Information Package / Course Catalogue
Artificial Intelligence in Occupational Safety and Health (osh)
Course Code: İSP133
Course Type: Required
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 1
Credit: 3
Lab: 0
ECTS: 3
Objectives of the Course

This course aims to introduce the applications of artificial intelligence technologies in the field of occupational health and safety (OHS), to provide information about basic concepts, and to teach how these technologies can be used in preventing occupational accidents, performing risk analysis, and in auditing processes.

Course Content

This course aims to introduce the applications of artificial intelligence technologies in the field of occupational health and safety (OHS) and to provide information about basic concepts.

Name of Lecturer(s)
Learning Outcomes
1.Defines the potential of Artificial Intelligence in the field of Occupational Health and Safety (OHS).
2.Explains the basic concepts of machine learning and image processing.
3.Can predict workplace accidents using data analysis and classification methods.
4.Explains the role of IoT and sensor technologies in OHS applications.
5.Can design digital communication using chatbots and messaging systems.
Recommended or Required Reading
1.Yazdi, M. (2024). Artificial Intelligence in Workplace Health and Safety, CRC Press.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to the Course; Artificial Intelligence Concepts in Occupational Health and Safety (OHS)
Week 2 - Theoretical
Fundamentals of Machine Learning; Sensor Technologies
Week 3 - Theoretical
Risk Analysis and Statistical Data Collection
Week 4 - Practice
Accident Prediction Using Classification Algorithms
Week 5 - Practice
Image Processing: Basic Concepts
Week 6 - Practice
Image Processing: Hazard Detection Applications
Week 7 - Practice
Chatbot Design for OHS Messaging Systems
Week 8 - Practice
Practical Presentations / Midterm Exam
Week 9 - Theoretical
Internet of Things (IoT) and Real-Time Monitoring Systems
Week 10 - Theoretical
Model Training and Evaluation
Week 11 - Theoretical
Ethical and Privacy Considerations in Artificial Intelligence
Week 12 - Theoretical
Midterm Presentations of Student Projects
Week 13 - Practice
Development of Sample AI Applications in Occupational Health and Safety (OHS)
Week 14 - Practice
Development of Sample AI Applications in Occupational Health and Safety (OHS) (Continued)
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures1%5
Verbal Examination1%5
Midterm Examination1%30
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory141128
Lecture - Practice141128
Midterm Examination19110
Final Examination19110
TOTAL WORKLOAD (hours)76
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
OÇ-1
4
3
3
4
4
4
4
5
2
4
OÇ-2
4
4
4
4
5
4
3
4
2
3
OÇ-3
4
5
4
4
5
4
4
5
2
4
OÇ-4
4
4
4
4
5
4
4
5
2
4
OÇ-5
3
4
4
3
5
4
3
4
2
3
Adnan Menderes University - Information Package / Course Catalogue
2026