Artificial Intelligence: Foundations, Applications, and the Future 2400-ZEWW1022(KC)-OG

Activities carried out as part of the project “Integrated Program for the Development of Teaching – ZIP 2.0,” co-financed by the European Social Fund – European Funds for Social Development 2021–2027 (FERS) (Agreement No.: FERS.01.05-IP.08-0365/23-00).

The course is structured as three acts. The first act covers the foundations of artificial intelligence: its history from rule-based systems through the early AI winters, the shift to statistical machine learning, the rise of deep neural networks, and the transformer architecture that underlies modern AI. The second act examines how AI processes and generates different types of content: how large language models handle language and reasoning, how machines see, hear, and create images, audio, and video, and how AI agents pursue goals and use tools autonomously in the world. The third act addresses the broader impact of AI: the compute infrastructure and global supply chains that determine who controls the technology, the data pipelines and hidden labor behind AI systems and the biases they carry, the effects on employment and the future of work, legal accountability and the EU regulatory landscape, and the scientific potential and safety challenges on the horizon.
Throughout the course, students use AI tools for homework assignments, and hands-on in-class demonstrations are a regular feature of each session.

Course coordinators

Artur Siurdyban

Type of course

general courses

Learning outcomes

After completing the course, the student:
• Understands the historical development of artificial intelligence and the key transitions between paradigms.
• Understands how modern AI systems are built, trained, and deployed, including large language models, generative models, and autonomous agents.
• Can evaluate AI-generated content critically, including understanding bias, hallucinations, and the limits of AI reasoning.
• Can use AI tools effectively for analytical and creative tasks, including prompting and retrieval-augmented approaches.
• Understands the infrastructure and geopolitical dimensions of AI, including compute dependencies and supply chain concentration.
• Understands the social, legal, and economic consequences of AI, including labor market effects and regulatory frameworks.
• Formulates their own informed position on the future development of AI and its implications for their field and career.

Assessment criteria

For the general university subjects offered in the ZIP 2.0 Programme the mandatory method of verifying the assumed learning outcomes is a pre-test and post-test prepared by the lecturer in accordance with the specific nature of the subject, enabling the verification of the increase in knowledge and skills.

The methods include mini-lectures with presentations, including live demonstrations of AI tools. Whenever possible, the demonstrated tools are freely accessible, allowing students to complete short, practical homework assignments. Selected topics, particularly those with a less established research foundation (e.g., future development directions), are addressed in the form of moderated discussions.
Course assessment is based on a final written report (30%) and a continuous assessment component comprising automated in-class quizzes, attendance, and homework assignments (70%).

Bibliography

Required:
• Course materials provided by the instructor via Moodle (reading distributed weekly).
Optional reading:
• Turing, A. (1950). Computing Machinery and Intelligence. Mind, 59(236), 433–460.
• MIT Technology Review — What is AI?
• MIT Technology Review — A short history of AI, and what it is (and isn’t)
• Stanford University Human-Centered Artificial Intelligence: Brief Definitions of Key Terms in AI
• World Economic Forum — The Future of Jobs Report 2025
• Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS 2017.
• Wei, J. et al. (2022). Emergent Abilities of Large Language Models. TMLR 2022.
• Brynjolfsson, E. (2022). The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence. Daedalus, 151(2).
• European Commission — EU AI Act: official summary (eur-lex.europa.eu).

Additional information

Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes: