Information Package / Course Catalogue
Digitalization and Artificial Intelligence
Course Code: İKT418
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 5
Objectives of the Course

The aim of this course is to enable students to understand the economic, social, and institutional impacts of digital transformation and artificial intelligence technologies, and to develop the ability to effectively use artificial intelligence applications in economic analysis, data evaluation, decision making, and policy development processes.

Course Content

Concepts of digital transformation and their economic impacts; digital economy and platform economies; Industry 4.0 and smart production systems; big data and data economy; fundamental concepts of artificial intelligence and machine learning; generative artificial intelligence applications; effective prompt engineering techniques; the use of artificial intelligence in academic research; AI assisted data analysis and reporting tools; the effects of artificial intelligence on labor markets, financial markets, and public policies; data security, ethics, and regulatory frameworks; artificial intelligence applications in economic analysis and contemporary case studies.

Name of Lecturer(s)
Learning Outcomes
1.Explain the fundamental concepts of digitalization, digital transformation, and artificial intelligence technologies and their effects on economic systems.
2.Evaluate and interpret economic problems using artificial intelligence and data driven analytical tools.
3.Analyze current developments in the digital economy, platform economies, and financial technologies.
4.Use generative artificial intelligence tools and effective prompt engineering techniques for academic and professional purposes.
5.Develop solution proposals by evaluating the ethical, legal, and social dimensions of artificial intelligence applications.
Recommended or Required Reading
1.Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th Edition).
2.Current academic articles, case studies, and artificial intelligence applications provided by the lecturer.
Weekly Detailed Course Contents
Week 1 - Theoretical
Concepts of digitalization and digital transformation, historical development, and economic impacts.
Week 2 - Theoretical
Fundamental characteristics of the digital economy, platform economies, and the data economy.
Week 3 - Theoretical
Industry 4.0, smart production systems, and technological transformation processes.
Week 4 - Theoretical
Big data, data sources, and data driven decision making processes.
Week 5 - Theoretical
Fundamental concepts, historical development, and operating principles of artificial intelligence.
Week 6 - Theoretical
Machine learning, deep learning, and contemporary artificial intelligence technologies.
Week 7 - Theoretical
Generative AI tools: ChatGPT, Claude, Gemini, and similar systems.
Week 8 - Theoretical
Prompt engineering techniques, effective interaction with AI, and practical applications.
Week 9 - Theoretical
Prompt engineering techniques, effective interaction with AI, and practical applications.
Week 10 - Theoretical
Prompt engineering techniques, effective interaction with AI, and practical applications.
Week 11 - Theoretical
The effects of artificial intelligence on labor markets, employment, and occupations.
Week 12 - Theoretical
AI assisted data analysis, reporting, and decision support systems.
Week 13 - Theoretical
Use of artificial intelligence in academic research, literature review, and scientific writing practices.
Week 14 - Theoretical
Artificial intelligence applications in public policy and contemporary case studies.
Assessment Methods and Criteria
Type of AssessmentCountPercent
Project1%10
Quiz1%10
Midterm Examination1%20
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142370
Lecture - Practice42320
Quiz28118
Midterm Examination1718
Final Examination1819
TOTAL WORKLOAD (hours)125
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
OÇ-1
4
4
4
4
5
4
4
OÇ-2
3
5
3
3
3
5
5
OÇ-3
4
3
3
4
4
5
5
OÇ-4
5
5
5
4
4
4
4
OÇ-5
3
3
3
3
3
4
3
Adnan Menderes University - Information Package / Course Catalogue
2026