CEIT358 ARTIFICIAL INTELLIGENCE: APPLICATIONS IN EDUCATION

Course Code:4300358
METU Credit (Theoretical-Laboratory hours/week):3 (3.00 - 0.00)
ECTS Credit:4.0
Department:Computer Education and Instructional Technology
Language of Instruction:English
Level of Study:Undergraduate
Course Coordinator:
Offered Semester:Fall and Spring Semesters.

Course Objectives

At the end of this course, the student will be able to:

  • Discuss the interrelated subjects and issues involved in Artificial Intelligence in education.
  • Respond effectively to those issues and processes which impact the successful applications.
  • Use and analyze AI applications in education.
  • Develop practical strategies for integrating AI into instructional design, assessment, and student engagement.

Course Content

Intelligence and features; difference between Artificial Intelligence (AI) and human intelligence; Artificial Intelligence: Current status and application areas; the history of artificial intelligence; expert systems: components, properties: expert systems: design, applications and technology; use of expert systems in education; intelligent learning systems; big data in education; learning analytics; educational agent; adaptive learning and adaptive testing; using logical programming languages.


Course Learning Outcomes

Upon successful completion of the course, the student should be able to:

  • Define intelligence and its features from different perspectives
  • Define learning, teaching, instruction, training, and education
  • Tell about the foundations of AI in education
  • Tell about the importance of data science and AI in education
  • Identify the differences between Artificial Intelligence vs Human Intelligence
  • Tell about the current status and application areas of AI
  • Tell about the history of artificial intelligence
  • Describe AI system in education
  • List different application areas of AI systems
  • List different AI technologies
  • Explain the impact of AI in education
  • Recognize the basic concepts of Artificial Intelligence and its applications in education
  • Tell about intelligent learning systems, Intelligent feedback and learning analytics
  • Tell about big data applications in education, and learning analytics
  • Tell about ethical, social, and policy considerations for AI in education
  • Tell about adaptive learning and adaptive testing
  • Use AI tools for grading, lesson planning, content generation, learning material development
  • Develop prompt design and effective classroom use cases
  • Prepare a PowerPoint presentation with narration on any of the topic from the course outline
  • Develop program segments using logical programming languages
  • Discuss the future of AI in education

Program Outcomes Matrix

Level of Contribution
#Program Outcomes0123
1They have the skill and knowledge to use information technologies.✔
2They use information technology to access information, and they analyze, synthesize, and evaluate knowledge by adapting to new situations.✔
3They use strategies and techniques based on learning theories and apply them to solve instructional problems in a systemic and systematic way✔
4They have skill and knowledge in analysis, design, development, implementation and evaluation in instructional design process.✔
5They implement learning-teaching methods and techniques in computer education.✔
6They have knowledge, skill and competency about computer hardware, operating systems, computer networks and programming languages.✔
7They determine measurement and evaluation methods and techniques used in computer education.✔
8They have the ability to conduct and present results of intra-disciplinary and inter-disciplinary researches in the field of instructional technology.✔
9They comprehend project management processes and implement and present projects electronically.✔
10They have critical thinking and problem solving skills.✔
11They have social communication and cultural exchange skills.✔
12They have legal knowledge, skills and attitudes required for teaching profession and apply them in the learning environment.✔

0: No Contribution 1: Little Contribution 2: Partial Contribution 3: Full Contribution