| 1 | Definitions of human intelligence and artificial intelligence, historical development, the Turing test, and basic concepts. |
| 2 | What is machine learning? How does it learn? Algorithmic thinking structure and types of learning. |
| 3 | The role of humans, principles of interaction with technology, user experience, and interface psychology. |
| 4 | Techniques for establishing correct communication with AI, creating context, and assigning roles. |
| 5 | Summarizing course notes, simplifying complex texts, and creating concept maps. |
| 6 | Time management, preparing weekly course schedules, and creating study calendars. |
| 7 | Practice in foreign language learning, vocabulary acquisition techniques, and error analysis. |
| 8 | Midterm Exam |
| 9 | Academic translation support, text editing (proofreading), spelling check, and tone management. |
| 10 | Creating visuals for presentations, media analysis, detection of disinformation, and deepfakes. |
| 11 | Algorithmic bias, discrimination, transparency, and responsibility in AI decisions. |
| 12 | Protection of personal data (GDPR/KVKK), digital footprint management, and cybersecurity awareness. |
| 13 | The future of the workforce, the concept of digital citizenship, and sustainable technology use. |
| 14 | Semester summary and student feedback. |
