Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
7İF011Artificial Intelligence Literacy3+0+03516.10.2026
Course Details
Language of Instruction Turkish
Level of Course Unit Bachelor's Degree
Department / Program Radio, Television and Cinema
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The objective is to enable students to grasp the underlying logic, capabilities, and limitations of artificial intelligence technologies; to equip them with the skills to critically evaluate the outputs of generative AI tools; and to ensure they can utilize these tools—within processes such as visual communication, audiovisual production, and game design—in a creative and transparent manner, while maintaining an awareness of ethical, legal, and aesthetic responsibilities.
Course Content The definition of artificial intelligence literacy and international competency frameworks; the fundamental workings of machine learning; the capabilities and limitations of generative AI and large language models; prompt engineering and output verification; algorithmic bias and fairness; data labor and environmental costs; AI applications in visual design, film, animation, and game production; copyright and personality rights; synthetic media and disinformation; and principles of academic integrity and responsible use.
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Research Assist. Dr. Mahmut CERAN
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
Buolamwini, J. (2023). Unmasking AI: My Mission to Protect What Is Human in a World of Machines. Random House.
Long, D. & Magerko, B. (2020). What is AI Literacy? Competencies and Design Considerations. CHI 2020.
UNESCO (2024). AI Competency Framework for Students. UNESCO Publishing.
Bender, E. M., Gebru, T., McMillan-Major, A. & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots. ACM FAccT 2021.
Shaker, N., Togelius, J. & Nelson, M. J. (2016). Procedural Content Generation in Games. Springer.
O ` Neil, C. (2016). Weapons of Math Destruction. Crown.
Course Notes Crawford, K. (2021) Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence, Yale University Press
Buolamwini, J. (2023) Unmasking AI: My Mission to Protect What Is Human in a World of Machines, Random House

Course Category
Field %100

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 40
Final examination 1 % 60
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 7 98
Mid-terms 1 4 4
Project 1 1 1
Final examination 1 6 6
Total Work Load   Number of ECTS Credits 5 151

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Ability to explain the fundamental concepts, operating principles, and international AI literacy frameworks of artificial intelligence and machine learning.
2 Critically evaluate the outputs of generative AI tools in terms of accuracy, bias, reliability, and source transparency.
3 Use AI tools appropriately and effectively in visual design, audiovisual production, and game development processes.
4 Discuss the implications of AI use for copyright, personality rights, labor, and academic integrity, and transparently document its use in their own production process.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 What Is AI Literacy? Concepts and Frameworks Preliminary readings on the topics Course Resources
2 Institutional Frameworks and Policy Preliminary readings on the topics Course Resources
3 How Do Artificial Intelligence and Machine Learning Work? Preliminary readings on the topics Course Resources
4 Generative AI and Large Language Models: Capabilities and Limitations Preliminary readings on the topics Course Resources
5 Prompt Literacy and Critical Use Preliminary readings and practical exercises on the topics Course Resources
6 Algorithmic Bias and Fairness Preliminary readings on the topics Course Resources
7 The Material Foundations of AI: Data, Labor, and the Environment Preliminary readings on the topics Course Resources
8 Midterm Exam Preparation for the Midterm Exam Course Resources
9 AI in Creative Production I: Visual Design, Illustration, and Text-to-Image Tools Preliminary readings and practical exercises on the topics Course Resources
10 AI in Creative Production II: Film, Video, Animation, and Creative Labor Preliminary readings and practical exercises on the topics Course Resources
11 AI in Game Development Preliminary readings and practical exercises on the topics Course Resources
12 Copyright, Consent, and Law Preliminary readings on the topics Course Resources
13 Deepfakes, Synthetic Media, and Disinformation Preliminary readings on the topics Course Resources
14 AI in Higher Education, Academic Integrity, and Effects on Learning Preliminary readings on the topics Course Resources
15 Responsible and Creative Use of AI, Project Presentations Preparation for the Final Assignment Course Resources

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15
C1 5 1 3 1 5 3 3 3 1 1 1 1 3 3 3
C2 5 1 3 3 5 5 5 5 3 1 1 3 3 1 5
C3 3 5 5 1 5 1 1 1 5 5 5 3 3 5 3
C4 3 1 1 5 3 5 5 5 1 1 1 3 3 1 3

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant