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Lecturer(s)
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Procházka Roman, doc. PhDr. Mgr. Ph.D.
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Petr Kryštof, Mgr.
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Course content
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This course explores the interdisciplinary convergence of psychological science and artificial intelligence (AI) and prepares students to become "informed users" and innovators in an increasingly algorithm-driven professional environment, particularly in applied psychology. Participants will expand their knowledge and skills in theoretical foundations and explore how computational tools can be applied in psychological research and how these tools are transforming clinical, organizational, and forensic practice. Topics: 1. What is AI, or Applied Statistics 2. Additional Concepts in Applied Statistics 3. Human vs. Machine 4. Testing Large Language Models, Low/No-Code Solutions 5. AI in Psychological Research 6. AI in Clinical Psychology 7. Computational psychiatry 8. AI in work and organizational psychology 9. AI in forensic psychology 10. Data, ethics, regulation, audit 11. Presentation of student projects I 12. Presentation of student projects II
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Learning activities and teaching methods
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Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration, Projection (static, dynamic)
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Learning outcomes
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1. Students are able to identify and describe differences between human cognitive processes and those of artificial intelligence systems. 2. They critically evaluate AI-generated content with respect to systemic biases, hallucinations, and methodological limitations, and are aware of the risks associated with the passive acceptance of algorithmic outputs. 3. Students gain hands-on experience with various models of AI tool utilization. 4. They demonstrate a basic understanding of ethical and regulatory frameworks. 5. They design and present a solution based on AI tools and critically assess its potential impacts.
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.
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Prerequisites
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The course has no prerequisites
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Assessment methods and criteria
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Seminar Work, Final project
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. Conduct a group business presentation in which students must identify a real-world psychological problem and propose a defensible, ethical solution based on algorithmic data processing.
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Recommended literature
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Diel, A., Lalgi, T., Schröter, I. C., MacDorman, K. F., Teufel, M., & Bäuerle, A. (2024). Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers. .
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Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., & Nelson, C. Clinical versus mechanical prediction: a meta-analysis. .
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Poldrack, R. A. Statistical Thinking for the 21st Century. 2024.
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Sutton, R. The bitter lesson. 2019.
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