| 1 | Generative AI Architecture: What is Generative AI? How do Large Language Models (LLMs) work? Probabilistic thinking structure. |
| 2 | Limitations and Verification: Hallucination problem, accuracy control (Fact-Checking), source reliability, and critical thinking. |
| 3 | Advanced Research Methods: Finding academic articles, literature review, summarizing, and reference management using AI. |
| 4 | Data Analysis and Visualization: Analysing numerical or textual data with AI, extracting insights, plotting charts, and reporting. |
| 5 | Customized Assistant (Custom GPT) Concept: Moving beyond standard models, the concept of needs-based assistants, and usage areas. |
| 6 | Application Workshop I (Assistant Design): Steps to configure a bot specific to one's professional field (Education, Law, Health, etc.) without coding. |
| 7 | Application Workshop II (Test and Optimization): Testing the designed assistant, managing the knowledge base, and making fine-tuning adjustments. |
| 8 | Case Study I (Complex Problem Solving): Methodology of defining professional problems and converting a real-life issue into an AI prompt. |
| 9 | Midterm Exam |
| 10 | Case Study II (Decision Support Systems): Evaluating options, performing risk analysis, and generating scenarios in strategic decision-making processes. |
| 11 | Case Study III (Innovation and Ideation): Using AI in developing new project, product, or service ideas in the professional field. |
| 12 | Professional Project Management: AI tools for creating workflows, task distribution, time planning, and project tracking. |
| 13 | Career Building and Portfolio: Professional CV preparation, interview simulations, LinkedIn optimization, and digital identity management. |
| 14 | General Evaluation: Future projection, sustainability of AI literacy, and general review. |
