Information Package / Course Catalogue
Artificial Intelligence in Science Education
Course Code: FBÖ459
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 0
Credit: 2
Lab: 0
ECTS: 4
Objectives of the Course

Because prospective science educators will use AI Technologies in their classroom and daily lives, to inform them about what is AI and its uses in science education is aimed in this course.

Course Content

Basic concepts of AI, individual differences in science education, intelligent tutoring systems, AI in various science education stages

Name of Lecturer(s)
Lec. Hanife Can ŞEN
Learning Outcomes
1.Defines AI.
2.Knows individual differences in science education.
3.Lists the properties of ITSs.
4.Tells the usage of AI in science education stages.
5.Propose ways to use AI in inquiry base science education.
Recommended or Required Reading
1.UNESCO (2019). Artificial Intelligence in Education: Challenges and Opportunities for Sustainable Development
2.Susanne P. Lajoie and Martial Vive. (2018). Artificial Intelligence in Education
Weekly Detailed Course Contents
Week 1 - Theoretical
An overview to AI Introduction
Week 2 - Theoretical
Introduction to AI
Week 3 - Theoretical
Basic concepts of AI
Week 4 - Theoretical
Individual differences in science education
Week 5 - Theoretical
Intelligent tutoring systems (ITSs)
Week 6 - Theoretical
AI in motivating students
Week 7 - Theoretical
AI in preparing lesson (MIDTERM)
Week 8 - Intermediate Exam
AI in transferring the learned knowledge into different contexts
Week 9 - Theoretical
AI in evaluating the lesson
Week 10 - Theoretical
AI examples in informal science learning
Week 11 - Theoretical
AI in international science education evaluation programs
Week 12 - Theoretical
AI in inquiry based science education
Week 13 - Theoretical
AI and STEM
Week 14 - Theoretical
AI and 21st century skills (FINAL)
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory130226
Assignment2048
Reading50525
Individual Work50525
Midterm Examination1617
Final Examination1729
TOTAL WORKLOAD (hours)100
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
5
2
2
4
3
4
2
3
4
5
OÇ-2
5
3
3
3
2
3
3
2
3
3
OÇ-3
5
5
5
4
4
5
3
4
4
4
OÇ-4
5
5
5
5
5
4
3
4
5
5
OÇ-5
5
5
5
5
4
5
3
5
4
4
Adnan Menderes University - Information Package / Course Catalogue
2026