ARTIFICIAL INTELLIGENCE APPLICATIONS IN DESIGN

GRA250

Course Code: GRA250
Semester: Fourth Term (Spring)
Theory+Practice: 2 + 2
ECTS: 3

Course Type

Compulsory Course

Course Level

Bachelor's Degree

Year of Study

Semester

Fourth Term (Spring)

ECTS

Name of Lecturer(s)

Learning Outcomes

At the end of this course, the students;

Gains ability to use AI tools for creative purposes,
Ability to combine traditional and digital design approaches,
Ability to transform artistic content into visual design,
Understanding the creative collaboration between humans and AI,
Ability to think critically and evaluate.

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

Course Description

This course aims to explore how artificial intelligence (AI) is integrated into design processes and the new possibilities this integration offers. Throughout the course, students are not only expected to learn how to use AI tools, but also to understand the creative collaboration between artificial intelligence and human intelligence. By combining traditional design approaches with technologies such as generative AI models, machine learning, and automation, the course will equip students with the foundational knowledge and skills to shape the design practices of the future.

Course Contents

WEEK	TOPICS
1^st Week	Introduction and Roadmap
2^nd Week	Introduction to Generative AI
3^rd Week	Defining the Project Topic and Ideation
4^th Week	Visual Research and Moodboard Creation
5^th Week	Traditional Design Principles and AI
6^th Week	Creating the Poster's Rough Draft
7^th Week	Mid-Project Presentation and Critique
8^th Week	Collaboration Between Humans and AI
9^th Week	Advanced Control Techniques in AI
10^th Week	AI Ethics in Design
11^th Week	Preparation for Final Poster
12^th Week	Preparation for Final Poster
13^th Week	Preparation for Final Poster
14^th Week	Project Presentation and Evaluation

Recommended or Required Reading

AI by Design: A Plan for Living with Artificial Intelligence Catriona Campbell
Generative Art: A Practical Guide Using Processing Matt Pearson
The People + AI Guidebook Google People + AI Research
Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play David Foster
Designing Human-Centric AI Experiences: Applied UX Design for Artificial Intelligence Akshay Kore
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World Pedro Domingos
Resisting AI: An Anti-Fascist Toolkit for the 21st Century Dan McQuillan

Planned Learning Activities and Teaching Methods

Lecture
Uygulama
Project
Problem Solving

Assessment Methods and Criteria

	Quantity	Percentage (%)
Mid-term	1	40
Total(%)		0
Contribution of In-term Studies to Overall Grade(%)		0
Contribution of Final Examination to Overall Grade(%)		100
Total(%)		100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Midterm exam	1	2	2
Preparation for Quiz
Individual or group work	14	2	28
Preparation for Final exam
Course hours	14	4	56
Preparation for Midterm exam
Laboratory (including preparation)
Final exam	1	2	2
Homework
Project	1	4	4
Total Work Load			92
Total Work Load / 30 (h)			3.06
ECTS Credit of the Course			3

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3	LO4	LO5
K1		X
K2
K3
K4			X
K5					X
K6			X
K7
K8
K9
K10
K11
K12
K13
K14
K15
K16
K17	X			X

	LO1	LO2	LO3	LO4	LO5
K1		X
K2
K3
K4			X
K5					X
K6			X
K7
K8
K9
K10
K11
K12
K13
K14
K15
K16
K17	X			X

	LO1	LO2	LO3	LO4	LO5
K1		X
K2
K3
K4			X
K5					X
K6			X
K7
K8
K9
K10
K11
K12
K13
K14
K15
K16
K17	X			X

	LO1	LO2	LO3	LO4	LO5
K1		X
K2
K3
K4			X
K5					X
K6			X
K7
K8
K9
K10
K11
K12
K13
K14
K15
K16
K17	X			X