5 Moves That Stop Students Taking the Easy Way Out

 Pedagogy · Cognitive Engagement

Why Your Classroom Might Be Teaching Students to Switch Off

The problem isn’t that students resist thinking. It’s that we’ve made not thinking the easier option.

⏱ 6 min read · By Matt Faulkner · Pedagogy / Cognitive Engagement

The average time a person spends on any single screen before switching has dropped from two and a half minutes in 2004 to around 47 seconds today. That is not a phone problem. That is a habit problem — and schools have been quietly building it for decades.

Last week’s post on productive struggle made the case that genuine learning requires cognitive effort — and that effort is often uncomfortable. That said, naming productive struggle as a goal is the easy part. The harder question is: what force is working against it? Why do students so reliably reach for the path of least cognitive resistance, even when they are sitting in a room designed for learning?

The answer isn’t laziness. In most cases, it isn’t even a lack of motivation. It’s something more structural — and more fixable.

What Is the Path of Least Cognitive Resistance?

The brain is an energy-conservation machine. Left to its own devices, it defaults to the lowest-cost route available. Psychologists call this System 1 thinking — fast, automatic, low-effort. System 2, the slower and more deliberate mode, is what we need for analysis, problem-solving, and genuine learning. The catch is that System 2 is expensive. The brain only commits to it when it has to.

This is not a design flaw. In most areas of life, conserving cognitive energy is sensible. The problem arises when classroom environments are structured in ways that make System 1 sufficient. In other words, when students can get through a lesson — answer the questions, fill the worksheet, satisfy the teacher — without ever really thinking.

The OECD defines cognitive engagement as the mental state where learners exert sufficient, sustained effort to persist with a complex idea or unstructured problem. That definition is worth holding onto. It is specifically about effort and persistence. Not compliance, not busyness, and not the appearance of working. By that definition, a class full of students who are on task may still not be cognitively engaged at all.

A student checking Instagram to survive a pointless lesson is different from a student checking Instagram during a lesson they find meaningful. We tend to treat these as the same problem. They are not.

How Classrooms Create the Problem

Here is the uncomfortable part. Distraction is not just something that happens to learners from the outside. It is also something that learning environments create from the inside.

Compliance-based classrooms — built around predictable tasks, scripted routines, and curricula disconnected from students’ questions — don’t simply fail to engage learners. Over time, they train students to zone out. To wait for class to end. To treat thinking as optional. The path of least cognitive resistance becomes the default setting. Not because students chose it, but because the environment made it easy.

That said, this is different from the phone debate. Removing distractions from the outside — banning devices, adjusting seating — addresses one half of the problem. The other half is internal: the structure of the learning experience itself. A student who finds a lesson genuinely purposeful will resist external distraction far better than one simply going through the motions in a phone-free room.

In practice, this means the question isn’t only “how do I keep students on task?” It is “what is the task asking of them, cognitively?” Those are different questions. Only the second one changes the default.

The Environment Angle: Make Thinking the Easier Choice

Research from UCL suggests that human decisions are shaped not just by deliberate reasoning but by the friction profile of available choices — how easy or effortful each option feels. The implication for classrooms is direct. If passive compliance is frictionless and active thinking requires effort, most students will choose compliance. As a result, the fix isn’t only to motivate students to think harder. It is to reduce the friction on thinking and raise the friction on switching off.

In other words, that’s a design problem — and it has design solutions.

Reducing Friction on Thinking

🏗️ Environment Moves That Shift the Default
  • Change the seating expectation. Rows facing the front make passive reception easy. Clusters or paired arrangements make conversation the default. Neither is always right, but consider what behaviour each layout makes effortless.
  • Build in talk as a structural requirement, not a reward. When discussion is positioned as “time to chat before we get back to work,” students read it as a break. When it is built into the task — “you need a partner’s view to complete this” — thinking becomes the easier path.
  • Use visible thinking tools. Mini whiteboards, sticky notes, and shared documents make thinking visible and public. That slight social pressure — knowing others can see your ideas — raises the cost of switching off.
  • Vary the routine intentionally. Around 45% of everyday behaviours are repeated in the same location without deliberate thought. When every lesson follows the exact same structure, the brain learns to run on autopilot. Small, deliberate variation — a different entry task, a different question format — keeps System 2 online a little longer.

The Planning Angle: Build Demand Into the Task

Environment changes help, but they are only scaffolding. The more powerful lever is the task itself. Even so, a thoughtfully designed environment cannot compensate for a task that requires no thinking.

For that reason, the key question at the planning stage — before you write the lesson, not after — is: at what point in this task does a student have to think? If the answer is “never, really,” the task needs redesigning, not better classroom management. This is exactly the territory that backward design addresses. Start from the learning intention and work backwards to a task that genuinely demands it.

Beyond that, the type of thinking matters. Tasks that ask students to recall, copy, or follow a procedure leave System 2 largely unused. By contrast, tasks that require students to evaluate, connect, justify, or construct something novel pull System 2 into the work. That is the core of what we covered in our post on higher-order questioning — and it applies equally to tasks, not just questions.

Task Design Checks Before You Teach

✏️ Four Planning Questions to Raise Cognitive Demand
  • Can a student complete this without thinking? If the answer is yes — if recall, copying, or pattern-matching is sufficient — redesign the endpoint. Add a “why” or “how do you know” requirement.
  • Does the task have a low floor and a high ceiling? Every student should be able to begin. Not every student should be able to finish quickly. Open-ended tasks with multiple entry points make switching off less tempting because there is always somewhere to go.
  • Where is the decision point? The moment a student must choose — between interpretations, approaches, or ideas — is the moment cognitive effort is unavoidable. Plan at least one genuine decision into every task.
  • Does the task connect to something students actually care about? Relevance reduces the pull of least resistance. Students will do more cognitive work on a problem they find worth solving.

Making Thinking the Low-Resistance Option

The goal is not to make classrooms cognitively exhausting. Students need to build capacity gradually — and they need to feel the success of thinking hard and getting somewhere. That is exactly the territory covered in the productive struggle post: challenge needs to sit within reach.

Even so, the framing shift matters. For most of us, the instinct is to fight the path of least resistance. To push students to try harder, engage more, focus better. That framing puts all the responsibility on the student. A more productive question is: which path are we making easy? In a well-designed classroom, the low-resistance option and the thinking option are, as often as possible, the same thing.

That doesn’t happen by accident. It happens in the planning stage and in the physical arrangement of the room. It happens in the habits we build — deliberately — from the first weeks of the year. Which is, conveniently, what the next series of posts is going to look at.

What does the path of least resistance look like in your classroom? Is it a particular task type, a time of day, or a room setup that makes switching off too easy? Share what you’ve noticed — and what you’ve tried — in the comments below.

References and Further Reading

Juliani, A. J. (2025). We have a distraction problem. But we’ve been solving the wrong half of it. ajjuliani.com. ajjuliani.com

OECD. (2025). Ensuring cognitive engagement: Unlocking high-quality teaching. OECD Publishing. oecd.org

University College London. (2017, February 21). Humans are hard-wired to follow the path of least resistance. ScienceDaily. sciencedaily.com

Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.

5 Powerful Moves That Will Actually Make Students Struggle

Week 15 · Pedagogy · Task Design

5 Task Design Moves That Actually Stretch Every Student

Productive struggle isn’t what happens when a task is too hard. It’s what happens when you’ve designed it just right.
⏱ 6 min read · By Matt Faulkner · Pedagogy / New Teachers

There is a particular kind of classroom frustration that has nothing to do with behaviour. It is the lesson where students race through the task, and you realise about twelve minutes in that it asked nothing of them. Or the opposite: where you pitched it harder and now three hands are up, six students are staring at the ceiling, and two have quietly written their name and stopped.

Both of those outcomes have the same root cause. The task was not designed with stretch in mind. It was designed with content in mind — and there is a difference.

Last week, we looked at how backward design changes what you plan for. This post takes the next step. Once you know where students need to get to, the question becomes: how do you design the task that gets them there — one that genuinely challenges without tipping into frustration? The answer has a name in the research literature: productive struggle. And it can be deliberately built in.

What “Just Beyond Comfortable” Actually Means

Lev Vygotsky described the zone of proximal development (ZPD) as the space between what a learner can do independently and what they can do with support. Tasks that sit below the ZPD require no new thinking. Tasks that sit too far above it produce confusion and disengagement. Tasks that sit right at the edge of the ZPD — just beyond what a student can currently do alone — are where genuine learning occurs.

John Sweller’s cognitive load theory adds important precision here. Working memory is limited. When a task overloads it — too many new elements at once, too much unfamiliar vocabulary, too many simultaneous steps — students cannot process the content, let alone learn from it. In other words, difficulty alone is not the goal. Productive difficulty is. The task needs to be complex enough to require real thinking, but structured enough that thinking is actually possible.

For teachers, that distinction is everything. A task is not automatically rigorous because it is hard. It is rigorous when it demands higher-order thinking within a structure students can manage. As a result, designing for productive struggle is less about making tasks harder and more about making them smarter.

The Mindset Piece: Designing for Effort, Not Just Difficulty

Carol Dweck’s research on growth mindset is relevant here — but not in the way it is usually applied. It is not enough to tell students that struggle is good for them. If the task design sends the opposite message — if there is only one right answer, if being wrong feels like failure, if speed is implicitly rewarded — then the growth mindset poster on the wall is just decoration.

Dweck’s central finding is that students who believe intelligence is fixed avoid challenge to protect their self-image. By contrast, students who believe ability grows through effort seek out challenge. The task itself can reinforce either belief. A task with one quick answer rewards speed and confidence. A task that requires iteration, justification, and revision sends the message that effort is the point.

That said, this is not about lowering the stakes or removing rigour. It is about designing tasks where being stuck is a legitimate part of the process — and where the structure supports students to push through, rather than wait for the teacher to rescue them.

There is a timely corollary here for AI use in classrooms. Cognitive scientist Paul Kirschner draws a useful distinction between cognitive offloading and cognitive outsourcing. Offloading — writing notes, drawing a diagram, working through steps on paper — frees up working memory while keeping the student as the thinker. Outsourcing — asking AI to explain, summarise, or produce — transfers the thinking itself to an external agent. The student receives a finished product. Nothing is encoded in long-term memory. For teachers designing tasks with productive struggle in mind, the question is worth asking: does the way students are using AI protect generation, or remove it entirely?

