From couture to code: reimagining fabric development through AI-enhanced material narratives
From couture to code: reimagining fabric development through AI-enhanced material narratives
As artificial intelligence (AI) becomes more embedded in fashion design, questions arise about the values these systems encode. Current tools emphasize speed, novelty and surface aesthetics, often overlooking the cultural, emotional and material dimensions of sustainable practice. This article proposes Slow AI: a speculative, ethically grounded framework rooted in fabric memory, narrative depth and material literacy. Drawing on material culture theory and studio-based research, it critiques how generative AI simulates sustainability while neglecting provenance and care. Through case studies of Marine Serre, BODE and FAÇON JACMIN, the article shows how designers already model the relational intelligence AI might one day support. A speculative toolkit outlines functions such as archive mapping, emotionally annotated datasets and story-aligned prompts. Rather than accelerating production, Slow AI reframes design as a process of co-authorship, rooted in repair, friction and the ethics of making.
slow fashion, artificial intelligence, fabric memory, speculative design, sustainable aesthetics, material narrative
Coats, Matthew
7e54315f-55b2-48fe-a6a8-61e4c075ffae
Coats, Matthew
7e54315f-55b2-48fe-a6a8-61e4c075ffae
Coats, Matthew
(2025)
From couture to code: reimagining fabric development through AI-enhanced material narratives.
Fashion, Style & Popular Culture.
(doi:10.1386/fspc_00359_1).
Abstract
As artificial intelligence (AI) becomes more embedded in fashion design, questions arise about the values these systems encode. Current tools emphasize speed, novelty and surface aesthetics, often overlooking the cultural, emotional and material dimensions of sustainable practice. This article proposes Slow AI: a speculative, ethically grounded framework rooted in fabric memory, narrative depth and material literacy. Drawing on material culture theory and studio-based research, it critiques how generative AI simulates sustainability while neglecting provenance and care. Through case studies of Marine Serre, BODE and FAÇON JACMIN, the article shows how designers already model the relational intelligence AI might one day support. A speculative toolkit outlines functions such as archive mapping, emotionally annotated datasets and story-aligned prompts. Rather than accelerating production, Slow AI reframes design as a process of co-authorship, rooted in repair, friction and the ethics of making.
Text
From Couture to Code (Accepted Manuscript)
- Accepted Manuscript
Restricted to Repository staff only until 8 November 2026.
Request a copy
More information
Accepted/In Press date: 4 August 2025
e-pub ahead of print date: 8 November 2025
Keywords:
slow fashion, artificial intelligence, fabric memory, speculative design, sustainable aesthetics, material narrative
Identifiers
Local EPrints ID: 505055
URI: http://eprints.soton.ac.uk/id/eprint/505055
ISSN: 2050-0726
PURE UUID: a61c111b-8bfe-4c51-9d53-5ba17bf8bef0
Catalogue record
Date deposited: 25 Sep 2025 16:46
Last modified: 15 Nov 2025 03:16
Export record
Altmetrics
Contributors
Author:
Matthew Coats
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics