ARTIFICIAL INTELLIGENCE ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
DES4913	Visual Expression of Idea	Spring	2	0	2	4

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Non-Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Instructor MURAD BABADAĞ
Course Lecturer(s):	Instructor INGI FERNANDEZ
Recommended Optional Program Components:	None
Course Objectives:	Ensuring that students recall the representational languages of architecture, interior architecture, and industrial design disciplines, as well as how these languages are used. Helping students understand what the common representational languages of these three disciplines are and how they can be used together. Additionally, explaining the fundamental concepts of eliminating scale differences in interdisciplinary work. Enabling students to put into practice their ability to work collaboratively in a workshop setting by overcoming scale differences across disciplines. In this process, applying their skills to establish communication channels effectively using different representational languages. Encouraging students to analyze the similarities and differences among the three disciplines and, based on these analyses, determine the methods for obtaining conceptual content. Supporting students in developing new and creative communication channels by utilizing interdisciplinary collaboration and representational languages. In this process, they are expected to synthesize theoretical and practical knowledge and generate innovative solutions. Encouraging students to evaluate the communication channels and representational languages they have developed, discussing which methods are most effective and which strategies are more efficient The aim of the course is to provide the student with theoretical and practical knowledge about the methods of obtaining conceptual content in communication channels that will be developed by using all representation languages used by these three disciplines by working together and side by side in a workshop in a way that can eliminate the scale difference in these different disciplines.

Learning Outcomes

The students who have succeeded in this course;
Defines iconography and conceptual metaphors.
Identifies the representational languages commonly used by all three disciplines.
Analyzes content-form relationships and interprets them from the perspective of different disciplines.
Constructs fundamental visual structures and applies them across different disciplines.
Implements methods to eliminate scale differences in interdisciplinary work.
Evaluates the collaborative dynamics of three different disciplines.
Resolves content-form relationships through an experimental approach.
Assesses and improves the effectiveness of interdisciplinary collaboration.
Conducts critical evaluations of conceptually driven visual compositions.
Designs a conceptual visual composition.

- elimination of interdisciplinary scale differences
- Increasing the ability of three different disciplines to work together
- development of experiential thinking skills on content-form relations
- experience on representational languages common to all three disciplines- define visual communication,
- define iconography and conceptual metaphors,
- demonstrate the basics of a visual construct,
- design a visual entity with conceptual content.

Course Content

The course content involves analyzing the theoretical foundations of the design process at both conscious and subconscious levels, understanding how the designer establishes themselves as an active agent in practice, and evaluating and applying this process through theoretical discussions and workshop activities. It includes a learning process where the communication principles of all representational languages and conceptual structuring are explained, knowledge is applied through design samples, and students synthesize what they have learned to develop new solutions.
The teaching methods of the course are as follows: lecture, reading, discussion and individual study

The content of the course consists of theoretical part and workshop studies that will make it possible to examine the theoretical infrastructure of the design activity at the conscious and unconscious level, and to understand how the designer actually establishes himself as an agent. It consists of verbal information in which the communication principles of all representation languages and conceptual construction are conveyed, and design examples in which the knowledge is applied.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to the course.
1)
1)
2)	examination of philosophical base of designing
3)	Elements of graphic language.
4)	Elements of graphic language.
5)	Abstraction and conceptualization.
6)	Abstraction and conceptualization.
7)	Iconography in classical arts.
8)	Using metaphors in visual communication.
9)	Exercise 1: Brief, start.
10)	Exercise 2: Development.
11)	Exercise 1: Final and dicussion.
12)	Exercise 2: Brief, start.
13)	Exercise 2: Development.
14)	Exercise 2: Final and discussion.

Sources

Course Notes / Textbooks:
References:

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Attendance	12	% 5
Homework Assignments	2	% 35
Preliminary Jury	1	% 20
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Workload
Course Hours	14	28
Study Hours Out of Class	14	42
Homework Assignments	2	6
Midterms	1	1
Final	1	2
Total Workload		79

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Build up a body of knowledge in mathematics, science and Artificial Intelligence Engineering subjects; use theoretical and applied information in these areas to model and solve complex engineering problems.
2)	Design complex Artificial Intelligence systems, platforms, processes, devices or products under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose.
3)	Identify, formulate, and solve complex Artificial Intelligence Engineering problems; select and apply proper modeling and analysis methods for this purpose.
4)	Devise, select, and use modern techniques and tools needed for solving complex problems in Artificial Intelligence Engineering practice; employ information technologies effectively.
5)	Design and conduct numerical or physical experiments, collect data, analyze and interpret results for investigating the complex problems specific to Artificial Intelligence Engineering.
6)	Ability to communicate effectively in English and Turkish (if he/she is a Turkish citizen), both orally and in writing. Write and understand reports, prepare design and production reports, deliver effective presentations, give and receive clear and understandable instructions.
7)	Recognize the need for life-long learning; show ability to access information, to follow developments in science and technology, and to continuously educate oneself.
8)	Develop an awareness of professional and ethical responsibility, and behave accordingly. Be informed about the standards used in Artificial Intelligence Engineering applications.	3
9)	Learn about business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development.
10)	Acquire knowledge about the effects of practices of Artificial Intelligence Engineering on health, environment, security in universal and social scope, and the contemporary problems of Artificial Intelligence Engineering; is aware of the legal consequences of Mechatronics engineering solutions.	4
11)	Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working on Artificial Intelligence-related problems.