Electrical and Electronic Engineering (English) Preview
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code:

UNI438

Course Name:

AI and Transformation Management

Semester:

Spring
Fall

Course Credits:

ECTS

Language of instruction:

English

Course Condition:

Does the Course Require Work Experience?:

Type of course:

University Elective

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

E-Learning

Course Coordinator:

Öğr. Gör. SERTAÇ YERLİKAYA

Course Lecturer(s):

Dr.Sertaç Yerlikaya

Course Assistants:

Course Objective and Content

Course Objectives:

The course aims to equip participants with the knowledge and tools to
understand how technology has transformed industries, societies, and
individuals, while also identifying opportunities and addressing
challenges in technology-driven change. The course seeks to develop
awareness of AI’s interdisciplinary impact and encourage critical
evaluation of its ethical, social, and political consequences. Through
expert insights, students will gain a deeper understanding of AI’s
real-world applications and implications. This course relies less on
theory and more on practical applications in the industry.

Course Content:

This course explores technological transformation from the Industrial
Revolutions to Artificial Intelligence (AI), examining key concepts in
transformation management. Students will analyze AI’s role across
various disciplines and discuss its social, political, and ethical
implications. Additionally, guest speakers from different industries
will provide real-world insights into AI’s impact, offering practical
perspectives on technology-driven change.

Learning Outcomes

The students who have succeeded in this course;
1) Understand historical and contemporary technological transformations:
2) Identify key principles of transformation management in tech-driven change:
3) Assess the opportunities and challenges presented by AI across industries:
4) Critically analyze AI’s business, social, ethical, and political implications:
5) Apply transformation management strategies to real-world scenarios:

Course Flow Plan

Week	Subject	Related Preparation
1)	Introduction to AI and Transformation Management Course	Students are expected to thoroughly review the course syllabus to understand the overall structure and objectives of the class.
2)	Industry 4.0/5.0	This week serves as an introduction to understanding the concept of transformation and learning the foundational elements of Industry 4.0 and 5.0. Before coming to class, explore studies that show how transformation affects not only technology but also society and individuals. Explore how technological transformation is unfolding in your own field (e.g., business, healthcare, engineering, arts) and follow current debates on how these changes are being discussed.
3)	Drivers and Challenges of Technology Transformation	Review your notes from Week 2. Reflect on the factors that drive transformation and common challenges faced by organizations. Read Andrew Ng’s How to Choose Your First AI Project and the introduction of Leading Digital.
4)	Transformation Management – Alignment with Strategy	Revise notes from Week 3 on innovation. Learn how digital transformation aligns with organizational strategy. Read “Digital Transformation Is Not About Technology” and “Digital Transformation Is About Talent, Not Technology.”
5)	Innovation Management – Problem-Solving in Transformation	Review the concepts of Week 4. Research innovation types and organizational problem-solving strategies. Explore case studies showing innovation under transformation pressures. Prepare your questions about Innovation Management for guest speaker.
6)	Introduction to AI – Concepts, History, and Trends	Review the strategy and transformation alignment topics from Week 5. Study the history and current uses of artificial intelligence . Preapre your questions for guest speaker.
7)	Fundamentals of AI and Machine Learning	Revisit AI concepts from Week 6, and read documents shared. Explore basic ML concepts: supervised/unsupervised learning, key algorithms, and model training. Prepare your questions for guest speaker.
8)	Midterm - proje	Begin by reviewing all course materials covered up to this point to ensure a solid understanding of the key concepts. Form your group, ideally with a minimum of 8 students from diverse faculties to encourage interdisciplinary collaboration. Each group will present an initial draft of their final paper to receive feedback. Prepare a clear and engaging presentation that addresses a transformation challenge within your field of study and proposes a practical, AI-driven solution. Focus on clearly defining the problem, demonstrating the relevance and feasibility of the AI application, and highlighting the interdisciplinary impact of your approach.
9)	Challenges in Scaling AI in Organizations (Panel Discussions)	This week features panel discussions with industry leaders on the real-world challenges of scaling AI within organizations. To prepare, review what you've learned about AI implementation barriers such as data readiness, organizational culture, and talent gaps. Prepare questions that connect these topics with your own field of study. During the session, take notes and reflect on how these challenges apply to both large and small organizations.
10)	Group Project Presentations	Prepare your projects as defined. Organize your team according to a 5-minute presentation and 2-minute Q&A format. Make sure to rehearse in advance.
11)	Con't- Group Project Presentations	Ref :Week 10
12)	Responsible AI	This week focuses on the ethical, transparent, fair, and accountable development and use of artificial intelligence systems. Review the concepts discussed in class and explore current articles on issues such as algorithmic bias, privacy, and decision-making accountability. Reflect on how AI applications in your own field may create responsibilities or ethical concerns. Be ready to discuss how human rights, data security, and social equity should be considered in the design and deployment of AI technologies. Try to approach the concept of "responsible AI" not only from a technical standpoint but also from social and institutional perspectives.
13)	The Role of Humans in a Trasnformed World	For this week, reflect on how the rapid advancement of AI is reshaping the role of humans in the workforce and society. Review class discussions on human-AI collaboration, skill transformation, and the future of work. Read the materials provided and consider how your field is adapting to human-machine interaction. Be prepared to discuss how human creativity, ethics, and emotional intelligence continue to provide value in a technology-driven world.
14)	AI and Its Evolving Landscape	This week focuses on understanding the constantly changing AI landscape. Explore recent trends such as generative AI, AI agents, and advances in large language models. Review the course materials and connect them with the latest news or breakthroughs you find through reputable sources (e.g., MIT Tech Review, Stanford AI Index). Reflect on how this evolution impacts your own field and be ready to share relevant examples.
15)	Revision
16)	final week

Sources

Course Notes / Textbooks:	Lecturer's Notes, Ders Notları
References:	Ders süresince sağlanacak seçilmiş makaleler ve vaka çalışmaları Selected articles and case studies (provided throughout the course)

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3	4	5
Program Outcomes
1) Adequate knowledge in mathematics, science and Electrical and Electronics engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems.
2) Ability to identify, formulate, and solve complex electrical and electronics engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.
3) Ability to design a complex circuit, device or system to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose.
4) Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in electrical and electronics engineering applications; ability to use information technologies effectively.
5) Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or electrical and electronics engineering research topics.
6) Ability to work effectively within and multidisciplinary teams; individual study skills.
7) Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; ability to write effectice reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8) Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously.
9) To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in electrical and electronics engineering applications.
10) Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development.
11) Knowledge of the effects of electrical and electronics engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in electrical and electronics engineering; awareness of the legal consequences of electrical and electronics engineering solutions.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Adequate knowledge in mathematics, science and Electrical and Electronics engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems.
2)	Ability to identify, formulate, and solve complex electrical and electronics engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.
3)	Ability to design a complex circuit, device or system to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose.
4)	Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in electrical and electronics engineering applications; ability to use information technologies effectively.
5)	Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or electrical and electronics engineering research topics.
6)	Ability to work effectively within and multidisciplinary teams; individual study skills.
7)	Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; ability to write effectice reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8)	Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously.
9)	To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in electrical and electronics engineering applications.
10)	Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development.
11)	Knowledge of the effects of electrical and electronics engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in electrical and electronics engineering; awareness of the legal consequences of electrical and electronics engineering solutions.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Attendance	14	% 15
Quizzes	5	% 10
Project	15	% 30
Seminar	6	% 5
Final	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Workload
Course Hours	14	56
Homework Assignments	2	10
Quizzes	8	32
Final	4	17
Total Workload		115