Tips, Tricks and Resources for Teachers How Smart Teachers Plan Backwards

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

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.

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

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

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

Why “Good Job!” Isn’t Good Enough Feedback

We’ve all written it. That encouraging red-pen scrawl at the bottom of a student’s work: “Well done! Keep it up!” It feels kind. It feels supportive. But here’s the uncomfortable truth — it tells the student absolutely nothing about how to improve. And if improving is the point of education, we might need to rethink our feedback habits.

John Hattie and Helen Timperley’s (2007) landmark paper on feedback remains one of the most cited works in education research for good reason. They found feedback has an average effect size of 0.73 on student achievement — making it one of the most powerful influences on learning. But — and this is crucial — the type of feedback matters enormously. Vague, evaluative feedback (“good work”) has minimal impact. Specific, actionable feedback changes everything.

The 3 Feedback Questions Every Student Needs Answered

Hattie and Timperley’s model centres on three fundamental feedback questions. Every piece of effective feedback should address at least one of these:

🎯 The Three Feedback Questions

Where am I going? (Feed up) — What is the learning goal? What does success look like?
How am I going? (Feed back) — How is my current performance relative to that goal?
Where to next? (Feed forward) — What specific action will help me improve or extend my understanding?

Most teacher feedback addresses only the second question — and often vaguely. The most underused is the third: feed forward. This is where the real learning lives. For a broader overview of how feedback sits within the assessment cycle, see our earlier post on understanding assessment for, as, and of learning.

Written Feedback: Making Every Minute Count

Let’s be honest — marking 28 books on a Friday night is a special kind of exhaustion. So the question isn’t just “what makes feedback effective?” but “how do we make it sustainable?” Here are three approaches that work in real Australian classrooms. And if you’re still building your broader formative assessment toolkit, that earlier post is worth a read alongside this one.

1. The 2 Stars and a Wish. A classic for good reason. Two specific, evidence-based positives (“Your use of the word ‘cascade’ to describe the waterfall was a beautiful word choice — it sounds like what it describes”) and one specific, achievable next step (“Now try beginning your next sentence with a different sentence starter — can you avoid starting with ‘The’?”). Quick to write. Clear for students.

2. Coded Feedback + Class Discussion. Instead of writing the same comment 20 times, develop a simple code. Underline powerful word choices in green. Circle punctuation errors in orange. Then have a five-minute class debrief where you address the patterns you saw across the whole class. Targeted, efficient, and the group discussion deepens understanding.

3. Verbal Feedback Stamp. A rubber stamp that says “Verbal Feedback Given” or a VF notation in your mark book. Stop and have a one-minute conversation with the student — face-to-face feedback is often more powerful than written feedback anyway. This counts as rich assessment evidence under AITSL standards.

Peer Feedback: Don’t Underestimate the Power of 9-Year-Olds

Peer feedback, when taught explicitly, can be extraordinarily powerful — and it’s directly aligned with the Australian Curriculum’s Personal and Social Capability strand. But the key word is taught. You cannot simply say “swap books and give each other feedback.” Students need sentence starters, they need a clear rubric or checklist, and they need to practise in low-stakes situations before applying it to valued work.

A simple scaffold: “I noticed that your writing… [observation]. This worked well because… . One thing that might make it even stronger is… [suggestion].” Model it. Practise it together. Then watch students take genuine pride in helping each other grow.

The Feedback Loop: Closing the Circle

Here’s the question most teachers forget to ask: did the student actually act on the feedback? Dylan Wiliam (2011), whose work on formative assessment is widely used in Australian schools, argues that feedback only improves learning when students have the opportunity to use it. If you mark work on Friday and move on to a new topic Monday, the feedback loop never closes. Build in “feedback response time” — even 10 minutes at the start of the following lesson where students read, reflect, and act on your comments.

“The most important thing about feedback is what happens next. If nothing changes, it wasn’t feedback — it was just a comment.” — Adapted from Wiliam, 2011

A Final Note for Preservice Teachers

When you’re observed on prac, assessors are looking for evidence of AITSL Standard 5: Assess, provide feedback and report on student learning. They want to see that your feedback is purposeful — connected to your learning intentions, specific to the student, and genuinely designed to move their learning forward. “Good work!” won’t cut it. A targeted, warm, specific comment? That’s the mark of a reflective, professional teacher.

When it comes to designing tasks that make feedback truly meaningful, it’s also worth reading our post on creating a performance of worth — summative tasks that give students something genuinely worth their best effort, and give you something genuinely worth your best feedback.

