ARTIFICIAL INTELLIGENCE AND APPLICATIONS IN BUSINESS PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
IBT429	ARTIFICIAL INTELLIGENCE AND APPLICATIONS IN BUSINESS	7	3	3	7

GENERAL INFORMATION
Language of Instruction :	English
Level of the Course Unit :	BACHELOR'S DEGREE, TYY: + 6.Level, EQF-LLL: 6.Level, QF-EHEA: First Cycle
Type of the Course :	Compulsory
Mode of Delivery of the Course Unit	-
Coordinator of the Course Unit	Assoc.Prof. GÜLGÜN ÇİĞDEM
Instructor(s) of the Course Unit
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	The aim of this course is to help students grasp the integration of artificial intelligence technologies into business processes and equip them with practical knowledge and skills related to the emerging digital transformation dynamics in the business world. By learning fundamental AI applications such as machine learning, natural language processing, predictive analytics, robotic process automation, and decision support systems, students will gain competence in strategic decision-making, productivity enhancement, customer experience improvement, and sustainable business model building in businesses. The course aims not only to convey theoretical knowledge but also to develop students' problem-solving, innovation, entrepreneurship, and data-driven thinking skills through project-based work, case studies, and industry collaborations. This way, students will develop into professionals who can create a competitive advantage in businesses through the use of AI and prioritize ethical and sustainability dimensions.
Contents of the Course Unit:	The "Artificial Intelligence and Applications in Business" course covers the fundamental concepts of artificial intelligence and the integration of technologies such as machine learning, natural language processing, robotic process automation, and data analytics into business functions. The course presents AI use cases in areas such as marketing, finance, human resources, supply chain, and customer relations, while also addressing ethical, data security, and sustainability dimensions. In addition to the theoretical framework, case studies and project-based applications strengthen students' skills in developing innovative AI-powered business models, improving decision-making processes, and adapting to the dynamics of digital transformation.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
Explains the fundamental concepts of artificial intelligence, including machine learning, deep learning, natural language processing, robotic process automation, and predictive analytics, and their roles in business transformation.
Analyzes the integration of AI applications into core business functions such as finance, marketing, human resources, supply chain, and international trade.
Applies AI-based data analytics and forecasting tools to support managerial decision-making and improve operational efficiency and customer experience.
Evaluates AI strategies in terms of ethical responsibility, data security, explainability (XAI), sustainability, and competitive advantage.
Designs a project-based AI-driven business solution or model that addresses a real organizational problem and demonstrates data-driven, innovative, and strategic thinking.

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	Reading, Literature Review	Introduction: Definition and Historical Development of Artificial Intelligence	Lecture, Question and Answer
2	Reading, Literature Review	The Role of Artificial Intelligence in Business	Lecture, Question and Answer
3	Reading, Literature Review	AI Fundamentals: Machine Learning, Deep Learning, Natural Language Processing	Lecture, Question and Answer
4	Reading, Literature Review	AI Application Areas: Finance, Marketing, Human Resources	Lecture, Question and Answer
5	Reading, Literature Review	AI in Business Decision-Making	Lecture, Question and Answer
6	Reading, Literature Review	AI-Based Forecasting and Data Analytics	Lecture, Question and Answer
7	Reading, Literature Review	AI Strategy & Management	Lecture, Question and Answer
8	Reading, Literature Review	AI-Enabled Supply Chain and Logistics Applications	Lecture, Question and Answer
9	Reading, Literature Review	International Trade and Access to Global Markets with AI	Lecture, Question and Answer
10	-	MID-TERM EXAM	-
11	Reading, Literature Review	The Ethical Dimensions of AI and Responsible Use in Business	Lecture, Question and Answer
12	Reading, Literature Review	Explainable Artificial Intelligence (XAI) and Its Management Applications	Lecture, Question and Answer
13	Project Presentations	AI Project Development Process in Business (Group Work and Presentations)	Lecture, Question and Answer
14	Project Presentations	AI Project Development Process in Business (Group Work and Presentations)	Lecture, Question and Answer
15	Project Presentations	AI Project Development Process in Business (Group Work and Presentations)	Lecture, Question and Answer
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description	Examination Method

Level of Contribution
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Summarizing basic and advanced topics in International Trade and Business.
2	To define the theories, concepts and principles of International Trade and Business and its sub-fields.
3	Interpreting the relationship of International Trade and Business with other disciplines.
4	Using the theoretical knowledge gained in the field of International Trade and Business in professional practices and daily life.

KNOWLEDGE
Factual
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Explaining current events and phenomena in the field with a holistic perspective analytically and systematically based on advanced knowledge and skills.
2	Solving problems encountered in business life, at individual and organizational level.
3	Effective use of computer programs (such as SPSS, R, Excel, Stata) in the face of complex business problems.
4	Evaluating the developments in the world from an intellectual perspective in the light of the joint faculty courses taken.

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Recognizing new theoretical and practical approaches in the field of International Trade and Business.
2	Recognize the relevant literature effectively.
3	Independently organize activities towards organizational goals and objectives.

SKILLS
Practical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	To conduct qualitative and quantitative research on the subjects related to the field.

OCCUPATIONAL
Autonomy & Responsibility
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	To be able to work independently in the light of the knowledge gained in the field of International Trade and Business and to take responsibility for the work done.

OCCUPATIONAL
Learning to Learn
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Adopting the philosophy of lifelong learning.

OCCUPATIONAL
Communication & Social
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Using foreign language skills effectively in business and social life, explaining their demands in writing or verbally.
2	To design a healthy communication network in the business world by using social life skills.

OCCUPATIONAL
Occupational and/or Vocational
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Benefiting from the theoretical and historical knowledge of international trade and business; analyzes current issues, events and problems.
2	Continually improving their knowledge of the structure and characteristics of the variables of international trade and business.

WORKLOAD & ECTS CREDITS OF THE COURSE UNIT
Workload for Learning & Teaching Activities
Type of the Learning Activites	Learning Activities (# of week)	Duration (hours, h)	Workload (h)
Lecture & In-Class Activities	0	0	0
Preliminary & Further Study	0	0	0
Land Surveying	0	0	0
Group Work	0	0	0
Laboratory	0	0	0
Reading	0	0	0
Assignment (Homework)	0	0	0
Project Work	0	0	0
Seminar	0	0	0
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	0	0	0
Practice at a workplace	0	0	0
Occupational Activity	0	0	0
Social Activity	0	0	0
Thesis Work	0	0	0
Field Study	0	0	0
Report Writing	0	0	0
Final Exam	0	0	0
Preparation for the Final Exam	0	0	0
Mid-Term Exam	0	0	0
Preparation for the Mid-Term Exam	0	0	0
Short Exam	0	0	0
Preparation for the Short Exam	0	0	0
TOTAL	0	0	0