We will walk through the syllabus, academic expectations, and how grading works. We will introduce what an information system is and look at how many of them a typical student already interacts with in a single day. In a short in-class exercise, you will map your own personal information system on one page: every account, every app, and every place your data is stored. The session ends with a brief discussion of what we will cover in the following 14 weeks.

We will examine what information systems are and how they work in practice. Using one well-known service as a worked example, we will trace how a single user action travels through the layers of a modern system: device, network, server, and database. In the second half of class you will work in small groups to map the same layers for a different digital service of your choice and present a short summary to the class. By the end of the session you will be able to describe how any digital service is structured at a high level and identify where it can fail.

We will discuss the types of information systems used in daily academic life: tools for managing information, learning, communication, and collaboration. We will introduce the common pattern behind every productivity system (capture, organize, do, review), then explore how cloud-based platforms such as Notion, Google Workspace, and Trello implement that pattern in different ways. In the second half of class you will set up a working personal productivity system in the lab. The session also introduces AI tools and the AI-reflection workbook, which opens this week.

We will examine what data is, how it is used, and why representation matters. We will analyse several real examples of charts that misled readers and discuss what made each one misleading. The session then introduces spreadsheet software, with particular focus on one concept that students often miss in their early use: the difference between relative and absolute cell references. Quiz 1 runs in the last 30 minutes of class and covers material from sessions 1 to 3.

Quiz 1 covers the lecture PDFs from Sessions 2, 3 and 4: information systems, personal productivity systems and introductory AI. Excel is not included. Available Monday 5 October 2026, 08:00–18:00 Prague time. 12 questions, 40 minutes, one attempt. Start by 17:20 to allow the full 40 minutes before closing.

We will introduce generative AI tools and explain, in plain language, how they produce their output. Understanding the underlying mechanism helps explain both their strengths and their tendency to hallucinate. We will introduce a simple prompt framework that you can name and apply, and then run the same task across three different AI tools in the lab to compare results. By the end of the session you will be able to write effective prompts, choose the right tool for a given task, and judge when AI output should and should not be trusted.

We will explore how AI tools can support academic research and writing, focusing on where they help (brainstorming, summarising, rephrasing) and where they are unsafe (generating facts, sources, or quotes). The session includes a live demonstration of AI fabricating academic citations, which we will verify together to make the limits visible. In the second half of class I will brief you on the semester project: topic options, team formation, deliverables, and deadlines. The AI workbook checkpoint is submitted at the end of class.

We will examine how AI-generated content is produced across text, image, and audio, and how to recognise it. The session uses several well-known global examples of synthetic media and discusses how each one affected public trust. In the second half of class you will create one piece of AI content in the lab and exchange with a classmate to attempt detection. The session closes with a short discussion of how to evaluate the trustworthiness of content online when traditional cues no longer hold.

We will begin with the mid-term test, which covers all material from sessions 1 to 7. The test runs for 90 minutes and uses a mix of multiple choice, short answer, and one applied question. The test is closed-book and AI tools are not permitted. The remaining class time is used for a short debrief and a brief preview of the second half of the course.

We will examine the main threats students face online: phishing, weak passwords, social engineering, and data leaks. We will introduce the concept of a threat model and apply it to your own situation: who would target you, why, and what is worth protecting. In the second half of class you will check your own email against a public breach database, install a password manager, and enable two-factor authentication on at least your primary email. The session closes with a short discussion of GDPR and your data rights in the EU.

We will work with spreadsheet software to explore how formulas and functions support data organisation and decision-making. The session covers cell formatting, basic functions, logical functions (IF, AND, OR), and lookup functions (VLOOKUP and XLOOKUP). We will also examine one well-known case where a spreadsheet error caused serious real-world consequences and use it to introduce the discipline of separating inputs, calculations, and outputs in any model you build. The semester project paper is due by end of class.

This session is dedicated to practice. Each team works with the same real public dataset, such as global music streaming, World Bank country data, or sports statistics, and goes through the full process of cleaning, organising, and analysing the data. We will cover the basic statistical concepts needed to read data honestly: mean versus median, outliers, and common sources of error. Each team produces one chart with one clear insight and presents it to the class at the end of the session.

We will examine how different platforms (whiteboards, slide tools, visual organisers) support brainstorming, structuring, and presenting ideas. The session introduces one structuring principle that improves most presentations (one claim per slide, supported by evidence) and applies it to a short comparison of strong and weak example slides. In the second half of class, each team builds one slide for the upcoming project presentation, exchanges it with another team for structured feedback, and revises it before leaving the room.

Each team will present their semester project: 10 minutes per team plus 5 minutes of Q&A. The audience will use a structured feedback rubric, and every non-presenting student is expected to ask at least one substantive question during the session. The final 20 minutes are a short course retrospective in which you reflect on what was most useful, what you would change, and what you will take with you. The AI-reflection workbook final submission is due at the end of class.

We will close the semester with the final test, covering material from the whole course with somewhat more weight on the second half. The test runs for 90 minutes, uses multiple choice and short answer, and ends with a short essay where you apply concepts from the course to a digital service you use. The test is closed-book and AI tools are not permitted. The remaining class time is used to look ahead at follow-on courses and resources for further reading.