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