1. FIRST CYCLE - BACHELOR'S DEGREE
  2. FACULTY OF EDUCATION
  3. DEPARTMENT OF MATHEMATICS AND SCIENCE TEACHING DEPARTMENT
  4. 133 ELEMENTARY MATHEMATICS EDUCATION
Course Information Course Learning Outcomes Course's Contribution To Program ECTS Workload Course Details
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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
YBS 488GENERATIVE ARTIFICIAL INTELLIGENCE2 + 13rd Semester3

COURSE DESCRIPTION
Course Level Bachelor's Degree
Course Type Elective
Course Objective The aim of this course is to introduce the fundamental concepts, operating principles, major model types, and current areas of use of generative artificial intelligence. The course covers essential topics such as the foundation model approach, prompt design, content generation, output evaluation, accuracy and reliability, hallucination, bias, ethics, and security. It also aims to demonstrate how generative artificial intelligence tools can be used in different types of problems and to foster a critical, informed, and responsible perspective toward these technologies.
Course Content Introduction to generative artificial intelligence; the historical development and fundamental concepts of generative artificial intelligence; differences between generative and discriminative approaches; the logic of foundation models; an overview of text, image, audio, and multimodal generation systems; introduction to large language models; fundamentals of prompt design; strategies for writing effective prompts; applications in content generation, summarization, rewriting, and idea development; methods for improving output quality; monitoring accuracy and reliability; hallucination mechanisms, types of bias, and error analysis; copyright, ethics, privacy, and security; areas of use of generative artificial intelligence in education, communication, information access, automation, and decision-making processes.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.

COURSE LEARNING OUTCOMES
1Explains the fundamental concepts of generative artificial intelligence, the main model types, and the differences between generative and discriminative approaches.
2Creates effective prompts for different purposes and uses generative artificial intelligence tools in an informed manner.
3Evaluates the accuracy, consistency, and usefulness of generative artificial intelligence outputs.
4Evaluates the accuracy, consistency, and reliability of model outputs; identifies hallucination, bias, and ethical risks.
5Evaluates the potential and limitations of generative artificial intelligence across different application areas.

COURSE'S CONTRIBUTION TO PROGRAM
Data not found.

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)21428
Hours for off-the-classroom study (Pre-study, practice)14228
Mid-terms177
Final examination11515
Total Work Load

ECTS Credit of the Course





78

3
COURSE DETAILS
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L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes