Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
7POLS 475AI and Society3+0+03506.10.2026

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program Political Science and Public Administration (English)
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The objective of the "AI and Society" course is to provide students with a comprehensive and critical understanding of the multifaceted impacts of artificial intelligence (AI) and emerging digital technologies on society, public administration, ethics, the economy, and political institutions. The course aims to equip students with the analytical skills to examine how algorithmic systems influence institutional trust, bias, inequality, labor markets, and governance frameworks. Furthermore, it investigates the strategic role of AI in public policy—focusing on smart cities, digital governance, disinformation, democratic processes, and regulatory frameworks—enabling students to evaluate and critically reflect on the future trajectories of human–AI coexistence.
Course Content Theories of technology and social transformation; digital transformation, diffusion of innovation, and the digital divide; introduction to artificial intelligence and its integration into everyday life; institutional trust and public confidence in algorithmic systems; AI ethics (fairness, accountability, transparency, explainability, and responsible AI); algorithmic bias, discrimination, inclusion, and data inequality; automation, labor markets, the platform economy, and future professions; global AI governance, regulation, and public policy; digital government and AI in public administration; smart cities and urban governance; AI, social media algorithms, deepfakes, and democratic resilience; generative AI in education, copyright, and academic integrity; and the future scenarios of human–AI interaction and societal resilience.
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Prof.Dr. AYSU KES ERKUL
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources

Course Category
Social Sciences %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 % 25
Project 1 % 25
Final examination 1 % 50
Total
3
% 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 3 42
Mid-terms 1 16 16
Project 1 20 20
Final examination 1 30 30
Total Work Load   Number of ECTS Credits 5 150

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Ability to analyze the societal, ethical, and economic impacts of artificial intelligence technologies within a theoretical and critical framework.
2 Ability to evaluate ethical issues such as algorithmic bias, data inequality, transparency, and accountability in the context of global governance and public policies.
3 Ability to discuss the transformative impacts of AI on public administration, democracy, media, and employment through contemporary cases and formulate forward-looking solutions.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Technology and Society Relevant literature reading Lecture slides and notes
2 Artificial Intelligence as a Social Revolution Relevant literature reading Lecture slides and notes
3 Trust in Artificial Intelligence Relevant literature reading Lecture slides and notes
4 AI Ethics Relevant literature reading Lecture slides and notes
5 AI, Bias and Inequality Relevant literature reading Lecture slides and notes
6 AI and the Future of Work Relevant literature reading Lecture slides and notes
7 AI Governance Relevant literature reading Lecture slides and notes
8 Midterm Exam Exam preparation Exam materials
9 AI in Government Relevant literature reading Lecture slides and notes
10 Smart Cities and Urban AI Relevant literature reading Lecture slides and notes
11 AI, Democracy and Media Relevant literature reading Lecture slides and notes
12 AI in Education and Creativity Relevant literature reading Lecture slides and notes
13 The Future of Human–AI Society Relevant literature reading Lecture slides and notes
14 Review and Conclusion Term review Lecture slides and notes

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10
All 2 3 3 3 3 3 3
C1 2 3 3 3 3 4
C2 2 3 4 4 3 3 3
C3 3 4 2 3 3 3 3

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