|
Lecturer(s)
|
-
Procházka Roman, doc. PhDr. Mgr. Ph.D.
-
Petr Kryštof, Mgr.
|
|
Course content
|
This course introduces students to key concepts in artificial intelligence (AI) as they relate to the study of the human mind and behavior. It emphasizes conceptual understanding over technical details and includes practical skills relevant to psychology students. Topics include the history and philosophy of AI, the responsible use of AI tools in academic research, the psychological implications of AI, and critical issues such as data ethics, environmental concerns, and the use of AI in mental health and basic psychological research. The course is designed to address both basic practical skills and cover fundamental terminology. Topics: 1. What is AI, or Applied Statistics 2. A Brief History of AI 3. Using AI in Study I - Commonly Used Tools 4. Using AI in Studies II - Reading, Searching, and Learning with AI 5. Using AI in Studies III - Writing Academic Texts 6. Privacy and Data in the Age of Surveillance Capitalism 7. Ethical and Environmental Issues Related to AI 8. Fundamentals of Human-Computer and Human-Robot Interaction 9. Society and AI 10. Mental Health and AI 11. AI in Basic Research in Psychology 12. Responsible Academic Use and Digital Well-being
|
|
Learning activities and teaching methods
|
|
Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration, Projection (static, dynamic)
|
|
Learning outcomes
|
Students are able to define artificial intelligence and distinguish between symbolic and learning-based approaches. They explain how artificial intelligence systems differ from, and in what ways they resemble, human cognitive processes. They critically discuss how psychological theories have influenced artificial intelligence and how these theories have, in turn, been challenged by it. They formulate ethical and societal questions raised by AI in relation to personality, autonomy, and intelligence. They demonstrate fundamental skills in the responsible use of AI tools for academic writing, information retrieval, and data handling in psychology. They identify key risks and responsibilities related to data protection, algorithmic bias, and AI-supported decision-making.
Students will gain a deeper understanding of how large language models work and their potential applications in algorithm-based approaches across various domains of psychology.
|
|
Prerequisites
|
The course has no prerequisites
|
|
Assessment methods and criteria
|
Essay, Seminar Work
To successfully complete the course, each student must: 1. Present at least one current event or recent research study in a group or individually and lead a brief discussion with the rest of the class 2. Submit a 1,000-1,500-word essay on a selected topic by the agreed-upon deadline
|
|
Recommended literature
|
-
Boden, M. A. AI: Its nature and future. 2016.
-
Mitchell, M. Artificial intelligence: A guide for thinking humans. 2019.
-
Searle, J. R. Minds, brains, and programs. 1980.
-
Turing, A. M. Computing machinery and intelligence (1950). 2021.
|