School of Engineering \ Computer Engineering
Course Credit
ECTS Credit
Course Type
Instructional Language
Programs that can take the course
This course aims to develop students’ digital literacy and knowledge-production skills using generative AI tools (e.g. ChatGPT, Claude, Copilot). It covers not only basic office software competencies, but also text generation, summarization, and the creation of discipline-specific digital content.
Textbook and / or References
1. TÜBİTAK BİLGEM Yayını: Büyük Dil Modelleri ve İstem Mühendisliği
2. Google Whitepaper: Prompt Engineering (Eylül 2024)
3. YAPAY ZEKÂ OKURYAZARLIĞI, Kolektif, Nobel Akademik Yayıncılık, 2024
This course aims to enable students to learn the fundamental functions of large language model–based AI tools (such as ChatGPT, Gemini, Claude, Copilot) and apply them appropriately in diverse contexts; to teach effective prompt engineering techniques for crafting accurate prompts and obtaining desired outputs from these platforms; to foster critical evaluation of AI-generated content by identifying biases and errors and conducting reliability analysis; and to develop students’ ability to prepare documents, data, and presentations professionally using core office software (e.g., Microsoft Office)
1. Use basic office software (e.g., Microsoft Office) professionally to prepare documents, data, and presentations.
2. Identify and articulate the core functions of large language models (ChatGPT, Gemini, Claude, Copilot, etc.) and their appropriate applications.
3. Design effective prompts, generate applicable outputs across different AI tools, and apply prompt-engineering techniques.
4. Critically evaluate AI-generated content by analyzing bias, errors, and overall reliability.
5. Creatively and ethically integrate AI tools into discipline-specific digital content production.
Week 1: Introduction to Digital Literacy
Week 2: Academic Content and Document Creation with Word & PowerPoint
Week 3: Excel – Timelines & Data Visualization
Week 4: Excel – Basic Functions, Pivot Tables & Conditional Formatting
Week 5: Excel – Advanced Functions, Pivot Tables & Chart Analysis
Week 6: Introduction to AI & Fundamentals of Large Language Models
Week 7: Prompt Engineering
Week 8: Advanced Prompt Techniques (role assignment, file reading & processing) & Discipline‑Specific Applications (media planning, flowcharting, Python‑based visuals, etc.)
Week 9: AI-Driven Digital Content Creation – Case Studies (e.g., writing LinkedIn job ads, logo design, image editing/enhancement, intro to visual creators like Midjourney, DALL‑E, Microsoft Designer)
Week 10: AI-Supported Project Planning
Week 11: Project Presentation Preparation
Week 12: Project Presentations
Tentative Assesment Methods
• Midterm Exam %30
• Final Exam 40%
• Attendance 10%
• Laboratory (Project) 20%
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