Tag: SAMR model

Walk into any staffroom right now and AI is in the conversation. Some teachers are experimenting enthusiastically, others are cautious, and most are quietly trying to work out how to respond without losing what makes their teaching meaningful.

In a recent Faculty Focus article — Humanizing Generative AI: Three Ways to Keep Students at the Center of Your Classroom — Dr Richard Violanti names a pattern that should sit at the heart of every AI-in-education conversation we are having this year. Teachers are using generative AI to draft emails, build slides, summarise readings and generate quizzes. All useful. All versions of what we already did. As Violanti puts it, the technology “enhances the product … but leaves the process of learning for the students unchanged.”

That diagnosis deserves our attention. If our ambition stops at efficiency, we will have adopted the most powerful learning technology in a generation and used it to make a slightly faster worksheet.

This post is a follow-up to Violanti’s piece. The aim here is to take his core argument — that AI’s real promise is pedagogical, not administrative — and sit it alongside two of the most widely used frameworks in educational technology: the SAMR model (Puentedura, 2006) and the TPACK framework (Koehler & Mishra, 2009). Together, they give us a sharper way to see when AI is genuinely transforming learning, and when it is quietly substituting for what was already there.

The efficiency trap, named

Violanti’s observation is not new, but it is important. Decades of educational technology research — from Larry Cuban onwards — show that most classroom innovations end up sustaining existing practice rather than transforming it. Interactive whiteboards replaced chalkboards. PDFs replaced photocopies. Learning management systems replaced filing cabinets. Each was a genuine workflow improvement. None, on its own, changed what learning felt like for students.

Generative AI is arriving with the same gravitational pull. The path of least resistance is to wire it into what we are already doing: a faster quiz generator, a tidier email reply, a cleaner slide. These uses are not bad — they give time back. But if that is where we stop, AI becomes another tool that makes the teacher’s life marginally easier without making the student’s learning meaningfully different.

The question Violanti poses is the right one, and it links directly to the kind of intentional planning we have talked about in earlier posts on planning with purpose: not how can I use AI?, but how can AI help me create a transformative, student-centred learning experience?

SAMR: a mirror for how we are actually using AI

Ruben Puentedura’s SAMR model offers four levels of technology integration: Substitution, Augmentation, Modification and Redefinition. The lower two levels enhance existing tasks. The upper two transform them — they make possible learning experiences that simply could not exist without the technology.

Mapped onto generative AI, the levels become uncomfortably clear.

None of this requires teachers to become SAMR theorists. The value of the model is its reflective prompt, which Violanti phrases neatly: is technology making learning different, or just more efficient? If the answer is “just more efficient,” we are at the bottom of the ladder. That is a reasonable place to start. It is not a reasonable place to stay.

It is worth being honest about SAMR’s limits too. Critics have pointed out, fairly, that the model can privilege product over process and can imply Redefinition is always pedagogically superior, which it is not. A well-designed Substitution task can teach more than a poorly-designed Redefinition one. SAMR is a mirror, not a scoreboard.

TPACK: why the mirror alone is not enough

If SAMR asks where on the ladder are we?, the TPACK framework asks what do we need to know to climb it well? Koehler and Mishra’s model describes teaching as the intersection of three knowledge domains: Content Knowledge (what we teach), Pedagogical Knowledge (how we teach) and Technological Knowledge (the tools we use). Transformative teaching lives at the centre of the Venn diagram, where all three overlap.

This matters enormously for AI, because AI is the first mainstream classroom technology where the Content and Pedagogy circles are genuinely under pressure from the Technology circle. A generative model can plausibly do some of what we used to teach students to do — write an essay, summarise an argument, solve a routine problem. TPACK reminds us the answer is not to retreat into one circle or another. It is to redesign the intersection.

When those three questions are asked in that order, AI stops being the starting point and becomes a design choice. That is TPACK doing its quiet, useful work.

What transformation actually looks like

Violanti offers three concrete practices that, in SAMR terms, push teaching toward Modification and Redefinition, and in TPACK terms sit firmly inside the intersection of all three circles. They are worth restating — and building on.

1. Differentiate, don’t just generate. Instead of producing one more artefact for the whole class, use AI to produce multiple on-ramps to the same content: a levelled version of a dense reading, an audio summary, a concept map, a worked example with scaffolding, a stretch version with primary sources. Every student engages with the same learning goal, but through the modality and entry point that works for them. This is differentiation at a scale that was genuinely impossible a few years ago, and it sits squarely in Redefinition territory.

2. Turn AI into a thinking partner, not an answer machine. The concern that AI short-circuits thinking is legitimate, but it is a design problem, not a technology problem. Assignments that ask students to produce with AI tend to flatten thinking. Assignments that ask students to interrogate AI tend to deepen it. Have students debate an AI-generated argument, diagnose where a model oversimplified a concept, compare AI output against primary sources, or co-develop a solution and then annotate which contributions were theirs and which were the model’s. This connects directly to the kind of higher-order questioning we have explored in earlier posts — the critical move is requiring students to evaluate AI, not just consume it. Metacognition becomes the assessable product.

3. Use AI to design, not just to deliver. This is the move most teachers have not yet made. Ask AI to help you generate learning experiences at different cognitive levels, propose authentic problem scenarios linked to your subject, suggest formative assessments aligned to specific outcomes, or role-play a sceptical student and stress-test your explanations. The teacher stays the designer. The AI becomes a collaborator in the design process itself. This is perhaps the highest-leverage shift available right now — and the one most hidden from view, because it happens before the lesson even starts. It pairs particularly well with what we have written previously about formative assessment: AI can help generate the next layer of probing questions after you see how students responded to the first.

A few honest caveats

A responsible follow-up has to acknowledge what this kind of transformation does not solve. Designing transformative AI tasks takes more time up front, not less — the efficiency trap is seductive partly because it is genuinely easier. It also assumes equitable student access to tools, clear school guidance on academic integrity, and professional learning that treats AI as a pedagogical question rather than a software training session. None of that is evenly distributed. And the technology itself is not neutral: generative models carry bias, hallucinate confidently, and raise real questions about labour, intellectual property and environmental cost that belong in any serious classroom conversation about their use.

Holding those caveats in mind is not a reason to retreat to Substitution. It is a reason to make the climb to Redefinition deliberately, transparently, and with students as partners in the conversation.

The more human use of artificial intelligence

The irony of generative AI in education is that its most powerful contribution may be the thing it cannot do. It cannot know a student. It cannot notice the one who has gone quiet in week six. It cannot decide what in a subject is worth knowing. It cannot replace the trust that makes feedback land.

What it can do — if we let SAMR and TPACK do their reflective work — is give that time back to us. Time to design richer tasks. Time to respond to individual students. Time to do the pedagogical work that most drew us to teaching in the first place.

If it is, we have climbed the ladder. If it is not, we have simply bought a faster worksheet.