The Educator’s AI Playbook

A teacher’s guide to AI: lesson planning, personalized learning, assessment, and responsible use.

What you’ll learn
Build a personal prompt library that compounds in value over time
Build lesson plans, differentiated materials, and scaffolding supports using AI in a fraction of the time
Design assessments that AI cannot trivially complete
Introduce AI to your students deliberately and responsibly
Understand how large language models actually work
Design assessments that AI cannot trivially complete
Create a classroom AI policy
Support diverse learners
Automate recurring administrative tasks
Apply a consistent privacy framework across every AI use case in your practice

Requirements
No prior AI knowledge required — the course explains how AI tools work from the ground up, in plain language designed for educators rather than technologists
Access to a free AI tool — ChatGPT, Claude, Gemini, or any comparable large language model. A free account is sufficient for every lab and exercise in the course
A current teaching context — the labs and exercises are designed to be applied to real curriculum, real tasks, and real classroom situations. The course is significantly more valuable if you have a subject, grade level, and school context to work with while you learn

Description
AI is already in your classroom. Your students are using it — on assignments, for homework, in ways you can see and in ways you can’t. The question is no longer whether AI belongs in education. The question is whether you have a framework for navigating it with confidence.

This course gives you that framework.

Designed specifically for classroom teachers, Teaching in the Age of AI takes you from uncertainty to competence across eight practical modules. You’ll learn how AI tools actually work — not at a technical level, but at the level that matters for your practice: why they hallucinate, where bias enters their output, and how to develop the calibrated judgment to know when to trust what they produce.

You’ll use AI to save real time on the tasks that currently cost you the most — differentiated materials, scaffolding, formative assessment, parent communication, documentation, and report card comments — with a repeatable workflow that keeps your professional standards intact.

You’ll redesign assessments that AI can no longer trivially complete, build a classroom policy your students actually understand and follow, and develop a principled approach to personalized learning that protects student privacy and guards against the equity traps that AI-generated pathways can create.

And you’ll do all of this while building something more important than a set of techniques: a way of thinking about AI that will serve you as the tools keep changing.

No technical background required. Just bring your classroom, your subject, and your professional standards.

By the end of this course, you will know exactly which parts of your practice AI can support, which parts require your irreplaceable expertise, and how to build a sustainable, responsible, and genuinely useful AI practice — starting the week you finish.

Who this course is for
Classroom/Hybrid/Remote teachers at any level — primary through post-secondary — who are already aware that students are using AI and want a principled, practical framework for responding, rather than a blanket prohibition policy
Teachers who feel behind on AI and want to catch up quickly without wading through technical content written for developers — this course assumes no prior AI knowledge and builds understanding from the ground up
Educators who are time-poor and want to recover hours lost to administrative tasks, lesson preparation, and differentiation work — without sacrificing the professional quality of what they produce
Department heads, curriculum coordinators, and instructional coaches who need to guide colleagues through AI integration and want the conceptual frameworks and facilitation language to do that well
Teachers who care about equity — who want to use AI to raise the ceiling for every student rather than inadvertently track struggling learners into lower expectations through poorly designed personalisation