Work in teams of 2–3 students. This is an investigation: you search, read, and use AI tools to understand (not to copy). No answers are given here.

Before you start

  • You manage your time and divide tasks as you want.
  • No “AI magic”: every claim must be tied to a mechanism (even explained simply).
  • Use AI like a tutor: ask it to explain, quiz you, challenge you, and reformulate—then you write in your own words.
  • Attention mechanism is mandatory: you must explain it and connect it to marketing “attention”.
  • Support your key claims with at least one credible source (paper, textbook chapter, reputable technical explainer).
  • Examples must cover all three sectors: Tourism + Hospitality + Food Service.

1) Define Generative AI

Write your definition in your own words. Keep it short and precise. You must include:

  1. The core function: what is generated (text/images/audio/code) and what it is generated from (learned patterns).
  2. What the output is: an output produced by a probability model (not automatically a verified fact).
  3. What it is NOT: list two common misunderstandings you want to avoid.
Investigation task: find one credible definition of “generative AI” and cite it. Then rewrite it for a manager (simple wording, no jargon).
Examples requirement: add one concrete example sentence for each sector: Tourism / Hospitality / Food Service. (No need to be long—just show you can translate the definition into reality.)

2) Explain the main algorithms (focus: attention)

Explain how a modern text generator works at a manager level, but with correct technical logic. You must explicitly cover the attention mechanism. Keep it clear: no math, no code.

Required questions (you answer them — no copying)

  1. Text → tokens: What is tokenization? Why does it matter for style, errors, and cost?
  2. Next-token prediction: What does “predicting the next token” mean? Why can this produce fluent but wrong text?
  3. Attention (mandatory): In your own words, what does “attention” do inside a transformer? What does it mean to weigh different parts of the input?
  4. Training vs inference: What is learned during training? What happens at inference time when you prompt the model?
  5. Hallucinations: Give two algorithmic reasons they happen (not “AI is bad”).

3) Identify Generative AI tools (capabilities table — not use cases)

Fill this table with capabilities and “fit” only. Do not write detailed use cases here (that’s section 4). You may add tools, but keep at least four.

Tool Main features (capabilities) Tourism (fit) Hospitality (fit) Food Service (fit)
ChatGPT        
Claude        
Gemini        
Microsoft Copilot        
Perplexity        
NotebookLM        
Mistral        
Canva (Magic Studio)        
Mini-task: choose one tool and write a 6-line “capabilities checklist”. Then list 3 risks if you use it for marketing content (accuracy, legal/compliance, brand voice, bias, etc.).

4) Propose possible uses (2–3 only)

Propose two or three realistic uses of Generative AI. Your examples must cover Tourism, Hospitality, and Food Service (at least one example per sector).

Use-case card (copy/paste ×2 or ×3)

  • Sector: Tourism / Hospitality / Food Service
  • Use case title: ______________________________________________
  • Marketing objective: __________________________________________
  • What is generated: ___________________________________________
  • Inputs you provide (context + constraints): _______________________
  • Quality control (how you reduce hallucinations): __________________
  • Limits & risks: _______________________________________________
  • What must remain human: _____________________________________
Attention requirement: at least one use case must explicitly discuss how generative content could capture or waste human attention—and why that matters.

5) Reflective synthesis (short, critical)

Write 10–12 lines answering the questions below. Be specific. Be critical.

  1. Value vs hype: where is the real value of generative AI across the three sectors?
  2. Main risk: what is the biggest risk (and why)?
  3. Accountability: who is responsible if generated content is wrong or harmful?
  4. Minimum safeguards: list 3 safeguards you would impose (review, sources, tone guide, forbidden claims, etc.).
  5. Attention link: how does generative AI change the way attention is captured, guided, or exploited?
Reminder: the written final exam questions will be built from students’ submissions. Anything you write can become an exam question.

MINI BIBLIOGRAPHY — Generative AI (Attention / Transformers)

  1. Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention Is All You Need. arXiv:1706.03762. arXiv | PDF
  2. Alammar, J. (2018). The Illustrated Transformer. Link
  3. Stanford CS224N. Self-Attention & Transformers (notes). PDF
  4. Stanford CS224N. Transformers (slides). PDF
  5. NIST CSRC Glossary. Generative Artificial Intelligence. Link
  6. NIST (2024). Artificial Intelligence Risk Management Framework (AI RMF 1.0). PDF