Generative Artificial Intelligence for Batik Design Innovation in Indonesian Creative Communities
DOI:
https://doi.org/10.59784/glosains.v7i3.799Keywords:
Human–AI Co-Creation, Generative AI, Batik Innovation, Traditional Craft, Cultural HeritageAbstract
Background: The declining generational renewal of batik designers poses challenges to sustaining innovation in Indonesia’s traditional craft industry.
Objective: This study investigates how generative artificial intelligence can support human–AI co-creation in batikdesign innovation.
Methods: Using a mixed-methods case study of a Batik AI training program organized by the Indonesian Batik Artisans and Entrepreneurs Association, this research examines the integration of AI-assisted design exploration within batikcreative communities. Data were collected through observations, documentation of AI-generated designs, and a perception survey involving 300 respondents from the batik ecosystem.
Results: The results indicate that generative AI substantially expands the exploration of motif variations, enabling participants to generate 300 batik design simulations—200 in Cirebon and 100 in Bandung—featuring traditional motifs, including parang, kawung, and megamendung. Descriptive statistical analysis involving 300 respondents revealed positive perceptions across all dimensions. Motif composition received the highest score (mean = 4.24, SD = 0.68), followed by color sharpness (mean = 4.21), motif authenticity (mean = 4.18), color brightness (mean = 4.15), originality of motif ideas (mean = 4.11), and the quality of supplementary motifs (mean = 4.07). The philosophical meaning of the motifs received the lowest score (mean = 3.96), indicating the limitations of AI in encoding cultural symbolism. The instrument demonstrated satisfactory reliability (Cronbach’s alpha = 0.87).
Conclusion: The findings suggest that generative AI functions as a collaborative tool that enhances the capacity for creative design exploration. However, respondents’ perceptions of the philosophical meaning of the motifs (mean = 3.96) indicate that human cultural curation remains indispensable for preserving the profound symbolic authenticity of traditional batik designs. These findings reflect respondents’ perceptions within the specific training contexts of Cirebon and Bandung.
References
Abdullah, F., bin Azizan, A. T., Adnan, A. M., Effendi, H., & Wardoyo, B. T. (2024). Indonesia-Malaysia Batik Pattern Collaboration Creation with Artificial Intelligence Platform. KnE Social Sciences, 180–186. https://doi.org/10.18502/kss.v9i15.16210
Adamson, G. (2013). The invention of craft. Bloomsbury Academic/V&A Publishing.
Adeleye, I. O. (2024). The impact of artificial intelligence on design: Enhancing creativity and efficiency. Journal of Engineering and Applied Sciences, 3(1), 1–14. https://doi.org/10.70560/vvsfej12
Bagnato, V. P. (2023). Artificial intelligence for design: the artificial intelligence of objects. Interdiscip. J. Archit. Built Environ, 27, 30–35. https://doi.org/10.37199/f40002705
Boden, M. A. (2016). AI: Its Nature and Future. Oxford University Press.
Candy, L., & Edmonds, E. (2018). Practice-based research in the creative arts: Foundations and futures from the front line. Leonardo, 51(1), 63-69.
Chandrasekera, T., Hosseini, Z., & Perera, U. (2025). Can artificial intelligence support creativity in early design processes? International Journal of Architectural Computing, 23(1), 122–136. https://doi.org/10.1177/14780771241254637
Colton, S., Wiggins, G. A., et al. (2015). Computational creativity: The final frontier? In Proceedings of the 24th International Joint Conference on Artificial Intelligence (pp. 2157-2164).
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Cross, N. (2011). Design Thinking: Understanding How Designers Think and Work. Berg Publishers.
Davis, R. L., Wambsganss, T., Jiang, W., Kim, K. G., Käser, T., & Dillenbourg, P. (2024). Fashioning creative expertise with generative AI: Graphical interfaces for design space exploration better support ideation than text prompts. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, 1–26. https://doi.org/10.1145/3613904.3642908
Davis, N., Hsiao, C.-P., Singh, K. Y., Li, L., & Magerko, B. (2016). Empirically studying participatory sense-making in abstract drawing with a co-creative cognitive agent. In Proceedings of the 21st International Conference on Intelligent User Interfaces (pp. 196-207).
Dormer, P. (1997). The culture of craft. Manchester University Press.
Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). CAN: Creative adversarial networks, generating “art” by learning about styles and deviating from style norms. arXiv preprint arXiv:1706.07068.