5 Design Moves That Build Productive Struggle In

These moves can be applied individually or in combination. They work across year levels and learning areas. Even one move applied consistently will shift what students experience in your classroom.

🎯 5 Task Design Moves for Productive Struggle
  • Move 1 — Open the entry point. Design tasks with a low floor and a high ceiling. Every student can begin; not every student will reach the same endpoint. This removes the paralysis of “I don’t know where to start” while keeping stretch available for those who are ready for it. Jo Boaler’s work on low-floor/high-ceiling tasks in mathematics is the clearest articulation of this principle in practice — and her YouCubed site has free task banks worth bookmarking.
  • Move 2 — Withhold the scaffold before the answer. Give students the problem before you give them the method. Even five minutes of initial thinking — before any explicit teaching — activates prior knowledge, surfaces misconceptions, and means the instruction that follows has somewhere to land. This is not about leaving students stranded. It is about priming the brain to receive new learning. There is a memory research term for what happens here: the Generation Effect. When students generate an answer — retrieve a word, attempt a problem, construct an explanation — they remember it significantly better than when the same information is simply presented to them. The act of production is itself the act of learning. Jamie Clark’s DistillED newsletter on the Generation Effect is worth reading alongside this post — it is one of the clearest explanations of the research and its classroom implications available right now.
  • Move 3 — Limit the information given upfront. Real-world problems rarely arrive with all the relevant information pre-sorted. Give students a question and some — not all — of the information they need. Require them to identify what’s missing or what they’d need to know. That act of identifying gaps is itself higher-order thinking.
  • Move 4 — Require justification, not just answers. “What is the answer?” asks for recall. “How do you know?” asks for thinking. Build justification into the task structure — not as an add-on, but as the core requirement. A student who can get the right answer but cannot explain it has not yet learned what you think they’ve learned.
  • Move 5 — Build in a “stuck” protocol. Before students begin, name what to do when they get stuck. Three specific options: re-read the task, try a different starting point, or discuss with a partner using a sentence starter (“I think the problem is asking me to…”). This removes the habit of raising a hand the moment something feels difficult — and it signals that being stuck is expected, not a sign of failure.

What This Looks Like in HASS: Year 5 Source Analysis

A standard source analysis task gives students a primary source and asks them to identify the author, date, and one key message. That is a recall task. It sits well below the ZPD for most Year 5 students who have been doing this since Year 3.

A productive struggle version applies Moves 1, 3, and 4. Students receive two short sources on colonial settlement — a settler’s diary entry and an account from an Aboriginal perspective — with no guiding questions. The task is simply: what story are these two sources telling, and where do they disagree? From there, students must write one claim supported by evidence from both sources.

The open entry point (Move 1) means every student can begin with an observation. Limiting the guiding questions (Move 3) means students must decide what matters. Requiring a supported claim (Move 4) means the answer is not retrievable without genuine analysis. The teacher’s role shifts from answer-giver to question-asker — circulating and probing with higher-order questions that push thinking further rather than narrowing it toward a predetermined response.

For more on structuring formative checks around this kind of task, the post on formative assessment strategies covers exit ticket design in detail.

What This Looks Like in Maths: Year 3 Multiplication

A standard multiplication task: Solve 3 × 4. Solve 5 × 6. Solve 7 × 8. Students either know these or they don’t. There is no thinking available beyond retrieval — and no entry point for students who aren’t yet fluent.

A productive struggle version uses Move 1 and Move 2. The teacher displays 24 counters on the interactive whiteboard and asks: how many different ways can you arrange these into equal rows? No method has been taught yet. Students work with their own counters, recording each arrangement. From there, the class shares findings — and the teacher introduces the array and the number sentence as the language for what students already discovered.

The low floor means students working at different points of readiness can all begin. The high ceiling means a student who finds three arrangements quickly is asked: how do you know you’ve found them all? That follow-up question — requiring justification and systematic thinking — is where the stretch lives. In practice, it produces more mathematical reasoning than a worksheet of 20 isolated facts ever will.

Beyond that, notice what the task design does to the explicit teaching that follows. Because students have already struggled with the concept, the instruction has a problem to solve. It lands differently than instruction delivered cold.

Where the Gradual Release Fits

Fisher and Frey’s Gradual Release of Responsibility model — I do, We do, You do — is one of the most widely used frameworks in Australian primary schools. It is also frequently misapplied in ways that accidentally prevent productive struggle.

The problem arises when the “I do” phase is so thorough that the “You do” phase requires nothing beyond imitation. Students are not struggling — they are copying. The GRR is designed to scaffold students into independence, not to eliminate the experience of not-yet-knowing.

Used well, the GRR creates the conditions for productive struggle at the “You do” stage by deliberately withdrawing support at the right moment. The key is that the “We do” phase builds enough shared understanding that students can tolerate uncertainty — not so much that the independent task becomes a rehearsal of what they’ve already watched you do.

That calibration — knowing when to hold the scaffold and when to pull it back — is one of the most important professional judgements in teaching. Even so, it is a judgement that gets easier with practice. And it starts with task design that has stretch built in from the beginning.

Design the task for the thinking you want, not just the answer you need. Students rise to what the task demands of them — and rarely higher.

💬 Over to You — Which of the five moves are you already using, and which one are you going to try first? Or if you’ve got a task that created genuinely productive struggle in your classroom, share it below. The best ideas on this site always come from teachers who’ve already done the hard yards.

References

Bertsch, S., Pesta, B. J., Wiscott, R., & McDaniel, M. A. (2007). The generation effect: A meta-analytic review. Memory & Cognition, 35(2), 201–210. https://doi.org/10.3758/BF03193441

Bjork, E. L., & Bjork, R. A. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In M. A. Gernsbacher, R. W. Pew, L. M. Hough, & J. R. Pomerantz (Eds.), Psychology and the real world: Essays illustrating fundamental contributions to society (pp. 56–64). Worth Publishers.

Boaler, J. (2016). Mathematical mindsets: Unleashing students’ potential through creative math, inspiring messages and innovative teaching. Jossey-Bass.

Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.

Fisher, D., & Frey, N. (2014). Better learning through structured teaching: A framework for the gradual release of responsibility (2nd ed.). ASCD.

Kirschner, P. A. (2026). Offloading? No outsourcing! Retrieved from https://paulkirschner.com

Slamecka, N. J., & Graf, P. (1978). The generation effect: Delineation of a phenomenon. Journal of Experimental Psychology: Human Learning and Memory, 4(6), 592–604. https://doi.org/10.1037/0278-7393.4.6.592

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

How Smart Teachers Plan Backwards — and Get Better Results

Here is a question worth sitting with: when you plan a lesson, what do you plan first? If the answer is “the activity” — the game, the worksheet, the group task — you are in good company. Most new teachers start there. In practice, that instinct is also the source of one of the most common planning problems in Australian classrooms: lessons that are busy but not particularly purposeful.

The shift that separates competent planning from genuinely effective planning is deceptively simple. Expert teachers start at the end. They ask: what do students need to know or be able to do by the close of this lesson? From there, everything else flows backwards from that answer — the activities, the questions, the formative checks.

That approach has a name: backward design. Put simply, it means designing for learning rather than for activity. This post explains how it works, why the research supports it, and what it looks like in two real Australian classrooms — one in HASS, one in Maths.

Why “Covering the Content” Isn’t Planning

Grant Wiggins and Jay McTighe introduced backward design in Understanding by Design (2005) — and it remains one of the most cited frameworks in curriculum planning. Their core argument is straightforward: if you design a lesson around an activity, the activity becomes the goal. If you design it around a learning outcome, the activity becomes the vehicle.

The difference matters more than it sounds. An activity-first lesson might be engaging and enjoyed by students. By contrast, a learning-first lesson does all of that and moves students measurably closer to a specific understanding. One fills time well. The other changes what students can do.

John Hattie’s research reinforces this. In Visible Learning for Teachers (2012), he identifies clear learning intentions and success criteria as among the highest-leverage moves a teacher can make — with an effect size of 0.75 on student achievement. That said, intentions only work when they are genuinely specific. “Students will learn about fractions” is not a learning intention. “Students will be able to add fractions with the same denominator and explain why the denominator stays the same” is.

The Three-Stage Planning Framework

Wiggins and McTighe describe backward design in three stages. In the Australian context, each maps directly onto the AITSL Professional Standards that preservice and early-career teachers are assessed against. For that reason, understanding the framework is not just good pedagogy — it is also good professional preparation.

🪜 Backward Design: Three Stages
  • Stage 1 — Identify desired results. What should students know, understand, and be able to do? This comes directly from the Australian Curriculum content descriptor and achievement standard for your year level. This is where AITSL Standard 2 (Know the content and how to teach it) does its work — you cannot write a learning intention for content you don’t understand deeply yourself.
  • Stage 2 — Determine acceptable evidence. How will you know students have learned it? This is your assessment — not just the end-of-unit test, but the formative check you build into the lesson itself. Exit tickets, questioning, observation, mini whiteboards. AITSL Standard 5 (Assess, provide feedback and report) lives here.
  • Stage 3 — Plan learning experiences. Only now do you choose activities — the ones that most efficiently move students from where they are to where Stage 1 says they need to be. This is AITSL Standard 3 (Plan for and implement effective teaching and learning) in action.

Notice the order. Assessment comes before activities. For most new teachers, that sequence feels backwards — even counterintuitive. In practice, it is the sequence that makes every other planning decision easier. Each activity can then be evaluated against one clear question: does this move students toward the learning intention, or does it just fill time nicely?

Stage 1 in Practice: Writing a Learning Intention That Works

A learning intention describes what students will learn — not what they will do. “Students will complete a fact file about ancient Egypt” is a task description. By contrast, “Students will identify two ways ancient Egyptians adapted their way of life to the Nile River” is a learning intention. The distinction is worth practising until it becomes automatic.

Hattie suggests sharing the learning intention with students at the start of the lesson — not as a compliance exercise, but because students who know where they are going are better placed to monitor their own progress. That self-monitoring is itself a high-leverage learning behaviour. For this reason, the learning intention pairs naturally with a success criterion: a student-facing description of what meeting the intention looks like in practice. The Australian Curriculum v9.0 achievement standards are the most reliable anchor for writing success criteria at the right level.

