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Lecturer(s)
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Polách Vladimír, Mgr. Ph.D.
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Marková Eva, Mgr. Ph.D.
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Course content
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1. History and theory of natural language processing: from symbolic NLP to the representation of meaning in information theory. 2. Architecture and technical foundations of LLMs: tokenisation, vectorisation, semantic embeddings and Transformers. 3. Prompting methodology and architecture: the R-T-F model, prompt chaining, few-shot prompting and iterative refinement. 4. Working with context and tone: specifying perspective, target audiences and stylistic register. 5. Closed-domain document analysis (RAG): source verification, preventing hallucinations and working in the NotebookLM environment. 6. AI in publishing and editorial practice: stylistic proofreading, drafting peer reviews and grant applications. 7. Systematisation and agent-based systems: creating custom GPTs, multi-stage automation and the principle of vibe coding. 8. Educational use of AI: Socratic dialogue, argumentation training, gamification and the creation of teaching aids. 9. Ethical, legal and institutional limits: plagiarism, AI disclosure, authorship and the ELIZA effect. (covered throughout the course) 10. AI ontology and the vision of AGI/ASI: the development of autonomy, the limits of consciousness, post-scarcity and the transformation of society.
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Learning activities and teaching methods
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Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming), Work with Text (with Book, Textbook)
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Learning outcomes
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- To understand the fundamentals of artificial intelligence (AI) and its applications in the fields of (among others) publishing, editing and creative work - To analyse the possibilities, ethical issues and limitations involved in working with AI - To acquire practical skills in working with AI tools - To create your own AI-based project focused on publishing and editorial practice
Upon successful completion of the course, the student will be able to: - explain the key theoretical and historical milestones in the development of NLP and the physical and mathematical principles underlying the functioning of large language models (tokenisation, embeddings); - design and apply advanced structured prompts (using R-T-F models, chaining and context specification) for complex text-based tasks; - create a functional architecture for customer AI agents or custom GPT bots with multi-stage instruction processing for specific work processes (proofreading, PR, editorial work); - critically assess the factual accuracy, coherence, ethical pitfalls and limitations of AI-generated outputs; - be familiar with and able to apply applicable ethical and research standards and academic rules (including citations and AI disclosure) in academic and creative work.
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Prerequisites
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unspecified
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Assessment methods and criteria
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Student performance, Systematic Observation of Student
- One absence is permitted; any absence may be made up by submitting the work required for that session (which will be available on Moodle) - At the end of each seminar, students must submit the results of their work in accordance with the lecturers' specifications (for more complex assignments, submission may take place in several stages) - A prerequisite for obtaining credit is the completion and presentation of a final project, which will be set as a group assignment
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Recommended literature
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Carr Nicholas. Skleněná klec. Brno. 2015.
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Dvořák, Tomáš a kol.:. Umělá inteligence jako (nové) médium. Praha. 2025.
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McStay, Andrew. (2018). Emotional AI : the rise of empathic media.
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PIORECKÝ, Karel ? HUSÁROVá, Zuzana. Kultura neuronových sítí. syntetická literatura a umění (nejen) v českém a slovenském prostředí. Praha. 2024.
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Rudolf Červenka. (2025). Umělá inteligence: zdroje, podstata, současný stav, využití, výhled.
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