Designing the Future: AI in Modern Textile Creations Rooted in Cultural Heritage, 2025

Authors

  • Hend Saleh Abdulghaffar

Keywords:

generative AI, Al-Qatt Al-Asiri, Al-Sadu, cultural heritage, textile design, fashion innovation, ethical AI, Saudi Arabia, DALL·E, participatory design.

Abstract

This study investigates the application of generative artificial intelligence (AI) to recreate historic Saudi textile arts, specifically Al-Qatt Al-Asiri and Al-Sadu, as modern fashion and design fields. By generating culturally motivated flat patterns and apparel using DALL-E, the aim of this study was to bridge the preservation of heritage with the creation of new content.

With culture-based stimuli, AI-generated outcomes were compared critically for beauty and cultural appropriateness by experts and were seen to be highly rated in both these aspects. The research then compares its findings with that of current regional and global research, confirming that AI is useful as an addendum to creative processes but needs ethical safeguards against cultural homogenization and design homogenation.

Results show that AI can support sustainable innovation in heritage-based design when properly managed and in conjunction with traditional artisans. The article contributes a theoretical framework combining posthuman design, decolonial theory, and participatory design that offers a path to culture-situated, ethically conscious textile innovation in the intelligent technology era.

This study highlights the ability of generative AI to link between cultural heritage and sustain modernity by rephrasing Saudi Arabia's Al-Qatt Al-Asiri and Al-Sadu motifs. Nevertheless, in order to strengthen the found evidence and develop it even more, future research should emphasize on prioritizing ethical practice and developing co-operation between AI developers and artisans, with the aim of guaranteeing long-term cultural preservation.

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Published

2026-10-05

How to Cite

Abdulghaffar, H. S. (2026). Designing the Future: AI in Modern Textile Creations Rooted in Cultural Heritage, 2025. International Journal of Artificial Intelligence and Machine Learning, 6(13s), 131–142. Retrieved from https://svedbergopen.com/index.php/ijaiml/article/view/2648