✏️ Learning Intention vs Success Criterion — The Difference
  • Learning intention: “We are learning to identify how the features of a place influence the way people live.” (The what of learning — teacher-language, curriculum-anchored.)
  • Success criterion: “I can name two features of the Nile River and explain how each one shaped life for ancient Egyptians.” (The how will I know — student-language, observable, specific.)
  • Not this: “We are learning about ancient Egypt.” (Too broad — students cannot self-assess against it, and neither can you.)

AITSL Standard 1 (Know students and how they learn) shapes how you write these. A success criterion pitched at the wrong level — too abstract for Year 3, too simple for Year 6 — will not function as a genuine reference point for students. In other words, knowing your cohort is not separate from writing good intentions. It is part of the same act.

Worked Example 1: Year 5 HASS

The following example applies backward design to a Year 5 HASS lesson on colonial Australia, anchored to the Australian Curriculum.

📋 Backward Design in Action — Year 5 HASS
  • Curriculum anchor: AC9HS5K03 — The impact of colonial settlement on the lives of Aboriginal and Torres Strait Islander Peoples.
  • Stage 1 — Learning intention: “We are learning to explain how colonial settlement affected the lives of Aboriginal Peoples, using specific evidence.”
  • Success criterion: “I can describe at least two impacts of colonial settlement on Aboriginal Peoples and support each with a piece of evidence from a source.”
  • Stage 2 — Formative evidence: An exit ticket — students write two sentence starters: “One impact was… because the evidence shows…” and “Another impact was… For example…” This is collected before students leave and informs the next lesson’s starting point. For more on making exit tickets work, see our post on formative assessment strategies.
  • Stage 3 — Learning activities: (a) Paired analysis of two short primary sources — a settler diary entry and an account from an Aboriginal perspective. (b) Structured discussion: what do these sources agree on? What do they show differently? (c) Individual written response using the exit ticket stems. The Venn diagram activity — which we’ve discussed before as an alternative to the overused KWL chart — works well at step (b) to surface the contrast between sources.
  • AITSL connection: Standard 1.4 (Strategies for teaching Aboriginal and Torres Strait Islander students) and Standard 2.4 (Understand and respect Aboriginal and Torres Strait Islander people) are both directly relevant here. Source selection and discussion framing must be handled with cultural care and, where possible, informed by community consultation.

Worked Example 2: Year 3 Maths

The same framework applies equally to Maths — where the temptation to jump straight to a worksheet or manipulative activity is especially strong. Even so, the three-stage sequence holds. Start with what students need to understand; build the assessment check; then choose activities that close the gap.

📋 Backward Design in Action — Year 3 Maths
  • Curriculum anchor: AC9M3N04 — Multiply and divide using a range of mental and informal written strategies.
  • Stage 1 — Learning intention: “We are learning to use an array to show how multiplication works and explain what the rows and columns represent.”
  • Success criterion: “I can draw an array for a multiplication fact, label the rows and columns, and write the matching number sentence.”
  • Stage 2 — Formative evidence: Mini whiteboard check — teacher calls a multiplication fact (e.g. 3 × 4), students draw the array and hold it up simultaneously. Teacher scans for: correct dimensions, correct orientation, students who have drawn repeated addition rather than an array. That pattern in the errors pinpoints exactly where re-teaching is needed. See our post on reading the room mid-lesson for more on using student responses as real-time data.
  • Stage 3 — Learning activities: (a) Hook: teacher places 12 counters on a document camera and asks — “how many ways can I arrange these into equal rows?” Students explore with their own counters. (b) Explicit teaching: name the array, define rows and columns, connect to the number sentence. (c) Guided practice: three teacher-led examples with student input. (d) Independent practice: students draw and label three arrays of their choice, then write the matching number sentence. (e) Mini whiteboard exit check as described above.
  • AITSL connection: Standard 2.1 (Content and teaching strategies of the teaching area) — understanding why arrays work (not just how to draw them) is essential before teaching this lesson. Standard 3.3 (Use teaching strategies) — the move from concrete (counters) to representational (drawn array) to abstract (number sentence) is the CRA sequence, and it should be deliberate, not accidental.

The Teaching-Learning Cycle: Beyond a Single Lesson

Backward design works for a single lesson. It works even better when it organises a whole unit. The teaching-learning cycle used across Australian curriculum areas — building the field, deconstruction, joint construction, independent construction — is itself a backward-designed structure. You begin with the end text or performance in mind and work back through the scaffolding students need to get there.

For new teachers, the most practical application is this: before you plan any lesson in a unit, write the end-of-unit assessment task first. Not after you’ve taught everything — before. That task becomes your compass. Consequently, every lesson can be evaluated against one question: does this move students closer to being able to do that?

Beyond that, this approach connects directly to what we’ve explored in our posts on designing a performance of worth and feedback that feeds forward — both of which assume a clear destination that students are working toward. Without a backward-designed starting point, those strategies lose their anchor.

A Note on Using AI in This Process

If you are using AI to support your planning — and there are good reasons to — backward design gives you a much stronger prompt structure. Instead of asking AI to “write a lesson plan on fractions,” give it your learning intention, your success criterion, and your formative check. From there, ask it to suggest activities that bridge the gap. That sequence produces something genuinely useful. By contrast, starting with “write me a lesson” produces something generic.

In other words, the quality of your AI output is a direct reflection of how clearly you have done Stage 1 yourself first. The framework does not become less relevant when AI is involved. If anything, it becomes more important.

The One-Page Planning Habit

For preservice teachers on prac, and for first-year teachers building confidence, a full backward-design unit plan for every lesson is not realistic. That said, the thinking behind backward design can be applied in under five minutes per lesson with a simple habit. Before you open any planning document, write three things on a sticky note.

🎯 The Three-Question Planning Check
  • What will students be able to do by the end that they couldn’t do at the start? (Your learning intention — one specific, observable thing.)
  • How will I know if they’ve got there? (Your formative check — exit ticket, whiteboard, observation, question.)
  • What is the most direct path from here to there? (Your activity sequence — chosen because it serves the intention, not because it fills the time.)

Those three questions, answered honestly before you start planning the rest, will improve your lessons more reliably than any template, resource pack, or planning app. They are the essence of what AITSL Standard 3 asks of you. Put simply, they are the difference between a lesson that covers content and a lesson that builds learning.

“Don’t ask ‘what will I teach today?’ Ask ‘what will students be able to do at the end that they couldn’t do at the start?’ The answer to the second question is your lesson.” — TeacherTalk

💬 Your Turn! Do you plan backwards from the learning intention — or do you start with the activity and work forward? And if you’ve made the switch to backward design, what changed for you? Share below — your experience is exactly what another new teacher needs to read this week.

References

Australian Curriculum, Assessment and Reporting Authority (ACARA). (2023). Australian Curriculum v9.0. https://v9.australiancurriculum.edu.au

Australian Institute for Teaching and School Leadership (AITSL). (2018). Australian Professional Standards for Teachers (Standards 1, 2, 3 & 5). https://www.aitsl.edu.au/teach/standards

Hattie, J. (2012). Visible learning for teachers: Maximising impact on learning. Routledge.

Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.

Wiggins, G., & McTighe, J. (2005). Understanding by design (2nd ed.). ASCD.

7 Free Classroom Tools That Actually Cut Your Workload

Not every classroom has a full class set of iPads. Not every school has fast, reliable Wi-Fi in every room. Not every teacher has time to learn a complex new platform this term. And that is completely fine — because some of the most effective digital tools in education are also the simplest, and some of the most engaging learning experiences require very little technology at all.

This is the final post in our Teaching in the Digital Age mini-series. After exploring digital citizenship and AI as a planning partner, today we’re going practical and low-barrier — a toolkit of digital tools, starting with QR codes, that work in any Australian classroom regardless of budget, device availability, or technical confidence. Because equity matters: if a strategy only works for well-resourced schools, it’s not a solution — it’s a privilege.

QR Codes: Still Underused, Still Brilliant

TeacherTalk has covered QR codes before, but they deserve a fresh look — because their use in Australian education has expanded enormously, and many teachers are still only scratching the surface of what they can do. QR code use in Australian educational institutions has grown rapidly, with schools using them for everything from interactive scavenger hunts to distributing differentiated resources to managing classroom transitions.

The core advantage of a QR code is deceptively simple: it eliminates the barrier between a student and a digital resource. Instead of dictating a long URL, writing it on the board while students squint and mistype, a QR code is scanned in two seconds. That friction reduction turns out to matter a lot in a busy classroom.

Here is how to generate one for free: Adobe Express has a free QR code generator that requires no account. Canva, which most Australian teachers already use, also generates QR codes in its free tier. Both take under a minute.

15 Ways to Use QR Codes Right Now

📱 QR Code Ideas: Start Simple, Go Further
  • Differentiated task cards. Create three versions of a task (below, at, and above year level) and link each to its own QR code. Students scan their code and access the appropriate resource independently — no shuffling of paper piles, no broadcasting which student is working at which level.
  • Exit tickets via Google Forms. Create a Google Form with 2–3 quick comprehension or reflection questions, generate a QR code for it, and post it on the board at the end of the lesson. Students scan, respond, and you have instant formative data in a spreadsheet before they’ve packed up. Links naturally to our post on formative assessment strategies.
  • Learning centres or rotations. Each station in a learning centre has a QR code linking to the task instructions, video stimulus, or additional resource for that station. Students move through independently, without needing the teacher to explain each station every rotation.
  • QR scavenger hunts. Place QR codes around the classroom or school (on doors, walls, furniture) each linking to a clue, question, or resource. Students work in pairs to scan, respond, and move to the next code. High engagement, builds inquiry skills, gets students moving.
  • Vocabulary walls that talk. Add QR codes to word wall displays linking to short explanatory videos, audio pronunciations, or student-created explanations. Particularly powerful for EAL/D students who can access translations or visual supports independently.
  • Reading response journals. Link QR codes on the back of library books or in book corners to a Google Form where students log their reading response — a sentence, a rating, a question it raised. Builds reading culture and gives you an informal record without marking piles of exercise books.
  • Parent communication. Add a QR code to newsletters, classroom displays, or student work portfolios linking to a video explaining the unit of work, a gallery of classroom activities, or a parent FAQ. Bridges the gap between classroom and home in seconds.
  • Assessment instructions. Link the task instructions for a major assessment to a QR code on the assessment sheet. Students can re-watch or re-read the instructions independently without needing to interrupt the teacher.
  • STEM challenge materials. In STEAM activities, QR codes can link to research resources, instructional videos, or design criteria — making inquiry more independent and reducing the teacher as the sole information source. Links to our earlier post on STEAM in the classroom.
  • Student reflection. Post a “Week in Review” QR code each Friday linking to a short reflection form. Three questions: What’s one thing you learned this week? What are you still wondering about? What would you like more help with? Takes five minutes and gives you rich planning data for the following week.

