BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| ECONOMICS | |||||
| 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 |
| ECO4091 | Generative Artificial Intelligence in Analysis and Research | Fall | 3 | 0 | 3 | 6 |
| 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: | Departmental Elective |
| Course Level: | Bachelor’s Degree (First Cycle) |
| Mode of Delivery: | E-Learning |
| Course Coordinator : | Prof. Dr. ÇAĞLAR YURTSEVEN |
| Course Objectives: | This project-based learning course equips students with practical skills in applying Generative Artificial Intelligence (AI) to economic analysis and research. The course emphasizes hands-on experience, with students actively engaging in projects that mirror real-world applications of Generative AI in economics. The course uniquely features the use of Generative AI to assist students in learning and implementing Python API applications, providing a meta-learning experience that reinforces both Generative AI utilization and coding skills. |
Learning Outcomes
|
The students who have succeeded in this course; On successful completion of this course, students will be able to: - Leverage web-based Generative AI interfaces for economic analysis and research tasks. - Develop Python scripts to interact with Generative AI APIs, with the unique approach of using Generative AI themselves to assist in the coding process. - Apply Generative AI to both microeconomic and macroeconomic research questions through guided projects. - Integrate Generative AI outputs with traditional economic models and methodologies. - Critically evaluate the strengths and limitations of Generative AI in economic contexts. - Conduct independent economic research projects utilizing Generative AI as a key analytical tool. |
Course Content
| The teaching methods of the course are Lecture, Reading, Technology-Enhanced Learning and Project. This course begins with an introduction to Generative AI and its economic relevance, followed by foundational training in both web-based Generative AI interfaces and Python basics for API integration. Students then apply these skills to microeconomic analysis, covering consumer behavior, market research, and labor market analysis. The focus then shifts to macroeconomic applications, including economic forecasting, policy analysis, and global economic trend assessment. Advanced Generative AI techniques are explored, such as fine-tuning models for economic tasks and combining Generative AI with traditional economic models. The course concludes with a section on using Generative AI in economic research and academic writing, followed by final project presentations where students demonstrate their ability to apply Generative AI to substantial economic research questions. Throughout the course, weekly hands-on projects provide practical experience, culminating in a comprehensive final project that synthesizes the skills and knowledge acquired. |
Weekly Detailed Course Contents
| Week | Subject | Related Preparation |
| 1) | Introduction to Generative AI and Its Economic Relevance | |
| 2) | Foundations of Generative AI Usage - Web-based Generative AI Interfaces | |
| 3) | Foundations of Generative AI Usage - Python Basics for Generative AI Integration | |
| 4) | Generative AI in Microeconomic Analysis - Consumer Behavior Analysis | |
| 5) | Generative AI in Microeconomic Analysis - Market Research and Competitive Analysis | |
| 6) | Generative AI in Microeconomic Analysis - Labor Market Analysis | |
| 7) | Generative AI in Macroeconomic Analysis - Economic Forecasting | |
| 8) | Generative AI in Macroeconomic Analysis - Policy Analysis | |
| 9) | Generative AI in Macroeconomic Analysis - Global Economic Trends | |
| 10) | Advanced Generative AI Techniques in Economics - Fine-tuning Generative AI for Economic Tasks | |
| 11) | Advanced Generative AI Techniques in Economics - Combining Generative AI with Traditional Economic Models | |
| 12) | Generative AI in Economic Research and Publication - Writing and Editing Support | |
| 13) | Generative AI in Economic Research and Publication - Literature Review and Research Assistance | |
| 14) | Presentation of Final Project |
Sources
| Course Notes / Textbooks: | Phoenix, J., & Taylor, M. (2024). Prompt Engineering for Generative AI. O'Reilly Media. DeepLearning AI Short Courses - AI Python for Beginners: Basics of AI Python Coding" |
| References: | Suleyman, M., & Bhaskar, M. (2023). The Coming Wave: Technology, Power, and the Twenty-first Century's Greatest Dilemma. Penguin Random House. Khan, S. (2023). Brave New Words: How AI Will Revolutionize Education (and Why That's a Good Thing). Twelve. Christian, B. (2020). The Alignment Problem: Machine Learning and Human Values. W. W. Norton & Company. Agrawal, A., Gans, J., & Goldfarb, A. (2022). Power and Prediction: The Disruptive Economics of Artificial Intelligence. Harvard Business Review Press." |
Evaluation System
| Semester Requirements | Number of Activities | Level of Contribution |
| Homework Assignments | 12 | % 60 |
| Final | 1 | % 40 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 60 | |
| PERCENTAGE OF FINAL WORK | % 40 | |
| Total | % 100 | |
ECTS / Workload Table
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Study Hours Out of Class | 14 | 4 | 56 |
| Project | 1 | 30 | 30 |
| Homework Assignments | 12 | 2 | 24 |
| Total Workload | 152 | ||
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) | As a world citizen, she is aware of global economic, political, social and ecological developments and trends. | 5 |
| 2) | He/she is equipped to closely follow the technological progress required by global and local dynamics and to continue learning. | 3 |
| 3) | Absorbs basic economic principles and analysis methods and uses them to evaluate daily events. | 1 |
| 4) | Uses quantitative and statistical tools to identify economic problems, analyze them, and share their findings with relevant stakeholders. | 2 |
| 5) | Understands the decision-making stages of economic units under existing constraints and incentives, examines the interactions and possible future effects of these decisions. | 3 |
| 6) | Comprehends new ways of doing business using digital technologies. and new market structures. | 4 |
| 7) | Takes critical approach to economic and social problems and develops analytical solutions. | 4 |
| 8) | Has the necessary mathematical equipment to produce analytical solutions and use quantitative research methods. | 2 |
| 9) | In the works he/she contributes, observes individual and social welfare together and with an ethical perspective. | 2 |
| 10) | Deals with economic problems with an interdisciplinary approach and seeks solutions by making use of different disciplines. | 3 |
| 11) | Generates original and innovative ideas in the works she/he contributes as part of a team. | 5 |