Build the knowledge, routines, and judgement to teach AI-ready thinking across the curriculum. Follow the complete pathway, or use a verified class report to choose a focused starting point.
Open a module and mark a lesson complete when you have reflected and chosen a classroom action.
No verified class report is selected, so this route covers the essential foundations for every teacher.
Establish the core teaching language and an evidence-informed improvement cycle.
Build an accurate model of AI, training data, generation, and verification.
Teach privacy, fairness, human oversight, and meaningful challenge.
Each lesson moves professional learning into classroom practice rather than leaving it as background reading.
Build a shared language for the six capabilities, interpret assessment evidence responsibly, and plan an inclusive learning sequence.
By the end: Leave with a focused four-week plan that connects assessment evidence to everyday classroom practice.
Help pupils select important features, move between representations, and test models against reality.
By the end: Teach pupils to create useful models while recognising what every model leaves out.
Move pupils from noticing regularity to describing, testing, and qualifying a defensible pattern.
By the end: Build routines that distinguish meaningful signal, coincidence, and exception.
Teach pupils to define a complex goal, create manageable parts, coordinate dependencies, and recombine the work.
By the end: Use decomposition as a planning and collaboration strategy—not simply a longer to-do list.
Develop precise procedures, systematic tracing, debugging habits, and informed choices about efficiency.
By the end: Enable pupils to design and improve reliable processes in digital and non-digital contexts.
Demystify models, training data, prediction, generative systems, and human oversight without requiring code.
By the end: Give pupils an accurate, age-appropriate mental model for what contemporary AI can and cannot do.
Build practical judgement about data quality, bias, privacy, transparency, contestability, and human impact.
By the end: Help pupils evaluate AI-supported decisions from the perspective of people affected—not only technical performance.