Teaching AI Literacy: The Curriculum Decisions We Made — and Why

People ask us sometimes what makes a SAIL course different. The honest answer is that the difference is not primarily in the tools we use — although we have spent an enormous amount of effort developing those tools — it is in the decisions we made before we chose to use any third-party tools, or what to build into our own. It is the strategy behind the tools that matters.

We start with the end

The single most important influence on how SAIL (Scholastic Artificial Intelligence Learning) structures its curriculum is backward design, developed by Grant Wiggins and Jay McTighe in Understanding by Design. Before deciding what to teach or how, we define what a student should be able to do when finished. Three observable capabilities: direct AI purposefully, evaluate AI critically, and engage with AI ethically. Everything else flows from those three.

We treat AI as a subject, not a setting

We teach about AI before teaching with it. This is grounded in the SAMR framework developed by education researcher Ruben Puentedura. The vast majority of AI tool use in schools today sits at Substitution. SAIL is designed to operate at Modification and Redefinition — learning experiences that would not be possible without AI.

We build in ethical reasoning from day one

Ethics is not a module at the end. It is present in how every task is framed, drawing on Paulo Freire’s tradition of critical pedagogy. Applied to AI, this means students who can ask: who built this system, on what data, with what assumptions? Terry builds this into task framing from the first session.

We selected tools last — including our own

Once outcomes, pedagogy, and ethics were defined, we chose the tools. Every feature in our own software serves a specific learning or teaching outcome that was already defined. The tool follows the strategy. Always. The result is a curriculum more demanding than most AI education programmes — and more honest about what it is trying to achieve.

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