SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code	Course Name	Year	Semester	Theoretical	Practical	Credit	ECTS
YTN6012	Yapay Zeka Okuryazarlığı	0	Spring	3	0	3	6

Course Type :	University Elective
Cycle:	Bachelor TQF-HE:6. Master`s Degree QF-EHEA:First Cycle EQF-LLL:6. Master`s Degree
Language of Instruction:	Turkish
Prerequisities and Co-requisities:	N/A
Mode of Delivery:	Face to face
Name of Coordinator:	Instructor FATMA NUR BUDAK
Dersin Öğretim Eleman(lar)ı:	Instructor FATMA NUR BUDAK
Dersin Kategorisi:

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives:

This course aims to provide students with artificial intelligence literacy. Within the scope of the course, the fundamental concepts of AI, its historical development, problem-solving approaches, and the basics of data and machine learning are covered. A holistic framework is presented through current AI models, ethical principles, social impacts, and critical thinking dimensions. It is intended for students to evaluate AI systems not only from a technical standpoint but also through the lens of ethics, society, and conscious usage. The course aims to equip students from various disciplines with the competence to understand, interpret, and responsibly use artificial intelligence.

Course Content:

The course covers an introduction to AI literacy, the historical development of artificial intelligence, and its fundamental approaches. It examines the concepts of problem definition, state representation, goal setting, and basic planning models, while providing an overview of data, machine learning, and deep learning. Furthermore, the limitations of AI systems, issues of error and bias, ethical principles, and the societal impacts of AI are discussed. Emphasis is placed on the conscious use of AI tools and the development of critical thinking skills.

Course Specific Rules

Regular attendance is mandatory for this course. A minimum attendance rate of 70% is required; students who fail to meet this requirement will not be eligible to take the final examination. Students are expected to participate actively, contribute to in-class discussions, and submit assignments on time. The use of AI tools within the scope of the course will be evaluated within the framework of ethical and responsible use, and any usage contrary to academic integrity will be considered invalid. In cases of cheating, plagiarism, or similar academic violations, the relevant university regulations will be applied. Unauthorized sharing of course materials is prohibited. Students are expected to follow course announcements regularly and comply with all procedural rules.

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:



Knowledge *(Described as Theoritical and/or Factual Knowledge.)*
1) Defines the basic concepts of artificial intelligence, its historical development, and the evolution of its approaches, and explains them with examples from daily life.
2) Conceptually models the problem-solving process in artificial intelligence and adapts simple planning approaches to everyday scenarios.
3) Evaluates, at a conceptual level, the role of data in artificial intelligence systems and the fundamental principles of machine learning and deep learning approaches.
4) Analyzes the limitations of AI systems, the sources of errors, and different types of bias.
5) Applies ethical principles in the use of artificial intelligence (fairness, transparency, accountability) and critically examines its societal impacts.
6) Engages with AI tools in an informed manner, develops critical thinking skills, and makes a holistic reflection on the future.
Skills *(Describe as Cognitive and/or Practical Skills.)*
1) Distinguishes between correlation and causation, and evaluates the impact of this difference on artificial intelligence decision-making processes.
Competences *(Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)*

Weekly Course Schedule

Week	Subject	Materials Sharing *
		Related Preparation	Further Study
1)	Introduction to artificial intelligence literacy and fundamental concepts of AI.	Gökçearslan, Ş., & Yıldız Durak, H. (Eds.). (2024). Artificial intelligence literacy. Nobel Academic Publishing.
2)	The historical development of artificial intelligence and the evolution of approaches.	Gökçearslan, Ş., & Yıldız Durak, H. (Eds.). (2024). Artificial intelligence literacy. Nobel Academic Publishing.
3)	The concept of a problem and the problem-solving approach in artificial intelligence.	Gökçearslan, Ş., & Yıldız Durak, H. (Eds.). (2024). Artificial intelligence literacy. Nobel Academic Publishing.
4)	State representation, goal concepts, and basic planning models.	Gökçearslan, Ş., & Yıldız Durak, H. (Eds.). (2024). Artificial intelligence literacy. Nobel Academic Publishing.
5)	The concept of data and the role of data in artificial intelligence systems.	Gökçearslan, Ş., & Yıldız Durak, H. (Eds.). (2024). Artificial intelligence literacy. Nobel Academic Publishing.
6)	A conceptual perspective on machine learning and its types of learning.	Gökçearslan, Ş., & Yıldız Durak, H. (Eds.). (2024). Artificial intelligence literacy. Nobel Academic Publishing.
7)	Deep learning and a general overview of contemporary AI models.	Gökçearslan, Ş., & Yıldız Durak, H. (Eds.). (2024). Artificial intelligence literacy. Nobel Academic Publishing.
8)	Midterm
9)	The limitations of artificial intelligence systems, and the concepts of errors and bias.	Gökçearslan, Ş., & Yıldız Durak, H. (Eds.). (2024). Artificial intelligence literacy. Nobel Academic Publishing.
10)	Artificial intelligence and ethical principles: justice, transparency, and responsibility.	Gökçearslan, Ş., & Yıldız Durak, H. (Eds.). (2024). Artificial intelligence literacy. Nobel Academic Publishing.
11)	The societal impact of artificial intelligence and its transformative effects on occupations.	Gökçearslan, Ş., & Yıldız Durak, H. (Eds.). (2024). Artificial intelligence literacy. Nobel Academic Publishing.
12)	Interacting with AI tools and their informed use.	Gökçearslan, Ş., & Yıldız Durak, H. (Eds.). (2024). Artificial intelligence literacy. Nobel Academic Publishing.
13)	AI literacy and critical thinking.	Gökçearslan, Ş., & Yıldız Durak, H. (Eds.). (2024). Artificial intelligence literacy. Nobel Academic Publishing.
14)	A holistic perspective on AI literacy and future outlook.	Gökçearslan, Ş., & Yıldız Durak, H. (Eds.). (2024). Artificial intelligence literacy. Nobel Academic Publishing.

