Information Package
baskent.edu.tr

Program of Preschool Teaching

EDS425

ARTIFICIAL INTELLIGENCE APPLICATIONS IN EDUCATION

Course Code
EDS425
Semester
-
Theory+Practice
2 + 0
ECTS
4
Course TypeElective Course
Course LevelBachelor's Degree
Year of Study-
Semester-
ECTS4
Name of Lecturer(s)
Learning Outcomes

At the end of this course, the students;

  1. Explain the fundamental concepts, historical development, and educational relevance of artificial intelligence.
  2. Identify key AI applications such as machine learning, natural language processing, data analysis, and pattern recognition.
  3. Analyze the influence of AI on teaching, learning, and assessment processes.
  4. Evaluate examples of AI applications across different disciplines.
  5. Discuss ethical, social, and policy-related implications of AI in education.
  6. Design or propose AI-based solutions for teacher education and personalized learning contexts.
  7. Demonstrate the ability to use AI critically, responsibly, and ethically.
Mode of DeliveryFace to face
PrerequisitesNo
Recommended Optional Programme Components
Course DescriptionIn this course, students explore the fundamental concepts and evolution of artificial intelligence (AI) and its role in education. Applications such as machine learning, data analysis, pattern recognition, and natural language processing are introduced, while the use of AI in teaching, learning, and institutional processes is examined through historical, philosophical, and practical lenses. Students critically evaluate the impact of AI on teacher education, personalized learning, adaptive instruction, and student development, developing ethical and reflective approaches to the use of such technologies. Through examples from different disciplines (STEM, social sciences, languages, arts, etc.), the course enhances awareness of diverse applications of AI. It provides knowledge, perspectives, and practical opportunities for effectively employing AI in educational, administrative, and research contexts, while also discussing ethical considerations and policy implications for schools and educational systems.
Course Contents
WEEK TOPICS
1st Week Introduction to AI: Concepts, History, and Core Approaches
2nd Week AI in Education: Opportunities and Challenges
3rd Week Fundamentals of Machine Learning
4th Week Data Analysis and Pattern Recognition Applications
5th Week Natural Language Processing and Its Educational Applications
6th Week Learning Analytics and Data-Driven Decision Making
7th Week Personalized and Adaptive Learning Systems
8th Week Project Proposal Presentations
9th Week AI Tools for Teacher Education
10th Week AI for Student Development and Support Systems
11th Week AI Applications Across Disciplines
12th Week Ethics, Trust, and Data Privacy in AI
13th Week Educational Policy and AI Integration
14th Week Research Trends and Future Directions in AI and Education
Recommended or Required Reading1. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education promises and implications for teaching and learning. Center for Curriculum Redesign.
2. Luckin, R. (2018). Machine Learning and Human Intelligence: The Future of Education for the 21st Century. UCL Institute of Education Press.
3. Shah, P. (2023). AI and the Future of Education: Teaching in the Age of Artificial Intelligence. John Wiley & Sons.
4. Holmes, W., & Miao, F. (2023). Guidance for generative AI in education and research. Unesco Publishing.
5. Zawacki-Richter, O., Marin, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education?where are the educators?. International journal of educational technology in higher education, 16(1), 1-27.
6. Cope, B., Kalantzis, M., & Searsmith, D. (2021). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational philosophy and theory, 53(12), 1229-1245.
Planned Learning Activities and Teaching Methods
  • Lecture
  • Discussion
  • Questions/Answers
  • Case Study
  • Problem Solving
  • Practice
  • Project
  • Report Preparation
  • Presentation
Assessment Methods and Criteria
Quantity Percentage (%)
Assignment 3 30
Practice 1 10
Project 1 20
Total(%) 60
Contribution of In-term Studies to Overall Grade(%) 60
Contribution of Final Examination to Overall Grade(%) 40
Total(%) 100
ECTS Allocated Based on Student Workload
Activities Quantity Duration (Hour) Total Workload (Hour)
Midterm exam
Preparation for Quiz
Individual or group work
Preparation for Final exam 1 18 18
Course hours 13 2 26
Preparation for Midterm exam
Laboratory (including preparation) 14 1 14
Final exam 1 1 1
Homework 3 12 36
Presentation (including preperation) 1 12 12
Project 1 12 12
Total Work Load119
Total Work Load / 30 (h)3.96
ECTS Credit of the Course4
Language of InstructionTurkish
Work Placement(s)No

Programme Qualifications / Learning Outcomes

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