💬 Your Turn! What’s your go-to feedback strategy? Have you tried coded feedback or verbal feedback stamps? We’d love to hear what works in your classroom — share in the comments below!

References

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

Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. https://doi.org/10.1080/0969595980050102

Clarke, S. (2008). Active learning through formative assessment. Hodder Education.

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

Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487

Wiliam, D. (2011). Embedded formative assessment. Solution Tree Press.

You Asked a Great Question – Now What?

Last week we explored the difference between lower-order and higher-order questions — and why the questions we ask signal the depth of thinking we’re demanding from our students. If you missed it, you can read that post here. But there’s a sequel to that conversation that doesn’t get nearly enough attention: what do you actually do with the answers?

Asking a great HOTS question is only half the job. The other half — the half most of us were never explicitly taught — is reading what those responses tell you in real time, and having both the skill and the confidence to change direction when the room is telling you something isn’t landing. Think of it like a football coach at three-quarter time: the game plan was perfect on paper, but the scoreboard is telling a different story. Great coaches adjust. Great teachers do too.

Why “Pushing Through” Costs More Than You Think

Be honest: how many times have you been twelve minutes into a lesson — one you spent two hours planning — and sensed the room slipping? Eyes glazing. The same three hands going up again and again. Bodies turning sideways. And you kept going anyway, hoping it would click by the end?

Most of us have. The instinct to push through is understandable — you’ve planned this, the content needs covering, and pivoting mid-lesson can feel like losing ground. But the research tells a clear story about what we’re sacrificing when we do.

Black and Wiliam’s landmark 1998 review of over 250 studies established that improving the quality of in-class, moment-to-moment assessment substantially raised student achievement — far more than end-of-unit tests or term benchmarks. The greatest lever on learning, they found, is what happens between teacher and student inside the lesson, right now. Wiliam (2011) later gave this a name: “contingent teaching” — instruction that is genuinely responsive to what students are doing, rather than proceeding through a predetermined script regardless of what’s actually happening in the room. It is the opposite of pushing through.

John Hattie’s Visible Learning synthesis reinforces the point, placing formative evaluation at an effect size of 0.90 — nearly double the threshold he considers educationally meaningful (Hattie, 2009). The critical caveat, as both Hattie and Wiliam emphasise, is that information you collect but don’t act on is just noise. Reading the room is what transforms that information into learning.

“Teachers need to know about the learning, and they need to know about it while it is still happening, while there is still time to do something about it.” — Wiliam, 2011, p. 50

What “Reading the Room” Actually Looks Like

Reading the room is not a mysterious sixth sense that experienced teachers develop after decades in the classroom. It is a set of observable, learnable signals — and like any professional skill, it sharpens with deliberate practice. Here are the four most reliable ones to watch for, with questions at the heart of each:

🔍 The 4 Signals to Watch For

Participation patterns. Who is answering your questions? If the same three students are carrying every response, the other 25 are passengers — not learners. Equity of voice is a real-time diagnostic. When only a handful of students are engaged, the rest have quietly checked out.
Body language clusters. One student leaning back is a data point. Five students leaning back simultaneously is a trend worth acting on. Train yourself to scan for clusters, not outliers — the pattern across the room is what matters.
The nature of student questions. When students are genuinely with you, their questions push the learning forward — they want to know more, go further, explore the edges. When they’re lost, their questions go backward — asking you to repeat what you just said, or falling silent altogether. In an Australian primary classroom, prolonged silence in response to a question is rarely a sign of deep thinking. It usually means students have stopped trying to keep up.
The gap between expected and actual output. You asked for three sentences applying a new concept. Most students wrote one sentence restating the definition. That gap between what you anticipated and what arrived on the page is perhaps the most reliable signal of all — and it pinpoints precisely where the thinking broke down.

Planning for the Pivot Before You Walk In the Door

Here’s a planning insight that changes everything: most lesson plans are written entirely around success. Here’s the hook, here’s the content, here’s the activity, here’s the close. Very few lesson plans include the equally important contingency: if this isn’t landing, here is what I will do instead. If you’d like to revisit the fundamentals of lesson planning, our earlier post on planning the teaching-learning cycle is a great starting point.