*These fields provides students with course materials for their pre- and further study before and after the course delivered.

DERS ÖĞRENME ÇIKTILARI - PROGRAM ÖĞRENME ÇIKTILARI İLİŞKİSİ

Contribution of The Course Unit To The Programme Learning Outcomes

Ders Öğrenme Çıktıları (DÖÇ)	1	2	3	4	5	6	7
Program Öğrenme Çıktıları (PÖÇ)
1) Explain the fundamental concepts, historical development, and theoretical framework of graphic design.
2) Define typography, color theory, and composition principles in visual communication design.
3) Evaluate the social, cultural, and ethical aspects of graphic design to develop an interdisciplinary perspective.
4) Develop original and innovative design solutions using creative problem-solving methods.
5) Apply visual hierarchy, perception psychology, and user experience (UX) principles to design for international markets.
6) Effectively use digital tools and design software to produce professional graphic design work.
7) Take responsibility in international graphic design projects individually or within a team to develop creative solutions.
8) Manage graphic design projects and plan processes while applying a professional work discipline.
9) Continuously improve by following global innovations, technologies, and methodologies in graphic design.
10) Adopt intercultural design principles to create visual solutions for global audiences.
11) Develop design solutions that are culturally sensitive, ethically appropriate, and sustainable.
12) Work independently or participate in teamwork within graphic design processes.

SECTION III: RELATIONSHIP BETWEEN COURSE UNIT AND COURSE LEARNING OUTCOMES (CLOs)

Level of Contribution of the Course to PLOs

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

	Programme Learning Outcomes	Contribution Level (from 1 to 5)
1)	Explain the fundamental concepts, historical development, and theoretical framework of graphic design.
2)	Define typography, color theory, and composition principles in visual communication design.
3)	Evaluate the social, cultural, and ethical aspects of graphic design to develop an interdisciplinary perspective.
4)	Develop original and innovative design solutions using creative problem-solving methods.	1
5)	Apply visual hierarchy, perception psychology, and user experience (UX) principles to design for international markets.
6)	Effectively use digital tools and design software to produce professional graphic design work.	1
7)	Take responsibility in international graphic design projects individually or within a team to develop creative solutions.	1
8)	Manage graphic design projects and plan processes while applying a professional work discipline.
9)	Continuously improve by following global innovations, technologies, and methodologies in graphic design.	2
10)	Adopt intercultural design principles to create visual solutions for global audiences.	1
11)	Develop design solutions that are culturally sensitive, ethically appropriate, and sustainable.
12)	Work independently or participate in teamwork within graphic design processes.	1

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Teaching and Learning Methods defined at the Programme Level	Teaching and Learning Methods Defined for the Course
Lectures
Discussion
Views
Homework
Project Preparation
Brain Storming
Questions Answers
Individual and Group Work
Active Participation in Class

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Aassessment and evaluation Methods defined at the Programme Level	Assessment and Evaluation Methods defined for the Course
Midterm
Final Exam
Homework Evaluation

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods	# of practice per semester	Level of Contribution
Homework Assignments	1	% 15.00
Midterms	1	% 35.00
Semester Final Exam	1	% 50.00
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE

WORKLOAD OF TEACHING & LEARNING ACTIVITIES
Teaching & Learning Activities	# of Activities per semester	Duration (hour)	Total Workload
Course	14	3	42
Laboratory	0	0	0
Application	0	0	0
Special Course Internship (Work Placement)	0	0	0
Field Work	0	0	0
Study Hours Out of Class	14	1.5	21
Presentations / Seminar	0	0	0
Project	0	0	0
Homework Assignments	1	20	20
Total Workload of Teaching & Learning Activities	-	-	83
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES
Assesment & Evaluation Activities	# of Activities per semester	Duration (hour)	Total Workload
Quizzes	0	0	0
Midterms	1	30	30
Semester Final Exam	1	40	40
Total Workload of Assesment & Evaluation Activities	-	-	70
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities)			153
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h)			6