AI has rapidly become part of everyday learning and work. If students learn how to use AI early, in a conscious framework, they gain a real competitive edge: researching, organizing, creating, and arguing faster.
The program’s goal is to make technology a tool rather than a crutch – with critical thinking, ethical control, and measurable outcomes.

Goals • Skills to be developed

• Creativity (idea generation, rewriting, style shifting)
• Logical thinking (task breakdown, step-by-step planning)
• Critical thinking (fact-checking, finding counterexamples)
• Teamwork (roles, version control, responsibility sharing)
• Argumentation (position, evidence, counter-argument handling)
• Prompt engineering (role assignment, chaining, example-driven prompts)
• Digital skills (data handling, formats, tool integration)
• AI literacy

Platform & working framework

• Self-study materials: short videos + notes + exercises; mandatory prompt log.
• Small team work: 3–4 students
• Biweekly meetings: sprint demo and feedback (30–45 min), action points for the next cycle.
• Submission formats: written (PDF), presentation (PPT/Canva), video (≤ 3 min), code/plan (if relevant), appendix: prompt & source log.
• Recommended tools: Discord,

AI knowledge to be delivered (embedded in projects)

• Trust calibration: when to trust AI, when to ask for evidence?
• Critical thinking: breaking down claims, fact-checking, counter-argument simulation.
• Ethical use & limitations: data handling, copyright, “what not to ask.”
• Prompt engineering: goal-context-constraint triad, roleplay prompts, chain-of-thought, self-check questions.
• Dependency management: how to remain the decision-maker, not the AI.
• Hallucinations: recognition, demand for evidence, triangulating sources.
• Lack of social connection: AI ≠ empathy; online communication etiquette.
• Risks & career orientation: job market trends, new roles (AI assistant, prompt specialist).
• Data security & privacy: personal data, sensitive content, anonymization.
• Scientific & source criticism: citations, quality criteria, biases.
• AI & manipulation: deepfake, bot networks, confirmation bias.
• Law & responsibility: copyright, citation, adaptation, liability limits.
• Bias: inherited dataset biases, mitigation.
• AI as a “partner” – risks of anthropomorphism: what’s human vs algorithmic?
• AI as a thinking mirror: metacognition, self-reflection questions.
• How AI shapes our thinking: cognitive outsourcing, decision biases.
• Virtual identity & self-image: digital footprint, reputation management.
• Time management: sprint planning, priorities, “definition of done.”

Language knowledge (embedded in projects)

• Exam topics and language competencies

Entry requirement:

B1 level (aiming for B2, C2 exam)

Assessment • Outcomes

• Weighting: content 30% • methodology/source criticism 25% • ethics/data security 15% • teamwork 15% • presentation 10% • deadlines 5%.
• Mandatory appendices: prompt log (with versions), source log (link + evaluation), data protection checklist, personal reflections (max. 1 page).
• Outcome: publishable mini-project (PDF/PPT/MP4) + reflective summary.