The fix is not complicated. Before each lesson, spend three minutes identifying one or two simple moves you can make if the room signals confusion. Not dramatic overhauls — small, targeted adjustments that take two to three minutes and redirect the learning without abandoning the lesson entirely. Dylan Wiliam (2011) refers to this kind of prepared responsiveness as a hallmark of truly expert teaching, and it is a skill that can be built systematically. Here are three go-to moves worth having in your toolkit:

⏸ 3 Mid-Lesson Pivot Moves

Move 1 — Turn and Talk with Purpose. Stop the delivery. Give students two minutes to turn to a partner and share: what makes sense so far, and what’s still fuzzy? This is not casual chat — you circulate and listen. What you overhear in those two minutes tells you exactly where to pick back up and what needs re-teaching. It’s Think-Pair-Share with a diagnostic purpose built in, and it re-engages students who have drifted by giving them a low-stakes way back into the learning.
Move 2 — The Single Diagnostic Question. This is where your Blog 1 toolkit pays off directly. Pose one carefully chosen HOTS question that requires students to apply what you’ve just taught — not recall it. Crucially, ask students to write their answer before anyone shares aloud. Mini whiteboards are ideal: every student holds up a simultaneous response and you scan the room in under ten seconds. You are not looking for the right answer — you are looking for the pattern in the wrong answers, because that pattern reveals precisely where understanding broke down and where your next teaching move needs to target.
Move 3 — Back Up and Re-Enter. When you recognise that the room is genuinely lost, resist the urge to push forward faster or layer more explanation on top of existing confusion. Instead, mentally rewind: identify the last point in the lesson where students were demonstrably with you, back up to that point, and re-enter it with a different approach — a concrete example, a visual, a brief hands-on activity, a different analogy. This takes real confidence, because it feels like losing ground. It is not. Pressing on over a shaky foundation is what loses ground.

What This Looks Like in an Australian Primary Classroom

You’re mid-way through a Year 4 HASS lesson on how laws are made in Australia. You’ve explained the legislative process and now you throw out the HOTS question from your board: “If you disagreed with a new law, what could you do about it — and which option do you think would be most effective, and why?” Students write their responses on mini whiteboards and hold them up.

You scan the room in ten seconds. Almost everyone has written some version of “vote.” Barely anyone has mentioned advocacy, community campaigns, petitions, protest, or contacting their local Member of Parliament. The content went in — but the thinking hasn’t gone deep enough yet.

That’s your signal. You don’t push to the next slide. Instead, you put a real example on the board — perhaps the long campaign by Indigenous Australians and their allies to change the date of the national public holiday — and ask: “What did people actually do here? Which of these strategies seemed to make a difference, and why?” You’ve backed up, re-entered at the right level, and now the HOTS question has genuine traction. The adjustment took three minutes. The depth of learning it unlocked was worth every second.

Building It Into Your Reflective Practice

Like any professional skill, mid-lesson responsiveness gets sharper the more deliberately you practise it. One of the most effective ways to build it is through a brief post-lesson reflection — three questions, two minutes, straight after the bell: At what point did I first notice the room wasn’t with me? What did I do in response? What might I have done differently? Over time, this kind of structured reflection builds pattern recognition. You start noticing the signals earlier. Your pivot repertoire grows. The lag between sensing something is off and doing something about it shrinks — and that is what separates responsive teaching from reactive teaching.

For preservice teachers, this matters beyond the classroom too. AITSL Standard 3.3 requires that you demonstrate the use of teaching strategies that respond to the learning strengths and needs of students, and Standard 5.4 asks you to interpret student assessment data to evaluate student learning. A diagnostic question mid-lesson, read carefully and acted upon, is live evidence of both — and among the most convincing things an assessor can observe on a prac visit. For a broader toolkit of responsive teaching strategies, see our post on teaching strategies worth having in your repertoire.

“Expert teachers monitor learning constantly and use this information to adjust their instruction. They treat the lesson as a hypothesis to be tested, not a script to be delivered.” — Adapted from Hattie & Yates, 2014

💬 Your Turn! Have you ever deliberately stopped a lesson mid-stream and changed direction? What was the signal that told you it was time? Share your story below — your experience might be exactly what another teacher needs to hear 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 3.3 & 5.4). https://www.aitsl.edu.au/teach/standards

Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. https://doi.org/10.1080/0969595980050102

Black, P., Harrison, C., Lee, C., Marshall, B., & Wiliam, D. (2004). Working inside the black box: Assessment for learning in the classroom. Phi Delta Kappan, 86(1), 8–21. https://doi.org/10.1177/003172170408600105

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

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

Leahy, S., Lyon, C., Thompson, M., & Wiliam, D. (2005). Classroom assessment: Minute by minute, day by day. Educational Leadership, 63(3), 18–24.

Wiliam, D. (2011). Embedded formative assessment. Solution Tree Press.

Wiliam, D., & Leahy, S. (2015). Embedding formative assessment: Practical techniques for K–12 classrooms. Learning Sciences International.