SELF-PACED PROFESSIONAL LEARNING
The Course
Free, self-paced modules to cultivate your pedagogical agency and tend a thriving digital classroom ecosystem. Plant the seeds of AI basics—how models learn, work, and sprout in daily tools—before branching into critical realities like data privacy, ethical guardrails, and Indigenous data sovereignty. From root-level safety to cross-curricular growth, gain the homegrown confidence to nurture student AI literacy and lead ethical adoption in your learning community.
Chapter 1 What is AI?
The foundations: what artificial intelligence actually is, how data, computing power and infrastructure make it work, how generative and agentic systems differ, and why AI literacy matters for educators and their students.
TopicsTasks
- I. Defining Core Concepts0/4
- II. Inside the AI Engine0/5
- III. From Chatbots to Agents0/5
- IV. So What for Classrooms?0/3
Chapter 2 How AI Learns & Makes Decisions
How a model is built and how it behaves: the data, patterns and training behind it, the machine learning ideas that make it work, and how to recognise the models already running in everyday software, search and classroom technology.
Topics
- I. Meet the Model: Data, Patterns and Training
- II. Machine Learning Basics
- III. Spotting AI Models in Daily Software, Search, and Classroom Technologies
Chapter 3 Risks and Realities
The harms to weigh before adopting a tool: ethical risk, the cost of cognitive offloading, Indigenous data sovereignty, student privacy, and where AI use meets academic integrity and copyright.
Topics
- I. Navigating AI’s Ethical Risks
- II. Impact of Cognitive Offloading
- III. Indigenous Data Sovereignty & Ethical Guardrails
- IV. Data Privacy and Student Protection
- V. Academic Integrity and Copyright
Chapter 4 Daily Practice & Classroom Integration
AI in the working week: lightening administrative load, differentiating learning and adapting instruction, and designing learning experiences with AI rather than around it.
Topics
- I. Supporting Teacher Workflow & Administrative Tasks
- II. Differentiating Learning and Adapting Instruction
- III. Designing Learning with AI
Chapter 5 Student AI Literacy & Future Readiness
Preparing students for what comes next: building AI literacy across grade levels, the career pathways opening in a changing digital workforce, and how this connects to Canada’s Skills for Success.
Topics
- I. Fostering Student AI Literacy Across Grade Levels
- II. Career Pathways and the Evolving Digital Workforce
- III. Canada Skills for Success
Chapter 6 Professional Leadership and Agency
Leadership beyond the classroom: Canada’s own AI story, the international competency frameworks shaping practice, and the habits that keep educators adaptable as the technology changes.
Topics
- I. Our Home-Grown AI Story
- II. Understanding International AI Competency Frameworks
- III. Developing Lifelong Adaptability for an Evolving Tech Ecosystem