ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN BUSINESS AND MANAGEMENT

BUS491

Course Code: BUS491
Semester: -
Theory+Practice: 3 + 0
ECTS: 5

Course Type

Elective Course

Course Level

Bachelor's Degree

Year of Study

Semester

ECTS

Name of Lecturer(s)

Learning Outcomes

At the end of this course, the students;

Explain the fundamental principles of prompt engineering and apply various prompting techniques.
Use Generative AI tools (ChatGPT, Gemini, Claude AI, Canva AI, etc.) in a business context for business correspondence, report summarization, and content creation.
Discuss ethical, privacy, and copyright issues in the use of artificial intelligence.
Develop Generative AI-based business ideas, present prototypes, and propose solutions to business problems.

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

Course Description

The aim of this course is to enhance the students' understanding, knowledge and skills about applications, opportunities and risks on the Artificial Intelligence technologies and subfields of neural networks, genetic algorithms and machine learning in business management. Besides, it is aimed that students have information about security, ethics and social issues in digitalized enterprises. Students who miss more than 30% of total class hours receive an F2 grade.

Course Contents

WEEK	TOPICS
1^st Week	Generative AI and Its Role in the Business World
2^nd Week	Large Language Models and Their Core Principles
3^rd Week	Introduction to Prompt Engineering
4^th Week	Prompt Optimization and Techniques
5^th Week	Prompt Applications
6^th Week	Text Generation and Summarization: Business Correspondence and Reports
7^th Week	Visual Content Generation
8^th Week	No-Code Applications and Dashboard Creation
9^th Week	AI Tools in Practice
10^th Week	Chatbot and AI Assistant Development
11^th Week	Generative AI Applications: Cost, Resilience, and Safe Automation
12^th Week	Ethics, Copyright, and Artificial Intelligence
13^th Week	Project Presentations
14^th Week	General Review

Recommended or Required Reading

Phoenix, 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.
Course Notes

Planned Learning Activities and Teaching Methods

Lecture
Project
Presentation
Practice

Assessment Methods and Criteria

	Quantity	Percentage (%)
Assignment	1	10
Project	1	40
Total(%)		50
Contribution of In-term Studies to Overall Grade(%)		50
Contribution of Final Examination to Overall Grade(%)		50
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	1	14
Preparation for Final exam	1	40	40
Course hours	14	3	42
Preparation for Midterm exam	1	55	55
Laboratory (including preparation)
Final exam	1	2	2
Homework	1	3	3
Total Work Load			158
Total Work Load / 30 (h)			5.26
ECTS Credit of the Course			5

Language of Instruction

English

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3	LO4
K1	X	X	X	X
K2	X		X	X
K3		X
K4		X
K5
K6	X		X	X
K7
K8
K9
K10
K11
K12
K13