Information Package / Course Catalogue
Artificial Intelligence Applications in Fashion Design
Course Code: MTS124
Course Type: Required
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 0
Credit: 2
Lab: 0
ECTS: 2
Objectives of the Course

The aim of this course is to provide fashion design students with practical skills in generative artificial intelligence, visual artificial intelligence, and data-driven design approaches, relating them to textiles, color, surface, pattern, garment form, collection development, and garment manufacturing processes. Upon completion of the course, students are expected to develop AI-assisted fashion design outputs while adhering to ethical principles, copyright, personal data security, and professional responsibility.

Course Content

English course description: This course introduces artificial intelligence applications in fashion design, including generative AI, prompt design, trend research, moodboard development, color palette generation, surface and pattern design, textile material interpretation, garment silhouette ideation, collection presentation, production planning, quality-control awareness, ethical use, copyright issues and AI-supported portfolio development.

Name of Lecturer(s)
Learning Outcomes
1.This document explains the fundamental applications of artificial intelligence in fashion design, textiles, and apparel.
2.The design problem develops AI commands/prompts appropriate to the target user, season, color, surface, and material context.
3.AI-powered trend research produces moodboard, color palette, and concept design outputs.
4.It develops and evaluates AI-supported alternatives in textile surface, texture, pattern, print, and accessory design.
5.It utilizes AI outputs in the processes of preparing garment forms, silhouettes, collection themes, and technical presentation files.
6.The garment production flow develops AI-supported decision recommendations for quality control, sustainability, and material selection problems
7.It applies the principles of copyright, originality, personal data, professional ethics, and academic integrity in the use of artificial intelligence.
8.The AI-powered fashion design team critically presents, receives feedback on, and revises their portfolio.
Recommended or Required Reading
1.AI-powered fashion design application handouts, prompt guides, and sample project files to be prepared by the instructor.
2.Up-to-date open course materials explaining fundamental concepts of artificial intelligence, generative models, data literacy, and responsible AI use.
3.User manuals for AI, visual design, CAD, and portfolio tools accessible by the institution or suitable for free/academic use.
4.Institutional/national/international guidelines regarding copyright, licensing, personal data, academic integrity, and the productive use of artificial intelligence.
Weekly Detailed Course Contents
Week 1 - Theoretical
Course Introduction: The transformation of artificial intelligence in the fields of fashion, textiles, and apparel.
Week 2 - Theoretical
Generative AI, visual AI, data, prompt and output evaluation logic.
Week 3 - Theoretical
Design problem, target audience, season, theme and trend research.
Week 4 - Theoretical
Moodboard, storyboard and concept development.
Week 5 - Theoretical
Color knowledge, color palette, seasonal colors, and visual harmony.
Week 6 - Theoretical
Textile surface, texture, pattern, print and repeat arrangement design.
Week 7 - Theoretical
Material, fabric structure, leather/textile surface, and accessory ideas.
Week 8 - Theoretical
Material, fabric structure, leather/textile surface, and accessory ideas.
Week 9 - Theoretical
Clothing form, silhouette, model variation, and collection integrity.
Week 10 - Theoretical
Technical drawing, product card, model description, and presentation language.
Week 11 - Theoretical
Garment production flow, product tree, sequence of operations, and quality perspective.
Week 12 - Theoretical
Image-based quality control, surface defect awareness, and an introduction to data literacy.
Week 13 - Theoretical
Sustainable design, waste reduction, reuse, and ethical use of artificial intelligence.
Week 14 - Theoretical
Final portfolio, collection presentation, peer feedback, and revisions.
Assessment Methods and Criteria
Type of AssessmentCountPercent
Presentation1%5
Assignment1%5
Midterm Examination1%30
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Assignment110010
Presentation 110010
Midterm Examination110010
Final Examination120020
TOTAL WORKLOAD (hours)50
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
OÇ-1
2
2
3
3
2
3
2
3
2
2
2
OÇ-2
3
2
3
2
2
3
3
2
2
3
3
OÇ-3
2
2
4
2
3
1
4
5
OÇ-4
3
2
3
2
2
OÇ-5
2
3
2
4
3
OÇ-6
2
2
3
2
2
3
OÇ-7
2
3
2
3
2
2
OÇ-8
2
3
2
2
3
2
2
Adnan Menderes University - Information Package / Course Catalogue
2026