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Lecturer(s)
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Slavík Martin, Mgr. Ph.D.
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Course content
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BLOCK 1: Me and information 1. Introduction to the subject and career planning: Self-awareness, soft skills, time management (GTD). The impact of artificial intelligence on the future employment of chemists and teachers. 2. Scientific literature and information hygiene: Types of documents, patents, and Open Science. FAIR data principles (Findable, Accessible, Interoperable, Reusable). 3. Chemical databases and searching: Principles of advanced searching (Boolean algebra). Working with sources such as Web of Science, PubChem, and specialised material/biological databases. BLOCK 2: Me and the team (Management and projects) 4. Fundamentals of management in science and education: Project life cycle (from idea to final report). Grant schemes. Team roles and effective communication. 5. Agile management and software tools: Traditional vs. agile management (Scrum, Kanban). Visualisation of time and tasks. Practical introduction to project management tools (Trello, Notion, MS Teams). BLOCK 3: Artificial Intelligence and Smart Data Processing 6. AI in Scientific Research and Text Processing: Use of LLM and specialised tools (Scite.ai, Gemini, Consensus...) for literature synthesis. Prompt engineering for chemists. Ethics and risks of AI (hallucinations, bias). 7. Personal knowledge system and citations: Citation managers (Zotero, Mendeley) and their integration with text editors. Applications for personal knowledge management. 8. Representation of chemical data: How a computer sees a molecule. SMILES, InChI, mol, sdf, cif formats. Conversions between formats. 9. Advanced data processing: Advanced spreadsheet processors (pivot tables). Use of AI assistants for cleaning, formatting, and basic analysis of experimental data. BLOCK 4: Chemist's tools and predictive modelling 10. Visualisation and 2D/3D modelling: Drawing structures (ACD/ChemSketch, ChemDraw). 3D visualisation and optimisation of molecular geometry (Avogadro, CheMagic, JSmol, PyMOL). 11. QSAR and property prediction: Introduction to quantitative relationships between structure and activity/property (QSAR/QSPR). Practical use of web predictors (e.g., SwissADME for drug evaluation, or tools for nanomaterial properties). 12. AI in biomolecule and material modelling: PDB database. Breakthrough technologies in structural biology (AlphaFold and prediction of 3D protein structures). Machine learning in materials chemistry. 13. Scientific data visualisation: Principles of creating publication graphs and visualising results. Business Intelligence tools (MS Power BI, Tableau, Looker Studio). CONCLUSION 14. Project presentations: Defence of the created research and project plan. Discussion on the effectiveness of traditional and AI tools used.
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Learning activities and teaching methods
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Monological explanation (lecture, presentation,briefing), Dialogue metods(conversation,discussion,brainstorming), Self-study (text study, reading, problematic tasks, practical tasks, experiments, research, written assignments), Observation, Active metods (simulation, situational contingency methods, drama,acting, namagerial acting ), Demonstration of student skills
- Home preparation for classes
- 10 hours per semester
- Class attendance
- 28 hours per semester
- Semestral paper
- 30 hours per semester
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Learning outcomes
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Obtaine an orientation in information resources, learning research methodology. Deepen the basic abilities and skills of work with information and communication technology (ICT), develop a comprehensive approach to ICT. An overview of the use of various types of computer software for scientific research.
After completing the course, students will: Apply advanced search strategies across chemical and patent databases (e.g., WoS, Scopus, PubChem) and effectively manage resources using citation managers (e.g., Zotero), in line with the principles of Open Science and FAIR data. Use generative artificial intelligence (LLM) tools for scientific text synthesis and data processing, critically evaluating their validity, identifying hallucinations, and adhering to information ethics. Plans and organises professional or research projects using project management methods (Kanban, Gantt charts) and modern team collaboration tools. Converts chemical structures between different digital representations (SMILES, InChI) and models structures in 2D and 3D visualisation programs. Explains the basic principles of machine learning in chemistry and QSAR/QSPR methodology (quantitative relationships between structure and activity/properties) and interprets outputs from online predictive tools. In the Bioengineering and Nanotechnology study program: Analyses and visualises 3D structures of macromolecules (proteins, enzymes) using databases (PDB) and modern AI tools (AlphaFold). Applies QSAR models and chemoinformatic approaches to predict biological activity and evaluate ADME-Tox parameters in potential drugs (drug design) or to predict the physicochemical properties of materials based on their structure. In the Chemistry Teaching study program: Didactically transforms digital tools (e.g., 2D/3D visualisation of molecules) and abstract concepts (e.g., QSAR prediction) into forms understandable and attractive to secondary and primary school students to develop their digital literacy (KRAAU 1.2 and 3.4). Integrates artificial intelligence tools into the preparation of teaching materials and assessment, while reflecting on ethical risks (KRAAU 6.2). Applies the principles of project and agile management in the organisation of school laboratory operations, inquiry-based teaching, or long-term student projects (KRAAU 3.3).
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Prerequisites
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knowledge of high school chemismy
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Assessment methods and criteria
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Combined examination, Practical demonstration of acquired skills, Oral presentation of self-study, Presentation of acquired knowledge via paper, Test
Structured Curriculum Vitae (1-2 pages A4 or on social network). Career vision for 10 years including matrix: Knowledge, skills | Personal characteristics | Values, principles | Interests, motivations, personal preferences. (1 page A4). AI assignment: Combined study: Develop a curriculum or teaching activity that integrates the use of AI into the teaching of your subject. Combined study: Choose a part of your final thesis and formulate an assignment for AI (e.g., https://gemini.google.com/app) that corresponds to its scope. If necessary, adjust the wording so that the answer is not trivial (commonly known) and contains information and contexts that you consider personally enriching and interesting for others as well. Verify the application's output against the original source and other sources you find, without using AI. Indicate which part of the AI's answer was new and enriching for you. If you have a different opinion on any part of the answer, state it and give reasons. Reflect on the entire experience of using AI for your own knowledge and professional growth.
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Recommended literature
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Gruber, D. Šetřme časem! Rychločtení. Rychlostudium.. Praha: Management Press, 1992.
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Sklenák, V. Data, informace, znalosti a Internet. C.H. Beck Praha, 2001. ISBN 80-7179-409-0.
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Šilhánek, J. Chemická informatika. Praha: VŠCHT, 2002. ISBN 80-7080-465-3.
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