| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 1 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- They will be able to understand the basic concepts and subfields of artificial intelligence.
- They will be able to identify real-world applications of artificial intelligence in industries.
- They will be able to recognize the opportunities, limitations, and ethical implications of AI.
- They will be able to assess which AI tools are suitable for specific tasks.
- They will be able to interact with AI technologies critically and responsibly.
|
| Mode of Delivery | E-Learning |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | |
|---|
| Course Description | Artificial Intelligence Applications in Social Sciences course is an introductory level course that aims to introduce social sciences students to the basic concepts, methods and applications of artificial intelligence technologies. Within the scope of the course, students recognize artificial intelligence approaches such as machine learning, deep learning, large language models They will learn the place of these technologies in data analysis, content production, project management and business applications in social sciences. In addition, by emphasizing the ethical dimensions of artificial intelligence (prejudice, justice, privacy), social effects and projections for the future, it is aimed that students understand technology not only as a tool, but also as a critical component of social transformations. Lesson Short video lessons, AI-powered reports, quizzes, and assessment activities support students to interact with AI critically, responsibly, and creatively |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
What is Artificial Intelligence? Definitions, History and Current Uses |
|
2nd Week |
Machine Learning Fundamentals |
|
3rd Week |
Artificial Intelligence Tools |
|
4th Week |
AI Ethics |
|
5th Week |
The Future of AI |
|
6th Week |
Command Engineering |
|
7th Week |
Midterm Exam |
|
8th Week |
AI and Creativity |
|
9th Week |
AI in Scientific Production |
|
10th Week |
AI in Business |
|
11th Week |
AI in Project Management |
|
12th Week |
AI Through the Eyes of Students |
|
13th Week |
AI-Assisted Analysis |
|
14th Week |
General evaluation |
|
15th Week |
|
|
| Recommended or Required Reading | Nidhya, R., Pavithra, D., Kumar, M., Kumar, A. D., & Balamurugan, S. (2025). Generative Artificial Intelligence: Concepts and Applications. John Wiley & Sons |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Questions/Answers
- Practice
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Quiz |
1 |
20 |
| Attendance |
1 |
10 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
|
|
|
| Preparation for Final exam |
1 |
10 |
10 |
| Course hours |
14 |
1 |
14 |
| Preparation for Midterm exam |
1 |
10 |
10 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
|
|
|
| Total Work Load | 38 |
| Total Work Load / 30 (h) | 1.26 |
| ECTS Credit of the Course | 1 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
