Information Package / Course Catalogue
Artificial Intelligence Knowledge
Course Code: EMY003
Course Type: Required
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 0
Credit: 2
Lab: 0
ECTS: 2
Objectives of the Course

The aim of this course is to enable students to learn the basic concepts of artificial intelligence and to use AI tools consciously in professional processes related to the real estate sector.

Course Content

The course covers artificial intelligence, data, algorithms, machine learning, generative AI, prompt writing, reporting with AI, AI use in real estate, GIS connections, ethics, and reliability.

Name of Lecturer(s)
Learning Outcomes
1.The student explains the concepts of artificial intelligence, data, algorithm, model, and machine learning at a basic level.
2.The student understands the use of generative AI tools in producing text, visuals, reports, and analyses.
3.The student develops the ability to write effective prompts and obtain accurate outputs from AI.
4.The student explains the use of AI in real estate, land registry, zoning, valuation, and customer relationship processes.
5.The student understands that AI outputs must be checked in terms of accuracy, ethics, and reliability.
Recommended or Required Reading
1.Instructor’s Lecture Notes
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to Artificial Intelligence: The concept of artificial intelligence, its historical development, daily-life applications, and its importance for the real estate sector are explained.
Week 2 - Theoretical
Basic Concepts of Artificial Intelligence: Basic concepts such as algorithm, data, model, prediction, automation, machine learning, and deep learning are explained in a simple way.
Week 3 - Theoretical
The Relationship Between Data and Artificial Intelligence: The importance of data for AI systems, data types, data quality, missing data, and inaccurate data are explained.
Week 4 - Theoretical
Introduction to Machine Learning: How AI can learn from data is explained through examples such as classification, prediction, recommendation systems, and pattern recognition.
Week 5 - Theoretical
Generative Artificial Intelligence: Systems similar to ChatGPT are introduced through examples such as text generation, image generation, document preparation, summarization, and content creation.
Week 6 - Theoretical
Prompt Writing Skills: Methods for writing effective prompts, assigning roles, providing context, using examples, and defining output formats are demonstrated to obtain better results from AI.
Week 7 - Practice
Practical Use of Artificial Intelligence: Students are asked to create a simple real estate listing, customer response, portfolio description, or report draft using AI.
Week 8 - Theoretical
Use of Artificial Intelligence in the Real Estate Sector: AI applications in portfolio management, customer analysis, price prediction, listing optimization, marketing, and sales processes are discussed.
Week 9 - Theoretical
Artificial Intelligence in Land Registry, Cadastre, and Zoning Processes: Reading title deed documents, interpreting zoning information, parcel analysis, and decision-support applications are explained with examples.
Week 10 - Theoretical
Geographic Information Systems and Artificial Intelligence: The role of AI in map data, location analysis, real estate valuation, environmental analysis, and spatial decision-support systems is explained.
Week 11 - Theoretical
Reporting and Presentation Preparation with Artificial Intelligence: Students are shown how to use AI to prepare real estate reports, market analyses, customer information texts, and presentation drafts.
Week 12 - Theoretical
Ethics, Reliability, and Legal Risks in Artificial Intelligence: Misinformation, bias, personal data protection, copyright, professional responsibility, and checking AI-generated outputs are covered.
Week 13 - Theoretical
Artificial Intelligence in Business Life and Future Professions: How AI transforms business processes, the new skills required for real estate agents, and future professional opportunities are evaluated.
Week 14 - Theoretical
General Review and Practical Project: Students are asked to prepare a sample real estate portfolio, customer scenario, zoning interpretation, or mini decision-support report using AI.
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment1%5
Quiz1%5
Midterm Examination1%30
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Assignment1044
Quiz1022
Midterm Examination1022
Final Examination101414
TOTAL WORKLOAD (hours)50
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
PÇ-13
OÇ-1
3
2
3
2
3
2
1
2
1
4
2
3
2
OÇ-2
2
3
2
1
2
3
2
4
1
2
3
2
4
OÇ-3
2
3
2
3
2
1
4
2
4
1
2
3
2
OÇ-4
3
2
3
2
4
1
2
1
4
5
2
3
2
OÇ-5
2
2
2
3
2
4
1
2
5
2
3
2
1
Adnan Menderes University - Information Package / Course Catalogue
2026