Information Package
baskent.edu.tr

Program of Technology and Knowledge Management

TKM204

ARTIFICIAL INTELLIGENCE USAGE AND MANAGEMENT

Course Code
TKM204
Semester
Fourth Term (Spring)
Theory+Practice
3 + 0
ECTS
6
Course TypeCompulsory Course
Course LevelBachelor's Degree
Year of Study2
SemesterFourth Term (Spring)
ECTS6
Name of Lecturer(s) -
Learning Outcomes

At the end of this course, the students;

  1. Explain the fundamental principles of prompt engineering and apply different prompt techniques
  2. Use generative AI tools (ChatGPT, Gemini, Claude AI, etc.) in business contexts for business correspondence, report summaries, and content production.
  3. Discuss ethical, privacy, and intellectual property issues in the use of artificial intelligence.
  4. Develop generative AI-based business ideas, present prototypes, and produce solution proposals for business problems.
Mode of DeliveryFace to face
PrerequisitesNo
Recommended Optional Programme Components
Course DescriptionThe aim of this course is to enable business students to understand generative artificial intelligence technologies and apply them in real-world business settings. Students learn the operating principles of large language models (LLMs), acquire prompt engineering skills, and experience how generative AI can be leveraged in areas such as content production, data analysis, and automation within business environments.
Course Contents
WEEK TOPICS
1st Week Generative AI and Its Place in the Business World
2nd Week Large Language Models and Their Core Principles
3rd Week Introduction to Prompt Engineering
4th Week Prompt Optimization and Techniques
5th Week Prompt Applications
6th Week Text Generation and Summarization: Business Correspondence and Reports
7th Week Visual Content Generation
8th Week No-Code Applications and Dashboard Development
9th Week AI Tools Applications
10th Week Chatbot and AI Assistant Development
11th Week Generative AI Applications: Cost, Resilience, and Safe Automation
12th Week Ethics, Copyright, and Artificial Intelligence
13th Week Project Presentations
14th Week General Evaluation
Recommended or Required ReadingPhoenix, James and Taylor, Mike (2024). Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs at Scale. O'Reilly Media, Inc
Hunter, Nathan (2023). The Art of Prompt Engineering with chatGPT: A Hands-On Guide. England.
Boonstra, Lee (2025). Prompt Engineering. Google.
Berryman, John, and Albert Ziegler (2024). Prompt Engineering for LLMs: The Art and Science of Building Large Language Model?Based Applications. O'Reilly Media, Inc.
GitHub (2025). Prompt Engineering Guide
OpenAI (2025). OpenAI Docs & Cookbook
Ders Notları
Planned Learning Activities and Teaching Methods
  • Lecture
  • Practice
  • Presentation
  • Project
  • Case Study
Assessment Methods and Criteria
Quantity Percentage (%)
Mid-term 1 30
Project 1 30
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 14 3 42
Preparation for Final exam 1 30 30
Course hours 14 3 42
Preparation for Midterm exam 1 20 20
Laboratory (including preparation)
Final exam 1 2 2
Homework
Presentation (including preperation) 1 10 10
Project 1 20 20
Total Work Load168
Total Work Load / 30 (h)5.6
ECTS Credit of the Course6
Language of InstructionTurkish
Work Placement(s)No

Programme Qualifications / Learning Outcomes

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K2 X X X
K3 X X
K4 X X
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