Fraser-Lu, S. (1989). Indonesian Batik: Processes, Patterns and Places. Oxford University Press.
Freese, S. (2023). AI in Co-Creation: The usability and impact of AI tools for co-creation in participatory design to generate innovative and user-centric design solutions.
Giaccardi, E., & Karana, E. (2015). Foundations of materials experience: An approach for HCI. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 2447-2456).
Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. (2016). Generative adversarial networks. Communications of the ACM, 63(11), 139-144.
Hertzmann, A. (2018). Can computers create art? Arts, 7(2), 18.
Hughes, R. T., Zhu, L., & Bednarz, T. (2021). Generative adversarial networks–enabled human–artificial intelligence collaborative applications for creative and design industries: A systematic review of current approaches and trends. Frontiers in Artificial Intelligence, 4, 604234. https://doi.org/10.3389/FRAI.2021.604234
Hutson, J., Lively, J., Robertson, B., Cotroneo, P., & Lang, M. (2023). Case Studies: Redefining Web Design and Co-creation. In Creative Convergence: The AI Renaissance in Art and Design (pp. 197–223). Springer. https://doi.org/10.1007/978-3-031-45127-0_7
Ibarrola, F., Lawton, T., & Grace, K. (2023). A collaborative, interactive and context-aware drawing agent for co-creative design. IEEE Transactions on Visualization and Computer Graphics, 30(8), 5525–5537. https://doi.org/10.1109/tvcg.2023.3293853
Kantosalo, A., & Toivonen, H. (2016). Modes for creative human-computer collaboration: Alternating and task-divided co-creativity. In Proceedings of the Seventh International Conference on Computational Creativity.
Latiff, M. A., Yassin, I., Zabidi, A., Mohd Noor, N. Q. B., Eskandari, F., Raju, R., Saadon, A., & Megat Ali, M. S. A. (2024). Harnessing the power of generative adversarial networks (GANs) for novel batik designs: An exploration of Lightweight GANs (LGANs) for automatic batik design. https://doi.org/10.32388/hhb1ca
McCormack, J., Gifford, T., & Hutchings, P. (2019). Autonomy, authenticity, authorship and intention in computer generated art. In Proceedings of the 10th International Conference on Computational Creativity (pp. 35-42).
Miller, A. I. (2019). The artist in the machine: The world of AI-powered creativity. Mit Press.
Norman, D. A. (2013). The Design of Everyday Things: Revised and Expanded Edition. Basic Books.
Putjorn, P., Prapattong, P., Boonleart, O., Hirunro, C., Charoensri, C., Champakaew, W., & Unroj, P. (2024). Hand, heart, and AI harmony: Integrating generative AI to innovate northern thai local wisdom. 2024 28th International Computer Science and Engineering Conference (ICSEC), 1–6. https://doi.org/10.1109/icsec62781.2024.10770642
Rezwana, J., & Maher, M. L. (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.
Rianto, R., Sela, E. I., & Wening, N. (2024). Pemanfaatan teknologi informasi untuk inovasi motif, diversifikasi produk, dan perluasan jaringan pasar pada batik nitik Kembangsore. I-Com: Indonesian Community Journal, 4(4), 2842–2854. https://doi.org/10.70609/icom.v4i4.5689
Sennett, R. (2008). The Craftsman. Yale University Press.
Song, H. (2024). Empowering Heritage through AI: LoRA Model for Digital Style Transfer in Traditional Cloth Pasting. Proceedings of the Twelfth International Symposium of Chinese CHI, 640–647. https://doi.org/10.1145/3758871.3758927
Tang, X., Yu, H., & Feng, Q. (2023). Capturing style: going beyond traditional artistic conventions through neural style transfer—evidence from Malaysia batik. Proceedings of the Eleventh International Symposium of Chinese CHI, 497–503. https://doi.org/10.1145/3629606.3629660
Veldhuisen, H. (1993). Batik Belanda 1840-1940: Dutch influence in batik from Java history and stories. (No Title).
Zhang, M., Cheng, Z., Shiu, S. T. R., Liang, J., Fang, C., Ma, Z., Fang, L., & Wang, S. J. (2023). Towards human-centred AI-co-creation: A three-level framework for effective collaboration between human and AI. Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing, 312–316. https://doi.org/10.1145/3584931.3607008 -e
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