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AI for Teachers

Course Overview

Quick introduction 👋

Section titled “Quick introduction 👋”

This introductory course aims to provide teachers with the knowledge and practical skills to use AI not only as a personal productivity tool, but as a true ally for designing more inclusive, personalized, and engaging learning experiences for students. Through a practical and hands-on approach, we will explore the potential, mechanisms, and ethical implications of this technology, empowering educators to lead innovation in schools.

Course objectives

Section titled “Course objectives”

By the end of the program, teachers will be able to:

  • Understand how generative AI and large language models (LLMs) work at a high level.
  • Apply prompt engineering techniques to communicate effectively with AI and get targeted results.
  • Design learning units, activities, and projects (e.g., Project Based Learning) with AI support.
  • Create and use personalized assistants for specific teaching needs.
  • Generate multimedia teaching materials (maps, presentations, images, podcasts) using specialized AI tools.
  • Promote a critical, responsible, and ethical use of AI among students, recognizing opportunities and risks.

Methodology

Section titled “Methodology”

The course adopts a theory-to-practice approach. Each lesson is divided into two phases:

  1. Presentation phase: introduction to key concepts, tools, and operational strategies.
  2. Hands-on phase: teachers will work directly with AI platforms, practicing what they learned through guided exercises and collaborative activities.

Detailed course outline

Section titled “Detailed course outline”

Core module

Section titled “Core module”

Lesson 1: What is generative AI and how does it work?

Section titled “Lesson 1: What is generative AI and how does it work?”
  • Main topics:
    • introduction to generative AI.
    • essential components: large language models (LLM)
    • high-level understanding of Transformers
    • the concept of tokens
    • the probabilistic nature of AI
    • overview and comparison of the main platforms: Gemini, ChatGPT, Copilot, Claude, and others.

Lesson 2: Prompt Engineering principles for teaching

Section titled “Lesson 2: Prompt Engineering principles for teaching”
  • Main topics:
    • what is Prompt Engineering
    • anatomy of an effective prompt: role, context, task, output format.
    • basic and advanced techniques (e.g., few-shot, chain-of-thought)
    • prompt examples for lesson plans, exercises, and rubrics.

Lesson 3: Advanced prompting and instructional design

Section titled “Lesson 3: Advanced prompting and instructional design”
  • Main topics:
    • using AI as a partner in design (e.g., Project Based Learning, debate, role-playing).
    • creating and using personalized assistants (GPTs, Gems)
    • critical analysis of results: recognizing and managing errors, bias, and AI hallucinations.

Lesson 4: “AI that processes your content”: NotebookLM and beyond

Section titled “Lesson 4: “AI that processes your content”: NotebookLM and beyond”
  • Main topics:
    • introduction to source-grounded platforms like NotebookLM
    • core features: summarize, query documents, generate study guides, concept maps, FAQs, and glossaries from your own sources (PDFs, slides, transcripts)
    • creating chatbots that answer based on a predefined knowledge base.

Lesson 5: AI for creativity and inclusion

Section titled “Lesson 5: AI for creativity and inclusion”
  • Main topics:
    • overview of specialized tools for visual content: concept maps (e.g., Napkin, Chatmind, NotebookLM), presentations (e.g., Gamma, Canva), images (Nano-Banana, DALL-E 3, etc.) and videos (Veo 3, Sora, etc.)
    • focus on using AI as a compensatory tool and support for students with special educational needs.

Lesson 6: Responsible use, ethics, and safety of AI

Section titled “Lesson 6: Responsible use, ethics, and safety of AI”
  • Main topics:
    • the plagiarism dilemma: tools to detect AI-generated content and strategies to design “AI-proof” assignments
    • the most used apps by students
    • risks, bias, and ethical implications
    • EU regulation (AI Act) and school policies.

Optional deep-dive lessons

Section titled “Optional deep-dive lessons”

Lesson 7 (Optional - Humanities): How machines became smart without thinking like humans

Section titled “Lesson 7 (Optional - Humanities): How machines became smart without thinking like humans”
  • Main topics:
    • an overview of the impact of intelligent algorithms on people’s lives
    • the “shortcut” to build intelligent machines that don’t think deductively
    • social machines and recommendation systems: assistants or controllers?
    • “Attention Is All You Need”: the algorithm that unveiled the secret of knowledge.
    • emergent properties in LLMs when their size exceeds a critical threshold
    • beyond the limits of our knowledge: how will we manage machines smarter than us?

Lesson 8 (Optional - STEM): “Vibe Coding” for the classroom

Section titled “Lesson 8 (Optional - STEM): “Vibe Coding” for the classroom”
  • Main topics:
    • using AI to generate working code without knowing how to program
    • exporting and sharing LLM outputs
    • building interactive simulations (e.g., mass-spring system, pendulum, molecules)
    • dynamic charts from data (e.g., spreadsheets)
    • interactive quizzes and simple educational games.