Beyond QR Codes: 5 More Free Tools Worth Your Time

QR codes are the gateway, but they work best as part of a broader low-barrier digital toolkit. Here are five more tools that Australian primary teachers are using effectively right now — all free, all low-complexity, all high-impact:

1. Padlet (padlet.com) — A virtual corkboard where students can post text, images, links, and video. Brilliant for brainstorming, exit tickets, collaborative research, gallery walks, and prior knowledge activation — a much richer alternative to the KWL chart we’ve previously critiqued on this very blog. The free version allows up to three active boards — rotate them across your units.

2. Mentimeter (mentimeter.com) — Live polling, word clouds, and quick question tools. Students respond on any device using a simple code — no account needed on the student end. A word cloud of student responses to “What do you already know about…?” is a far more engaging and visually rich prior knowledge check than a KWL chart. The free tier supports basic presentations and is perfectly adequate for most classroom uses.

3. Kahoot! (kahoot.com) — Most Australian primary teachers already know Kahoot, but many underuse it. Beyond the game-show quiz format, Kahoot can be used for vocabulary practice, formative review before an assessment, and even student-created quizzes where students design questions for each other — which is a genuinely higher-order task requiring deep content knowledge. Free for teachers.

4. Canva for Education (canva.com/en_au/education) — Free for all Australian teachers and students through the Education plan. Beyond creating posters and presentations, Canva’s Whiteboard feature is excellent for collaborative brainstorming, its video editor supports student multimedia projects, and its QR code generator is seamless. If your school doesn’t yet have school-wide Canva for Education access, it’s worth raising with your administration — it consolidates multiple tools into one.

5. Book Creator (bookcreator.com) — A multimodal publishing tool where students create digital books combining text, images, audio, and video. Excellent for summative assessment tasks — the kind of performance of worth where students demonstrate learning through creation rather than a traditional essay or test. The free teacher account allows one library of 40 books — enough for a class set.

The Equity Principle: Digital Tools Must Work for Every Student

Everything in this post is premised on a principle worth stating explicitly: digital tools should expand access, not create new barriers. As we explored in our AI ethics post, technology that advantages the already advantaged is not progress.

When choosing digital tools, ask: Can every student in my class access this? Does it require a device? Does it require home internet? Does it require a parent with a credit card? If the answer to any of these is “yes, and not all my students have that”, you need either a school-funded solution, a paired alternative, or a different approach entirely.

QR codes work beautifully in this regard — students can share devices to scan, codes can be printed and taken home, and the content they link to can be cached or printed as a fallback. The other tools above all have offline or low-bandwidth options worth exploring if your school has connectivity challenges.

A Word on Tool Overload

A final caution: resist the temptation to use every tool, every lesson. Digital tool overload is a real phenomenon — students (and teachers) spending more cognitive energy managing the technology than engaging with the learning. The most effective classroom technology is the technology that becomes invisible — so embedded in the routine that students don’t think about how to use it, only what they’re using it for.

Pick two or three tools from this post. Learn them thoroughly. Build them into regular routines. Then, and only then, consider adding another. That is the same principle we apply to teaching strategies generally — as our earlier post on teaching strategies noted, even the best technique becomes stale when overused. Mix it up, but do it deliberately.

“The best EdTech is invisible. Students aren’t thinking about the tool — they’re thinking about the learning. That’s the sign you’ve got it right.” — TeacherTalk

💬 Your Turn! What’s your go-to digital tool in the classroom — the one you couldn’t imagine teaching without? And is there one on this list you’ve been meaning to try? Share below — you might inspire someone else to take the leap!

References

Australian Curriculum, Assessment and Reporting Authority (ACARA). (2023). Australian Curriculum v9.0: Technologies. https://v9.australiancurriculum.edu.au

Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.

QR Codes Australia. (2025). Harnessing QR codes in Australian education today. https://qrcodesaustralia.com.au/qr-codes-in-australian-education/

Selwyn, N. (2011). Schools and schooling in the digital age: A critical analysis. Routledge.

Uniqode. (2025). Creative ways to use QR codes in the classroom. https://www.uniqode.com/blog/qr-code/qr-codes-in-schools-and-classrooms-for-enhanced-learning

7 AI Prompts That Actually Work in Australian Classrooms

In our recent three-part series on AI in education, we explored the big questions: the ethics, the equity concerns, the academic integrity debates, and what AI might mean for the future of schooling. All of that matters. However, this week we’re getting practical. Because alongside the philosophical questions, there is a very immediate one: how can you, right now, use AI to do your job better — without compromising your professional judgement or your students’ learning?

The answer is not complicated, but it does require a clear mental model. AI is a first-draft tool and a thought partner, not a finished product. Think of it as the teaching assistant who can do the tedious groundwork quickly, freeing you to do the intellectual and relational work that only you can do. Used well, it can genuinely give you back hours each week. Used poorly, it produces generic content that doesn’t know your students, your context, or your curriculum.

Let’s fix that with some practical, Australian-curriculum-anchored examples.

The Golden Rule: Context In, Quality Out

The most important principle for using AI as a planning tool is simple: the more context you give, the better the output. A vague prompt produces a vague result. A specific, contextualised prompt produces something genuinely useful.

Compare these two prompts:

❌ Vague vs ✅ Specific: The Difference Context Makes
  • Vague: “Write me a lesson plan on fractions.” → You’ll get a generic, probably American, lesson that has nothing to do with your class.
  • Specific: “Write a 60-minute Year 4 lesson on adding fractions with the same denominator, aligned to Australian Curriculum content descriptor AC9M4N03. My class has 26 students, including 3 EAL/D learners and 2 students working below year level. Include a warm-up, explicit teaching segment, guided practice, and an exit ticket. Use a pizza context.” → Now you have something to work with.

Australian educator and author Leon Furze, whose book Practical AI Strategies is one of the most grounded teacher resources on this topic, puts it well: we don’t want to offload the important work of planning onto AI. Instead, we use technology to support our own expertise and professionalism. The teacher decides the topic, the content, the context — AI provides a starting point that the teacher then checks, refines, and makes their own.

There is also a genuinely exciting Australian tool worth knowing about: KuraPlan, an AI lesson planning tool trained on the Australian Curriculum. Teachers report that it uses the right curriculum language, references appropriate content descriptors, and understands the Australian context — unlike general-purpose AI tools that default to American standards. It’s free to try and worth exploring as a starting point for planning drafts.

7 Practical AI Prompts for Australian Teachers

The following prompts are designed to be copied, modified with your own class details, and used directly with any AI tool — ChatGPT, Microsoft Copilot, Google Gemini, or the Australian-specific tools above. As always, review and edit the output before using it. Never include identifying student information in your prompts.

✍️ Ready-to-Use AI Prompts — Modify with Your Class Details
  • Lesson outline: “Create a 50-minute Year [X] lesson outline on [topic], aligned to Australian Curriculum content descriptor [paste CD code and description]. Include a hook, explicit teaching segment, structured practice, and a formative exit ticket. My class has [number] students including [brief context — EAL/D, mixed ability, etc.].”
  • Differentiation: “Here is my lesson plan on [topic] for Year [X]: [paste plan]. Suggest three differentiated versions of the main task — one for students working below year level, one at level, and one extension task for students who are ready to go deeper. All must address the same learning objective.”
  • Formative assessment questions: “Generate 5 higher-order questions for a Year [X] lesson on [topic] that require students to apply, analyse, or evaluate — not just recall. Align to Bloom’s revised taxonomy and the Australian Curriculum content descriptor [CD code].” (This connects directly to our post on higher-order questioning.)
  • Feedback on student writing: “Here is a Year [X] student’s writing sample on [topic]: [paste sample, no name]. Provide specific, warm, and growth-oriented written feedback using the 2 Stars and a Wish format. Focus on [specific criterion, e.g. use of evidence, sentence structure]. Do not rewrite the text.” (Links to our post on effective feedback strategies.)
  • Parent communication: “Draft a brief, warm, professional email to parents explaining our upcoming unit of work on [topic] for Year [X]. Include what students will learn, how they’ll be assessed, and one way parents can support learning at home. Keep it under 200 words and free of jargon.”
  • Rubric creation: “Create a rubric for a Year [X] assessment task on [topic] that assesses [specific criteria]. Align to the Australian Curriculum achievement standard for [year level]. Include 4 levels: Exceeding, Meeting, Developing, Beginning. Use language that is clear for students and parents to understand.”
  • Unit planning starting point: “I am planning a 4-week unit for Year [X] on [topic]. The relevant Australian Curriculum content descriptors are [paste CDs]. Suggest a sequence of learning activities that builds conceptual understanding progressively, includes a mix of individual and collaborative tasks, and ends with a performance task. Include suggestions for formative assessment throughout.” (See our post on planning the teaching-learning cycle for the broader framework.)

What to Check Before You Use AI Output

Every piece of AI-generated content needs your professional judgement before it goes near your students. Run through these four checks each time.

Check for cultural fit. AI tools trained mostly on American or British content sometimes produce examples that don’t fit Australian classrooms. Watch for Fahrenheit temperatures, references to US history as if it’s universal, or contexts that won’t land with your students. Replace these with local alternatives — Australian animals, familiar places, events from our history.

Check for curriculum alignment. AI does not always get content descriptors right. Cross-check any curriculum references against the ACARA Australian Curriculum v9.0 before using them in planning documents.

Privacy and Accuracy: Two More Non-Negotiables

Check for student privacy. Never paste student names, identifying information, or specific learning needs data into an AI tool. Use general descriptors only — “a student working below year level”, “an EAL/D learner” — and delete the chat after use.

Check for accuracy. AI can generate plausible-sounding but factually wrong information. Moreover, this is especially important for HASS content, where historical accuracy and cultural sensitivity matter. Always verify specific facts, dates, or research citations that appear in AI output.

Using AI for Your Own Professional Learning

Beyond planning, AI can support your ongoing development as a teacher in ways that are low-cost and immediately practical. Here are three ideas worth trying.

First, use AI to explain a concept you’re not confident teaching. Ask it to explain a maths concept at three different levels of complexity, or to outline the key ideas in a history unit before you design your lessons. Think of it as an on-demand subject matter expert available at any hour.

