Development of Customized Textile Design using AI Technology

Jung, Dawool; Suh, Sung-Eun

doi:10.5850/jksct.2023.47.6.1137

J. Korean Soc. Cloth. Text

2023

DOI: 10.5850/jksct.2023.47.6.1137

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Development of Customized Textile Design using AI Technology

Dawool Jung,

Sung-Eun Suh

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2024

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Cited by 4 publications

References 30 publications

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Enhancing Soft Skills through Generative AI in Sustainable Fashion Textile Design Education

Jung,

Suh

2024

Sustainability

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This study explores the significance of incorporating soft skill training in fashion design education through the use of artificial intelligence (AI) technology and examines various AI-based approaches for sustainable fashion textile design education employing a multifaceted methodology that encompasses empirical, quantitative, and qualitative methods. We investigate the aspects of Design Sprints, identify key soft skills that help students meet the complex demands of contemporary fashion design workplaces, propose a curriculum guide for AI textile design programs, and evaluate the soft skill training process. Participants included students who had completed basic fashion design courses over three to four semesters and had experience with the fashion design process. The findings confirmed that participants’ soft skills improved across four areas—digital competence, sense of initiative and entrepreneurship, problem-solving and thinking skills, and communication—through the AI-based fashion textile design curriculum. This study validates the importance of integrating AI technology into educational programs to enhance essential soft skills in the digital fashion industry environment. Additionally, it emphasizes the necessity of developing AI technology-specialized design prompts while maintaining a balance between traditional design education and digital design education for sustainable fashion design education.

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Enhancing Soft Skills through Generative AI in Sustainable Fashion Textile Design Education

Jung,

Suh

2024

Sustainability

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AI Technology Integrated Education Model for Empowering Fashion Design Ideation

Lee,

Suh

2024

Sustainability

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The rapidly increasing importance of technology integration and generative AI in the fashion industry is prompting changes in fashion design education. This study explored a new design methodology utilizing AI for sustainable and future-oriented fashion design education. The methodology involved consulting practitioners to select ChatGPT and Midjourney as AI tools and technology, pedagogy, and content knowledge (TPACK) as the theoretical framework. An AI prompt guide was developed based on specialized books, from which an educational program was created. An experiment with 30 third- and fourth-year fashion design students showed that their overall satisfaction with AI through TPACK was 4 out of 5, suggesting that TPACK enhances students’ creativity and efficiency through generative AI. Prompt guides received a satisfaction score of 4.7, indicating their usefulness for creative and efficient design outputs. AI-powered educational programs, like ChatGPT and Midjourney, also improved student creativity and learning efficiency, with ChatGPT scoring 4.5. However, concerns about technology dependency were noted. This study offers insights into integrating the latest technology into fashion design to improve process efficiency and creative output. This study not only provides a foundation for future research on AI design methodology but also explores practical directions for sustainable design in the fashion industry.

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A Study on Digital Textile Design using Image-Generative AI

Seo,

Koo

2024

FTRJ

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Development of Customized Textile Design using AI Technology

Cited by 4 publications

References 30 publications

Enhancing Soft Skills through Generative AI in Sustainable Fashion Textile Design Education

Enhancing Soft Skills through Generative AI in Sustainable Fashion Textile Design Education

AI Technology Integrated Education Model for Empowering Fashion Design Ideation

A Study on Digital Textile Design using Image-Generative AI

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