Learning Outcomes

Students will be able to:

KNOWLEDGE & UNDERSTANDING:

• Define what Artificial Intelligence is and articulate basic concepts of how it learns.
• Identify and explain key AI learning paradigms (e.g., reinforcement learning, supervised learning, LLMs).
• Recognize various types of AI and match them to appropriate real-world problems.

SKILLS & ABILITIES:

• Develop critical thinking skills to differentiate between human and AI-generated content.
• Apply understanding of AI to design conceptual solutions for sustainability challenges.
• Work collaboratively to brainstorm and present ideas.

ATTITUDES & VALUES:

• Understand that AI is a tool for innovation and problem-solving.
• Reflect on ethical considerations such as privacy, inclusiveness, and transparency.

European Dimension / Erasmus+ Connection

• Digital Transformation: Equipping students with essential digital competences.
• Sustainability: Focusing on AI solutions for ecological and environmental problems.
• Ethics & Inclusion: Promoting responsible AI use and understanding biases.

1. Resources and Tools

Digital Tools:

• Simulations: Teachable Machine (Google), Quick, Draw!.
• Generative AI: ChatGPT, Gemini, or Copilot (for testing prompts).
• Presentation: Padlet or PowerPoint.

Materials:

• Computers/Tablets with internet access.
• Projector/Smartboard.
• Matching Game Cards (physical or digital).

Useful Links:

• FAO
• Stanford HAI AI Index
• MIT Technology Review: AI
• NIH
• Microsoft AI for Earth
• EU Digital Strategy
• Introductory video on how AI learns

Classroom Support Materials:

• Use a research worksheet, a task checklist, and a visible timer during the matching and design activities.
• Prepare peer-evaluation or Google Forms materials plus introductory slides or a short PPT for the opening explanation.

2. Working Methods

• Interactive Lecture: Engaging introduction to core concepts.
• Hands-on Simulation: Directly training models to understand "learning."
• Collaborative Problem Solving: Group work to design AI solutions.
• Inquiry-Based Learning: Exploring real-world case studies.

Activity Overview

3. Introduction and Motivation

What is AI?

Goal: Define AI and dispel myths.

• Hook: Show images generated by AI vs. Human art. Can they tell the difference?
• Definition: AI is not magic; it is math and data. It mimics human cognition (learning and problem-solving).
• Comparison: Discuss: "What is easy for a human but hard for a robot?" (e.g., tying shoelaces vs. calculating pi).

4. Research and Learning

Activity 1: The "Black Box" of Machine Learning

Goal: Demystify how AI learns.

• Teachable Machine: Students train a simple model to recognize gestures (e.g., "Thumbs Up" vs. "Thumbs Down") or sounds.
• Quick, Draw!: Students play the game to see how a neural network recognizes doodles.
• Discussion: "Did the AI get smarter as you played? Why?" (Data quantity and patterns).

Activity 2: AI in the Real World

Goal: Connect theory to practice.

• Matching Game: Students match "AI Types" (LLMs, Computer Vision, Recommender Systems) to "Problems" (Writing emails, Detecting forest fires, Suggesting movies).
• Case Studies: Briefly discuss how AI helps in healthcare (diagnosing X-rays) or environment (tracking wildlife).

5. Creative Application

Activity 3: Solve a Problem with AI

Challenge: Identify a problem (e.g., too much plastic waste) and design an AI solution.

• Concept: "We will use Computer Vision to identify plastic types on a conveyor belt."
• Ethical Check: "Is our data biased? Is it safe?"
• Output: A short pitch or poster describing the solution.

6. Reflection and Evaluation

Final Thoughts

• Self-Assessment: Students complete the rubric (Att 4.1).
• Discussion: "How will AI impact your future job?" "Do we trust AI for everything?"
• Key Takeaway: AI is a powerful tool, but it requires human oversight and ethical responsibility.