Second, use AI to stress-test your lesson plans. Paste a lesson plan and ask: “What are the most likely points at which students will be confused or disengaged in this lesson? What could I do at each of those moments?” This connects directly to our post on reading the room and pivoting mid-lesson — AI can help you anticipate problems before they arrive.

Third, use AI to generate misconception checklists. Ask: “What are the most common misconceptions Year 4 students have about [topic]? For each, suggest how a teacher might recognise it in student responses and address it through instruction.” In addition, this kind of anticipatory thinking is exactly what Hattie and Yates describe as a characteristic of expert teachers.

The Boundary That Matters Most

As you build AI into your planning workflow, keep one boundary firmly in mind: AI should never make the professional judgements that are yours to make. It can suggest activities — you decide if they suit your students. It can draft feedback — you decide if it captures what this particular child needs to hear. Similarly, it can propose a rubric — you decide if it reflects what genuinely matters in this assessment.

Your knowledge of your students, your understanding of your community, your professional instincts built through experience — these are irreplaceable. AI is a tool in service of those things, not a substitute for them. As a result, use it to save time on tasks that don’t require your expertise, so you have more energy for the ones that do.

We do not need teachers who can use every AI tool. We need teachers who understand learning deeply enough to know when AI helps it and when it harms it. — TeacherTalk

💬 Your Turn! Are you using AI in your planning already? What’s worked well — and what’s produced results you wouldn’t touch with a barge pole? Share your experience below. The best prompt bank for Australian teachers is going to be built by Australian teachers sharing what actually works.

References

Australian Curriculum, Assessment and Reporting Authority (ACARA). (2023). Australian Curriculum v9.0. https://v9.australiancurriculum.edu.au

Furze, L. (2024). Practical AI strategies: Engaging with generative AI in education. Amba Press. https://leonfurze.com

Hattie, J., & Yates, G. (2014). Visible learning and the science of how we learn. Routledge.

National AI in Schools Taskforce. (2023, updated 2024). Australian Framework for Generative AI in Schools. Australian Government Department of Education. https://www.education.gov.au

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.

Digital Citizenship: Teaching Kids to Think Online

Something shifted in Australian schools on 10 December 2025. The world’s first social media minimum age law came into effect, requiring platforms including Instagram, TikTok, Snapchat, X, and YouTube to take reasonable steps to prevent Australians under 16 from holding accounts. Whether you support the policy or not, one thing is certain: it has made digital citizenship conversations in primary schools more urgent, more real, and more relevant than ever.

Because here is the truth that no law changes: students are navigating a complex, fast-moving digital world every single day, and schools are one of the few places where they can be taught — explicitly, systematically, and safely — how to do that well. Digital citizenship is not a poster on the wall or a one-off lesson in Term 2. It is a set of thinking skills that, like all thinking skills, need to be taught, practised, and revisited throughout a child’s entire schooling.

This is the first of three posts in our Teaching in the Digital Age mini-series. Today we focus on digital citizenship and online safety. Next week, we look at using AI as your planning partner. And in the final post, we revisit QR codes and a broader toolkit of practical digital tools for any classroom, any budget.

What Digital Citizenship Actually Means

Digital citizenship is often reduced to “be nice online” — a well-meaning but insufficient framing. The eSafety Commissioner, Australia’s independent regulator for online safety, defines digital literacy as the knowledge and skills students need to create, manage, communicate and investigate data, information and ideas, and solve problems — in ways that protect the safety of themselves and others in digital environments.

That is a much bigger brief than being polite on social media. It encompasses critical thinking about online content, understanding of digital footprints and privacy, the capacity to identify misinformation and manipulation, the ability to seek help when something goes wrong, and the social and emotional skills to manage online relationships with the same care they’d bring to offline ones.

Think of it this way: we teach students road safety not just by telling them to look both ways, but by building a genuine understanding of traffic, risk, judgement, and what to do in an emergency. Digital citizenship deserves the same depth.

The eSafety Commissioner: Your Best Free Resource

If you haven’t explored the eSafety Schools’ Hub recently, now is the time. It is genuinely one of the best free classroom resources available to Australian teachers, and it has been significantly updated following the social media age restriction changes.

The hub provides curriculum-linked classroom resources from Foundation to Year 12, covering online safety, cyberbullying, digital reputation, respectful communication, and privacy. Their classroom resources page lets you filter by year level and topic — so you can find age-appropriate materials for your specific cohort in minutes. Highlights include the Mighty Heroes animated series and interactive game for primary students, the award-winning Cyberbullying short film for upper primary, and a suite of privacy and personal information activities that connect directly to the Digital Technologies curriculum.

The Best Practice Framework for Online Safety Education is also worth bookmarking — it provides an evidence-based structure for whole-school approaches to online safety, covering how to prepare policy, engage the community, educate students and staff, and respond to incidents. It aligns neatly with AITSL Standard 4 (Create and maintain supportive and safe learning environments).

🛡️ 5 Digital Citizenship Concepts Every Primary Student Needs
  • Digital footprint. Everything students do online leaves a trace — and many traces are permanent. Teach students to ask before posting: “Would I be happy if my teacher, my grandparent, or a future employer saw this?” This connects directly to the Digital Technologies curriculum content descriptor for Years 5–6 (AC9TDI6K04): students identify their digital footprint and recognise its permanence.
  • Privacy and personal information. What counts as personal information? Why does it matter who has it? The eSafety Commissioner’s privacy and security activities walk students through this concretely and age-appropriately. Help students understand the difference between public and private, and why platforms collect data.
  • Critical evaluation of online content. Not everything online is true. Not everything is what it appears to be. Students need explicit practice in identifying reliable sources, recognising bias, and detecting manipulation — skills that are directly aligned with the Australian Curriculum’s Critical and Creative Thinking General Capability.
  • Respectful online communication. The tone students use online matters. Teach the concept of the “online disinhibition effect” in age-appropriate terms: people sometimes say things online they would never say face-to-face, because the screen creates a false sense of distance. Empathy doesn’t switch off when you open an app.
  • Help-seeking. If something goes wrong online — and at some point it will — students need to know who to tell, what to say, and that telling an adult is the right move, not a betrayal of friends. The eSafety Commissioner’s I need help page for young people is worth sharing with your class.

Moving Beyond the Poster: Teaching It in Context

The most effective digital citizenship teaching doesn’t happen in isolation — it happens when real situations arise, when students have genuine questions, and when the content is embedded in authentic learning experiences. Schools that are getting this right are not running digital citizenship as a separate subject; they are weaving it into HASS Civics and Citizenship units, into English (media literacy and text analysis), into Health (wellbeing and relationships), and into the everyday fabric of how the school talks about technology use.

Some approaches that work particularly well in Australian primary classrooms:

Scenario-based discussion. Rather than telling students what to do, present them with genuine dilemmas and ask them to reason through it. The eSafety Commissioner’s Making Good Choices Online slide deck does exactly this — placing students in the shoes of a character and asking them to decide what to do. Connecting this to our earlier discussion of higher-order questioning strategies is natural: “What would you do if…?”, “What might happen if…?”, and “How might the other person feel if…?” are all HOTS questions applied to digital life.

News and media literacy investigations. Bring in real examples (age-appropriate) of misleading content, out-of-context images, or sponsored posts that don’t look like advertising. Ask students to investigate using the SIFT method (Stop, Investigate the source, Find better coverage, Trace claims). This is enquiry-based learning applied to digital information — and it’s some of the most genuinely important critical thinking work students will do.

Digital citizenship connected to the social media ban. The under-16 social media law is a live, real-world policy that students have opinions about and questions around. Use it. Explore it as a civics issue: Why did the government make this decision? Who supported it? Who opposed it? What evidence did they use? What rights and responsibilities are in tension? This connects directly to assessment for and as learning — students analysing and evaluating real-world evidence is exactly what good HASS looks like.

The Whole-School Dimension

Individual classroom teaching matters enormously, but it is most effective when it sits within a coherent whole-school approach. The eSafety Toolkit for Schools provides a four-category framework — Prepare, Engage, Educate, Respond — that helps schools audit their current approach and build systematic improvement. It includes self-assessment tools, policy checklists, risk assessment frameworks for new technologies, and guidelines for managing online incidents outside school hours.

If your school doesn’t yet have a clear, current digital citizenship policy — one that addresses not just rules but education — this toolkit is the right starting point. And given that the Child Safe Standards now require schools to maintain online spaces that are safe and well-monitored, this is no longer optional. It is a professional and legal responsibility.

For preservice teachers, this connects directly to AITSL Standard 4.5 (Use ICT safely, responsibly and ethically). Demonstrating on prac that you understand the online safety dimensions of your digital tool choices — and that you teach students to think critically about their digital lives — is increasingly expected, and increasingly impressive to assessors who understand where education is heading.

“Digital citizenship is not about fear. It is about equipping young people with the thinking skills and the confidence to navigate the digital world as active, critical, and caring participants.” — Adapted from eSafety Commissioner, 2025

💬 Your Turn! How does your school approach digital citizenship — as a standalone topic, embedded across curriculum, or somewhere in between? Have the social media age restrictions changed the conversation in your staffroom? Share your experience below!

References

Australian Curriculum, Assessment and Reporting Authority (ACARA). (2023). Australian Curriculum v9.0: Digital Technologies. https://v9.australiancurriculum.edu.au

Australian Institute for Teaching and School Leadership (AITSL). (2018). Australian Professional Standards for Teachers (Standard 4.5). https://www.aitsl.edu.au/teach/standards

eSafety Commissioner. (2025). Best Practice Framework for Online Safety Education. https://www.esafety.gov.au/educators/best-practice-framework

eSafety Commissioner. (2025). Classroom resources for educators. https://www.esafety.gov.au/educators/classroom-resources

eSafety Commissioner. (2025). Supporting digital literacy for under-16s. https://www.esafety.gov.au/educators/supporting-digital-literacy-for-under-16s

eSafety Commissioner. (2025). Toolkit for Schools. https://www.esafety.gov.au/educators/toolkit-schools

Ribble, M. (2015). Digital citizenship in schools: Nine elements all students should know (3rd ed.). International Society for Technology in Education.

From Efficient to Transformative: AI, SAMR and TPACK

Week 13 · Pedagogy · Teaching with Technology

From Efficient to Transformative: AI, SAMR and TPACK

“The question is not how can I use AI? — it is how can AI help me create a transformative, student-centred learning experience?“
⏱ 5 min read · By Matt Faulkner · Pedagogy / Teaching with Technology

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.

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.

🪜 SAMR applied to generative AI
  • Substitution — AI writes the quiz you used to write yourself. Same quiz, same pedagogy, faster delivery.
  • Augmentation — AI writes a better quiz: adaptive difficulty, auto-generated distractors, instant feedback. Still a quiz.
  • Modification — A reading becomes a conversation. Students use an AI tutor to interrogate a text, surface their own misconceptions, and bring questions to class. The task is genuinely redesigned.
  • Redefinition — A task that could not exist before. A history student conducts a simulated interview with a historical figure and has to defend, with evidence, where the AI’s responses sit in the record and where they drift into plausible-sounding fiction.

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.

🎯 Three questions to ask — in this order
  • Content — What is the deep disciplinary knowledge I want students to build, and what parts of that knowledge are now more, not less, important because AI can approximate the surface of it?
  • Pedagogy — What does student-centred learning of that content actually look like — inquiry, dialogue, critique, production, reflection?
  • Technology — Where, specifically, does AI deepen that pedagogy rather than shortcut it?

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.

Teaching has never been about the tools. It is about the people in the room. The measure of whether we are using AI well is not how much time it saves us — it is whether the learning that happens in that room is different, deeper, and more clearly centred on the student than it was before.

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

Your turn.Where are you on the SAMR ladder with AI right now — still mostly Substitution, or pushing into Modification and Redefinition? Drop a comment below with one AI-supported task you have redesigned (or want to redesign) this term. I would love to hear what is actually working in real classrooms.

References and further reading

Koehler, M. J., & Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)? Contemporary Issues in Technology and Teacher Education, 9(1), 60–70.

Puentedura, R. R. (2006). Transformation, technology, and education. Hippasus.

Hamilton, E. R., Rosenberg, J. M., & Akcaoglu, M. (2016). The Substitution Augmentation Modification Redefinition (SAMR) model: A critical review and suggestions for its use. TechTrends, 60(5), 433–441.

Violanti, R. J. (2026). Humanizing generative AI: Three ways to keep students at the center of your classroom. Faculty Focus.

AI and the Future of Learning: What We Keep, What We Redesign

In Part 1 we grappled with what AI is and the ethical questions it raises. In Part 2 we looked honestly at what’s happening in schools right now. Now comes the hardest part: imagining forward.

If we had the opportunity to redesign education with AI as a given — not a disruption to manage, but a reality to build around — what would we keep? What would we deliberately redesign? And what would we fight, with everything we have, to protect? These are not abstract questions. The decisions being made in schools, systems, and staffrooms right now are shaping the answer.

Harnessing a Technology That Won’t Wait

One of the most disorienting things about generative AI is its pace of change. The tools available to teachers and students today are significantly more capable than those available eighteen months ago, and the tools available eighteen months from now will likely make today’s seem primitive. Education systems are built for decade-long policy cycles. AI is operating on a month-to-month development timeline.

This is not a reason for paralysis. It is an argument for building adaptive capacity — in teachers, in schools, and in systems — rather than chasing specific tool competencies that will be outdated before the professional development program is complete. What teachers need is not training in ChatGPT version 4. What they need is a deep enough understanding of how AI works, what it can and cannot do, and what good learning looks like, to make intelligent, principled decisions as the technology continues to evolve.

Australia’s senior educational policymakers have identified managing risks, educating teachers, and educating system leaders as the highest priorities for responsible AI integration. The research is clear that teacher capacity is both a top priority and a major challenge — and that ways for teachers to undertake significant professional learning to equip themselves and their students with AI skills, without overburdening them, remain genuinely unclear. This is the honest state of the field.

“We do not need teachers who can use every AI tool. We need teachers who understand learning deeply enough to know when AI helps it and when it harms it.” — TeacherTalk
teacher and students in a future-facing, collaborative classroom setting
The classroom of the future will be defined less by technology and more by the quality of the human relationships and thinking within it

What Needs to Be Redesigned: Assessment

If there is one part of the education system that AI has most urgently exposed as in need of redesign, it is assessment. The traditional essay, the take-home assignment, the multiple-choice test — these formats were designed for a world in which students had to do their own thinking to produce a response. In a world where AI can generate a credible essay on any topic in thirty seconds, assessing a student’s learning through a written product alone is no longer adequate.

This doesn’t mean abandoning writing. Writing is thinking made visible, and the process of composing a careful argument remains one of the most powerful learning activities a student can undertake. But it does mean reconsidering what we assess, and how we authenticate it.

Assessment reform for the age of AI is pointing in several consistent directions. Performance-based and oral assessments — where students must demonstrate understanding in real time, in conversation, with follow-up questions — are inherently AI-resistant and arguably more valid measures of genuine learning anyway. Portfolio assessment that captures process, including drafts, reflections, and revision histories, provides evidence of thinking that AI cannot fabricate. Authentic tasks with local, specific, personal dimensions — the kind of performance of worth we’ve written about before — demand contextual knowledge and personal voice that generic AI outputs cannot replicate.

These approaches are not new. They are, in many cases, what good assessment has always looked like. AI has simply made the case for them more urgent.

✅ Assessment Design Principles for an AI World
  • Build in process evidence. Draft submissions, reflective journals, and annotated bibliographies document thinking that AI cannot fake.
  • Use local and specific context. Tasks anchored to the student’s own community, experience, or school are far more AI-resistant than generic topics.
  • Include oral or performance components. A student who can explain their work, respond to follow-up questions, and articulate their reasoning has demonstrably done the thinking.
  • Separate the product from the learning. What did the student learn, not just what did they produce? Exit tickets, verbal check-ins, and the diagnostic questioning strategies in last week’s post on reading the room are more relevant than ever.
  • Design with the “so what” in mind. Assessments connected to real-world purpose, real audiences, and genuine consequences for the community are inherently more meaningful — and more AI-resistant. Revisit our post on the “so what” of learning.

What AI Could Do for Personalised Learning — If We Get It Right

Here is the genuinely exciting possibility. For decades, educators have understood that different students learn differently, at different rates, with different entry points and different strengths. The practical constraint has always been scale — one teacher, thirty students, one lesson. Differentiation has been the aspiration, but genuine personalisation at scale has been largely impossible.

AI changes this constraint. Adaptive learning platforms that adjust in real time to student interactions can, in principle, provide every student with an experience calibrated to their current understanding — extending the student who has mastered the concept, supporting the student who needs a different explanation, and providing the student with a disability or language barrier with accommodations that would otherwise require specialist support.

This is not science fiction. Carnegie Learning’s adaptive mathematics platform, Khan Academy’s Khanmigo tutoring tool, and government-built tools like NSW’s EduChat are all operational examples of this direction. Carnegie Learning’s adaptive learning platform provides a customised learning experience that aims to adapt in real time to each student’s interactions, while Khan Academy’s Khanmigo offers AI one-on-one tutoring by mimicking a writing coach — giving prompts and suggestions to move students forward as they write, debate, and collaborate.

The equity caveat, however, remains essential. Access to these tools is not uniform. Students in well-resourced urban schools with reliable devices and fast internet are positioned to benefit most from AI personalisation. Students in under-resourced schools, remote communities, and low-income families may find that the digital divide that has always characterised Australian education simply takes a new form. Without deliberate intervention — in infrastructure, in teacher training, in culturally responsive design — AI personalisation risks becoming another advantage for the already advantaged.

The Teacher in an AI World: More Important, Not Less

Perhaps the most important thing to say about the future of education and AI is this: the teacher becomes more important, not less, as AI becomes more capable.

This seems counterintuitive. If AI can deliver content, provide feedback, personalise the learning pathway, and assess the outcome — what is left for the teacher? The answer is everything that matters most.

The teacher who knows which student is being bullied and is too ashamed to ask for help. The teacher who notices that a child’s sudden academic disengagement reflects something happening at home. The teacher who creates the conditions of psychological safety that allow a student to take an intellectual risk. The teacher who connects a student’s passion for gaming to a career pathway they had never imagined. The teacher who, through the quality of their relationship with a child, communicates the fundamental message: you are capable, you matter, and I believe in you.

No AI does any of this. And in a world where AI handles more of the cognitive transactional work of education, these deeply human capabilities become the irreplaceable core of what teachers are for. The profession must articulate this loudly and clearly — not as a defensive reaction to AI, but as a confident statement of what teaching actually is.

teacher in genuine one-on-one conversation with a student, warm and engaged
No AI replicates the teacher who knows a student’s story and believes in their potential. That is the irreplaceable core of teaching.

The Questions That Will Define the Next Decade

We began this series with a set of hard questions. It is worth returning to them, not with definitive answers — the field is moving too fast for those — but with the honest acknowledgement that these questions demand deliberate, ongoing engagement from every educator.

🔍 The Questions That Will Shape Education’s AI Future
  • Can AI help teachers reach more kids and create better learning experiences? Yes — if we design the tools with equity at the centre, invest in teacher capability, and use AI to amplify human teaching rather than replace it.
  • Will AI hurt or help the traditional model of schooling? Both. It will expose what is worth keeping and accelerate the demise of what was never good enough. That is, ultimately, an opportunity.
  • Who is monitoring and researching the impact of AI on students, teachers, and the system? Researchers, governments, and frameworks are emerging — but the evidence base is thin and the technology is moving faster than the research. Schools must become active generators of evidence, not passive recipients of it.
  • What makes this different from every other tech revolution in education? AI operates inside the learning process itself. It doesn’t just present information — it can do the thinking. That is qualitatively different from every previous technology, and demands a qualitatively different response.
  • How can we harness a technology that is evolving so exponentially? By investing in deep pedagogical knowledge and adaptive professional capacity, not tool-specific training. Teachers who understand learning deeply will make good decisions about AI, whatever form it takes.
  • What happens when AI designs, completes, and grades the task? Nothing is learned. The answer is not to ban AI, but to redesign tasks so that the human thinking is visible, valued, and authenticated — and to build a school culture where genuine learning is the goal, not just the credential.

A Final Word: What We Fight to Protect

Education is not primarily about information transfer. It never was. It is about the development of human beings — their capacity to think, to connect, to create, to care, and to contribute to a world that is shared with others. AI can support some of those processes. It cannot replace any of them.

What we fight to protect, in an AI-saturated world, is the space for genuine human thinking — the productive struggle, the creative leap, the moment of understanding that belongs to the student and to no one else. We fight for equitable access to the benefits AI offers, because a technology that advantages the already advantaged is not progress. We fight for First Nations students whose cultures, languages, and ways of knowing must not be erased or distorted by AI systems trained on predominantly Western datasets. And we fight for the teaching profession — not as it is, but as it could be at its best: relationships of trust, built over time, between adults who believe in children and children who are learning to believe in themselves.

That is what education is for. AI does not change it. It simply asks us to be clearer about it than we have ever had to be before.

For practical strategies to keep human thinking at the centre of your lessons, revisit our posts on higher-order questioning, formative assessment, and assessment for, as, and of learning.

“AI will not replace great teachers. But teachers who understand AI will replace those who don’t — because their students will be better prepared for the world that actually exists.” — TeacherTalk

💬 Your Turn! What would you keep, redesign, or fight to protect in education as AI reshapes the landscape? This is one of the most important conversations our profession has ever had — and your voice belongs in it. Share your thoughts below.

References

Bower, M., Hedberg, J., Kuswara, A., & Dalgarno, B. (2025). What generative artificial intelligence priorities and challenges do senior Australian educational policy makers identify (and why)? The Australian Educational Researcher. https://doi.org/10.1007/s13384-025-00801-z

Furze, L. (2025, December 3). What the National AI Plan means for schools. leonfurze.com. https://leonfurze.com/2025/12/03/what-the-national-ai-plan-means-for-schools/

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2023). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32, 504–526. https://doi.org/10.1007/s40593-021-00239-1

Lodge, J. M., Bower, M., Gulson, K., Henderson, M., Slade, C., & Southgate, E. (2025). Australian Framework for Artificial Intelligence in Higher Education. Australian Centre for Student Equity and Success. https://www.acses.edu.au

Lodge, J. M., Howard, S., Selwyn, N., & Corrin, L. (2023). Assessment reform for the age of artificial intelligence. TEQSA. https://www.teqsa.gov.au

National AI in Schools Taskforce. (2023, updated 2024). Australian Framework for Generative AI in Schools. Australian Government Department of Education. https://www.education.gov.au

Varsik, S., & Vosberg, L. (2024). The potential impact of artificial intelligence on equity and inclusion in education. OECD Artificial Intelligence Papers, No. 23. https://doi.org/10.1787/15df715b-en

AI in Classrooms Right Now: The Good, the Messy, the Urgent

In Part 1 of this series, we asked the hard questions about what AI is and what ethical challenges it poses for Australian schools. Now let’s get specific. Because AI in education is no longer theoretical — it is happening in classrooms right now, in ways that are extraordinary, messy, promising, and at times deeply confronting.

This post looks at what AI is actually doing in schools today: the genuine benefits being documented in practice, the scenarios that are challenging everything we thought we knew about assessment and academic integrity, and the question that sits at the centre of it all — when AI can design the task, complete the task, and grade the task, what exactly is the student doing?

What AI Is Actually Doing in Australian Schools

Australia is moving faster on AI in schools than many realise. Research conducted in 2024 found that 78.2% of secondary schools were already using AI in some form. The nature of that use varies enormously — from a teacher using AI to generate a first draft of a rubric, to a school deploying a government-built AI tutoring assistant for every student.

The most significant state-level examples are worth knowing about:

🏫 AI in Australian Schools: Current Examples
  • NSW — EduChat: Trialled in 16 schools in Term 1 2024, expanded to 50 schools in Term 2, rolled out to all NSW public school staff by the end of 2024, and now available to all Year 5–12 students. Hosted in Azure’s Sydney data centre, it uses a blend of OpenAI models and eight content filtering systems. Crucially, it is designed to refuse direct answers — instead responding with guiding questions to encourage critical thinking. The NSW Department explicitly states that free-to-use commercial AI tools are neither endorsed nor recommended for use in their schools.
  • South Australia — EdChat: Rolled out to all SA public high schools from October 2025. Functions as an AI tutoring assistant helping students explore their coursework more deeply, with government oversight of data and content.
  • Queensland: The Queensland Department of Education has emphasised generative AI for regional and remote student populations, using AI to enable personalised learning, differentiated support, and improved accessibility in areas where teacher resources are limited.
  • Khan Academy — Khanmigo: Used in some Australian schools, this AI tutoring tool offers one-on-one coaching by mimicking a writing coach — giving prompts and suggestions to move students forward as they write, debate, and collaborate, rather than simply providing answers.
  • Carnegie Learning: An adaptive learning platform used in mathematics education that adapts in real time to each student’s interactions, providing a customised learning experience responsive to varied learning requirements.

Can AI Help Teachers Reach More Kids?

This is the question that genuinely excites many educators — and the evidence, while still early, is encouraging in specific areas.

AI tutoring tools can provide a form of one-on-one support that no teacher can replicate at scale. In a class of 28 students, a teacher can interact meaningfully with each child for perhaps a few minutes per lesson. An AI tutoring tool can engage every student simultaneously, adapt to their current understanding in real time, and provide immediate, individualised feedback. For students with English as an additional language, for students with learning difficulties, for students in remote schools with limited specialist support, these capabilities represent a genuine opportunity to reduce disadvantage.

AI can also free up teacher time for the work that only humans can do. When AI handles routine tasks — generating first drafts of lesson materials, creating differentiated worksheet versions, producing formative quiz questions, or providing initial written feedback on drafts — teachers can redirect their attention to relationship-building, targeted small-group instruction, and the nuanced professional judgement that is at the heart of great teaching. Our earlier posts on formative assessment and designing a performance of worth are directly relevant here — AI can support these processes, but it cannot replace the teacher’s judgement in designing tasks that are worth students’ genuine effort.

Most high school teachers who used AI in their work in the 2024–25 school year reported that these tools saved time and improved the quality of their work. That finding matters — teacher workload is one of the most significant drivers of attrition from the profession, and anything that genuinely reduces administrative burden without compromising student outcomes is worth taking seriously.

The Scenario Nobody Wants to Talk About

Here it is. The scenario that is already happening in classrooms around the world, including in Australia. A teacher uses AI to design an assessment task. A student uses AI to complete that task. The teacher uses AI to provide feedback on the submission. And somewhere in that loop, the student has done very little actual thinking.

This is not a hypothetical. Research examining the impact of generative AI on assessment has found that assessments were easily compromised using generative AI, and that many markers found it challenging to identify which student work was human-authored, which was AI-modified, and which was AI-authored. In higher grade bands, students using AI can generate work that is not only undetectable but that achieves grades their own unaided writing would not have reached. For teachers, this creates an unsettling dynamic where the marking process becomes contaminated by uncertainty — second-guessing authorship, with grade disparities emerging between markers who suspect AI use and those who do not.

In one telling study at a UK business school, markers showed unusually large grade disparities between first and second markers — with some appearing to have consciously or subconsciously marked down work they suspected was AI-generated. This is not a defensible assessment practice, but it reflects a very human response to a genuine problem of attribution in an AI-saturated environment.

“When AI designs a task, the student uses AI to complete the task, and AI grades the task for the teacher — what exactly has been learned? This is not a future scenario. It is happening now.” — TeacherTalk

The Cognitive Offloading Problem

There is a subtler risk beneath the academic integrity conversation that deserves more attention: cognitive offloading. Research is finding that students who rely on AI to complete higher-order thinking tasks may produce stronger immediate answers but develop weaker long-term learning and less independent capability. When novices lean on AI they often skip the hard metacognitive work — the struggle, the confusion, the revision — that builds genuine expertise.

A national survey of over 1,200 teens and young adults found that 41% believe AI will have both positive and negative impacts on their lives in the next decade. Those expecting negative impacts worry primarily about job displacement and AI dominance — but the educational risk of cognitive offloading is arguably more immediate. If students are using AI to avoid the hard thinking that builds neural pathways and conceptual understanding, the short-term grade gain may come at a long-term developmental cost.

This is precisely why the design of NSW’s EduChat is significant. By refusing to provide direct answers and responding instead with guiding questions, it attempts to position AI as a Socratic tutor rather than a task-completion engine. Whether that design holds up when students are stressed, time-poor, and looking for shortcuts remains to be seen.

What About Academic Integrity? The Research Is Mixed

The academic integrity question is genuinely complicated. Here is what the research actually shows:

Surveys of secondary students have found that approximately 19% of teens who are aware of ChatGPT have used it to help with schoolwork. The same research found that students consider using AI to research new topics broadly acceptable (69%), but consider using it to write an essay far less so (20%). There appears to be a student-held intuition about where AI assistance crosses a line — though that intuition is inconsistently applied under pressure to perform.

Interestingly, a study based on confidential student surveys found that cheating rates did not significantly increase in the early period of AI adoption — though a majority of teachers believe AI will negatively impact academic integrity over the next three years. The gap between what students are doing and what teachers fear they are doing is itself an important data point.

What is clear is that the solution is not solely technological. Detection tools using AI to detect AI-generated text are in an arms race with the generative tools themselves and will never be definitively reliable. The answer, as multiple researchers have concluded, is partly cultural — building a genuine school ethos around academic integrity, helping students understand the value of their own thinking, and redesigning assessments so that AI completion provides no meaningful advantage.

That last point brings us to the most important pedagogical response available to teachers right now: task design. Our post on the “so what” of learning is directly relevant here — tasks with a genuine real-world purpose, requiring personal voice, local knowledge, and specific evidence of process, are inherently more AI-resistant than generic essay prompts. The performance of worth is making a comeback.

Will AI Hurt or Help the Traditional Model of Schooling?

Both. The traditional model of schooling — teacher-delivered content, individual written assessment, grades as the primary currency of learning — is genuinely vulnerable to AI disruption. If the goal of education is to produce a correct written response, AI can do that faster and often better than a student can. That should prompt a serious rethink of what we’re assessing and why, not a defensive reaction against the technology.

But the elements of schooling that AI cannot replicate — human relationships, mentorship, the socialisation of learning, the development of character, curiosity, and resilience — become more important, not less, in an AI-saturated world. The teacher who knows a student’s story, who notices when something is wrong, who believes in a child’s potential before the child believes it themselves — no algorithm does that. And that is where the teaching profession should be investing its energy and its advocacy.

In Part 3 of this series, we’ll look forward: what could an AI-informed education system look like if we designed it well? What would we keep, what would we redesign, and what would we fight to protect? Stay tuned.

💬 Your Turn! Have you seen AI used well in a classroom — by a student or a teacher? Or have you encountered the scenario where AI did the work and nobody learned anything? Share your experience below — the most important research on this topic is happening in classrooms like yours.

References

Bower, M., Hedberg, J., Kuswara, A., & Dalgarno, B. (2025). What generative artificial intelligence priorities and challenges do senior Australian educational policy makers identify (and why)? The Australian Educational Researcher. https://doi.org/10.1007/s13384-025-00801-z

Digital Education Council. (2024). Global AI student survey 2024. Digital Education Council.

Furze, L. (2025, December 3). What the National AI Plan means for schools. leonfurze.com. https://leonfurze.com/2025/12/03/what-the-national-ai-plan-means-for-schools/

Kofinas, A., Malesios, C., Vikan, A., & Lev-On, A. (2025). The impact of generative AI on academic integrity of authentic assessments within a higher education context. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13585

Pew Research Center. (2023). Teens and AI: Awareness and use of ChatGPT. Pew Research Center.

Teaching for Tomorrow. (2025). AI in high school education: Trends, challenges, and opportunities. Bowdoin College Hastings AI Initiative.

Varsik, S., & Vosberg, L. (2024). The potential impact of artificial intelligence on equity and inclusion in education. OECD Artificial Intelligence Papers, No. 23. https://doi.org/10.1787/15df715b-en

AI in Schools: The Questions You Can’t Ignore

Let’s be honest. When ChatGPT launched in November 2022, most schools did one of two things: they either panicked and banned it, or they quietly hoped it would go away. Neither strategy worked. Artificial intelligence is now embedded in the daily lives of students across Australia — and the question is no longer whether it will affect your classroom. It already has. The question is what you’re going to do about it.

This is the first of a three-part series on AI in education. Today we’re asking the foundational questions: what exactly is AI, why is this moment genuinely different from previous technology waves in schooling, and what ethical questions does it raise that we simply cannot afford to sidestep? Part 2 will look at how schools are using AI right now — including some confronting examples. Part 3 will explore what a genuinely AI-informed education system could look like in the future.

So What Actually Is AI — and Why Now?

Artificial intelligence refers broadly to computer systems capable of performing cognitive tasks traditionally reserved for humans — perceiving, reasoning, learning, and generating content (Marandi, 2025). What makes generative AI — tools like ChatGPT, Google Gemini, and Microsoft Copilot — genuinely new is its ability to produce original text, images, code, and ideas in response to natural language prompts. You don’t need to know how to code. You just talk to it.

Previous technology waves in education — the overhead projector, the computer lab, the interactive whiteboard, the iPad — all shared a common characteristic: they were tools that humans used to do things. AI is different. It can do things on behalf of humans. It can write the essay, solve the maths problem, generate the rubric, mark the work, and provide feedback — all before the teacher has opened their laptop. That is not an incremental change. That is a structural one.

It is also moving with extraordinary speed. Australia’s Education Ministers endorsed the 2024 Framework Review for Generative AI in Schools in June 2025, making the Australian Framework for Generative AI in Schools the national blueprint for responsible classroom use. The framework itself — first launched just in October 2023 — has already required annual updates to keep pace with new developments. No previous educational technology has required a national policy framework within twelve months of becoming publicly available.

What Makes This Different from Every Other “Tech Revolution”?

Teachers who have been in the profession for more than a decade have lived through multiple rounds of “this technology will transform education.” The computer lab. Web 2.0. The 1:1 iPad program. Flipped learning. MOOCs. Each arrived with enormous promise and significant hype. Most were eventually absorbed into schools in modest, useful ways — or quietly abandoned.

So why should AI be any different? Here are three reasons it genuinely is.

First, the access barrier is essentially zero. Every previous technology required infrastructure — hardware, software licences, technical support, training. Generative AI tools are free, available on any device with an internet connection, and require no training to use at a basic level. A Year 6 student in a regional Queensland school can access the same AI capability as a student at an elite private school in Sydney. This is simultaneously AI’s greatest democratic promise and its most complex equity problem — a point we’ll return to shortly.

Second, it operates inside the learning process itself. Previous technologies were largely about accessing or presenting content. AI can participate in the cognitive work of learning — drafting, revising, explaining, questioning, assessing. Large language models capable of generating human-quality text and creative content have blurred the lines of authorship and academic integrity, giving students unprecedented access to tools capable of completing assignments, writing essays, and solving complex problems with minimal personal effort.

Third, it is evolving faster than education policy can follow. The implicit biases, inaccuracies, lack of transparency, and potential disruption to traditional learning and teaching posed by AI means that understanding how to set responsible and ethical policy is not necessarily obvious. By the time a school develops a considered AI policy, the technology it was written about has already changed significantly.

“Free access to powerful generative AI in schools has left educators and system leaders grappling with how to responsibly respond to the consequent challenges and opportunities this new technology poses.” — Bower et al., 2025, The Australian Educational Researcher

The Equity Question Nobody Wants to Answer

Here is the uncomfortable truth at the heart of AI in education: not all students will benefit equally, and without deliberate intervention, AI is likely to make existing educational inequalities worse, not better.

Consider what “free access” actually means in practice. A student with a reliable device, a fast internet connection, a quiet space to work, and a parent who can help them think critically about AI outputs is in a fundamentally different position from a student in a remote community with intermittent connectivity, a shared device, and no adult at home who has heard of ChatGPT. There are massive inequities in access to digital technologies throughout Australia, which mirrors global trends, and the way for education systems to provide all students with access to the benefits that AI might provide at the same time as safeguarding them against the risks is deeply uncertain.

The OECD’s 2024 working paper on AI and equity in education puts it plainly: AI adoption in education risks exacerbating existing disparities, and there is an urgent need to address privacy and ethical concerns, enhance cultural responsiveness, and provide continuing professional learning in AI to ensure AI supports a more equitable and inclusive learning environment.

For First Nations students and communities, the stakes are even higher. The Australian National AI Plan 2025 acknowledges that AI has manifested harms to First Nations people, including through perpetuating harmful stereotypes and the use, misattribution and falsification of First Nations cultural and intellectual property. Schools working with Aboriginal and Torres Strait Islander students have an obligation to consult with communities about AI use and to implement cultural protocols that respect Indigenous data sovereignty.

🔍 The Hard Questions Every School Needs to Answer
  • Do all of our students have equitable access to AI tools — in school and at home? If not, what are we doing about it?
  • What data about our students is being collected by AI platforms we’re using? Who owns it? Where is it stored? Have parents consented?
  • Are the AI tools we’re using culturally responsive? Do they reflect and respect the diversity of our student community?
  • What happens when a student without home internet access is competing with peers who used AI tools to complete the same assessment? Is that fair?
  • Who in our school is responsible for monitoring AI’s impact on student learning and wellbeing? Is anyone?

Who Is Watching? Research, Monitoring, and Accountability

One of the most honest things we can say about AI in education right now is that we don’t yet know enough. The technology arrived faster than the research could follow, and schools are being asked to make consequential decisions about its use without the evidence base that normally informs good educational policy.

There are some significant efforts underway. The OECD is actively researching AI’s impact on equity and inclusion. The Australian Government’s National AI in Schools Taskforce produced the national framework and conducts annual reviews. Researchers at Macquarie University, the University of Sydney, Monash, and the University of Newcastle are all actively publishing in this space. State-level developments include NSW’s EduChat and South Australia’s EdChat — government-built AI tools that may eventually replace commercial platforms in schools, giving governments far greater control over the data environment and the nature of AI interactions with students.

But there are also significant gaps. School students are younger, more vulnerable, and less equipped to critically evaluate AI outputs than university students. The potential harms — to wellbeing, to academic development, to equity — are arguably greater in K-12 than in higher education, yet the schools framework remains an aspirational document with limited regulatory teeth. We are, in many respects, running a large-scale experiment on children without fully understanding the variables.

Ban It or Embrace It? Why Both Are Wrong

The instinct to ban AI in schools is understandable. It feels protective. It feels like maintaining standards. And there are genuine risks worth taking seriously — academic integrity, cognitive offloading, data privacy, and the very real possibility that students are producing AI-generated work that neither they nor their teachers have learned anything from.

But banning AI in schools does not make it disappear from students’ lives. It simply pushes its use underground, removes any opportunity for schools to teach critical and ethical AI literacy, and — crucially — leaves students unprepared for a world in which AI fluency is rapidly becoming a foundational employment skill.

Equally, uncritical embrace of AI as an educational solution misses the point. A case can and has been made that AI has no place in education, and there may be some validity to this argument — these technologies were not developed for educational purposes and, in many ways, conflict with the values and purpose of education. The answer is neither ban nor blanket adoption. It is something harder: informed, principled, equity-conscious integration — guided by a clear understanding of what we are trying to achieve for every student.

That work starts with teachers. And it starts with conversations exactly like this one. In Part 2 of this series, we’ll look at what is actually happening in Australian and international schools right now — including some examples that will make you think, and some that might keep you up at night. Our earlier post on teaching strategies and active learning is also worth revisiting as you think about how AI changes the pedagogical landscape.

“The questions we should be asking about AI in education are not primarily technological. They are human. What is education for? Who does it serve? And what kind of future are we preparing children to inhabit?” — Adapted from Holmes et al., 2023

💬 Your Turn! What’s your school’s current position on AI — banned, embraced, or somewhere in between? And do you think that position is working? Share your experience in the comments — this is one of the most important conversations in education right now.

References

Bower, M., Hedberg, J., Kuswara, A., & Dalgarno, B. (2025). What generative artificial intelligence priorities and challenges do senior Australian educational policy makers identify (and why)? The Australian Educational Researcher. https://doi.org/10.1007/s13384-025-00801-z

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2023). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32, 504–526. https://doi.org/10.1007/s40593-021-00239-1

Lodge, J. M., Bower, M., Gulson, K., Henderson, M., Slade, C., & Southgate, E. (2025). Australian Framework for Artificial Intelligence in Higher Education. Australian Centre for Student Equity and Success. https://www.acses.edu.au

Marandi, R. (2025). Addressing student use of generative AI in schools and universities through academic integrity reporting. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1610836

National AI in Schools Taskforce. (2023, updated 2024). Australian Framework for Generative AI in Schools. Australian Government Department of Education. https://www.education.gov.au

Varsik, S., & Vosberg, L. (2024). The potential impact of artificial intelligence on equity and inclusion in education. OECD Artificial Intelligence Papers, No. 23. OECD Publishing. https://doi.org/10.1787/15df715b-en