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4 Proven Ways to Make Thinking Visible That Build Trust

Teaching in the Digital Age · Keep the Thinking Human (Part 3)

4 Proven Ways to Make Thinking Visible That Build Trust

You can’t detect your way to trust. Make thinking visible while students work, and the AI question mostly answers itself.

⏱ 6 min read · By Matt Faulkner · Pedagogy / Gen AI

A Year 6 student hands in a persuasive text that reads better than anything she has written all term. You feel the question rise before you can stop it. Did she write this?

That moment is now common, and it is corrosive. A Harvard Project Zero report in October described a two-way suspicion in classrooms. Teachers suspect students of using AI. Students fear being accused whether they used it or not. Both sides lose the trust that learning depends on.

This final part of Keep the Thinking Human takes a different route. Part 1 named the AI shortcuts that cost learning, and Part 2 set clear AI rules task by task. Part 3 shows how to make thinking visible while it happens. That way, the finished product stops being your only evidence.

Why Detection Is the Wrong Game

AJ Juliani puts it bluntly: he refuses to play the detection game, because it is a losing battle. AI detectors are unreliable, and every false accusation costs a relationship.

Two research groups reached the same view this year. The Project Zero team called for open conversation about AI use, not a better detector. MIT’s committee on AI in teaching went further. It recommended against relying on detectors at all. It also warned of an arms race with tools that rewrite AI text to slip past them.

The bigger risk is who gets flagged. MIT notes that detectors risk misreading writing by non-native English speakers and neurodivergent students. In an Australian classroom, that puts EAL/D learners first in line for a false accusation. Staff also told the committee that policing AI was wearing down their relationships with students. The report calls the result an “underground river of mutual suspicion.”

Juliani’s fix is practical. Change what you look at, from the product to the process.

Primary teachers have an advantage here. We see our students all day. We already watch them work. The four ways below build on what good formative assessment already does.

Way 1: Confer While They Work

The simplest way to see thinking is to ask about it mid-task. Juliani uses short one-on-one conversations while students are still working. A two-minute roving conference tells you more than any finished draft.

Keep the questions short and open. “Talk me through what you’ve done so far.” “What was the hardest decision?” “What will you do next?” A student who did the thinking answers easily. A student who didn’t will stall, and that is information, not an accusation.

For example, a Year 4 student writing a procedure can tell you why step three comes before step four. That answer shows understanding the page alone never could.

Way 2: Ask Students to Explain Out Loud

Juliani also asks students to record verbal explanations of their work. In a primary classroom, this fits tools many schools already use. A 60-second Seesaw audio note on a maths strategy is quick to make and quick to hear.

Ron Ritchhart and his Project Zero colleagues built a whole approach on this idea. Their thinking routines, such as See, Think, Wonder, give students a simple structure for showing how they reason. In Making Thinking Visible, they argue that thinking becomes teachable once students can see and hear it.

🎙️ Five Prompts That Make Thinking Visible

“How did you start?” Shows the first decision, which AI never makes for a student.
“What did you try that didn’t work?” Dead ends are proof of real thinking.
“Why this word, number or example?” Asks for the reason behind one choice.
“What would you change now?” Shows whether the student can judge their own work.
“Teach it to a Year 2 student.” Simplifying a concept needs real understanding.

Way 3: Give Feedback Students Use Mid-Task

Juliani names this his most important change. He asks students to keep notes while they work, then gives feedback they use before the task is finished. If AI produced the product, he never gets the chance to give useful feedback at all.

Draft history shows the process as well. MIT’s committee suggests handwritten early drafts in class, regular checkpoints and version history as evidence of process. In primary, that looks like the edit history in a Google Doc or a run of saved drafts in Seesaw. You watch the work grow, one step at a time.

There is a trust dividend here too. Henderson and colleagues (2025) surveyed 6,960 students at four Australian universities. About 90% rated teacher feedback as trustworthy. Only about 60% said the same of AI feedback. Students valued feedback from someone who knew their work and their context.

Students still trust the teacher who watched them work. That trust is worth more than any detector.

In practice, this means feedback in the middle of a task, not just at the end. A sticky note with one next step, given during writing time, is feedback a student acts on today. Our post on why “good job” isn’t good enough feedback has more on making that note count.

Way 4: Invite Students to Defend Their Learning

Juliani’s last move is to ask students to defend their learning once they finish a product. The framing matters. It is an invitation to share the learning, not an accusation of AI use.

In primary, a defence can be gentle and short. Try a sharing circle where each child explains one choice they made. Alternatively, run a learning expo where families ask questions about the work. We wrote about this kind of real audience when making the general capabilities real.

Students who know they will talk about their work prepare differently. They make choices they can explain. Over time, that changes how they use any tool, AI included.

Make Thinking Visible, and Keep the Thinking Human

Across this series, the thread is simple. Name the skill a task builds. Keep AI out of the step that builds it. Then watch the thinking happen, instead of guessing about it afterwards.

🪜 One Week to Make Thinking Visible

Monday: Choose one task and fill in the AI Rules Card from Part 2.
Tuesday to Thursday: Run three two-minute conferences each lesson.
Wednesday: Collect one 60-second audio explanation from every student.
Friday: Hold a sharing circle where each child defends one choice.

None of these moves needs new software. They need time, which is the hardest resource in any school. Even so, time spent watching students think is time you no longer spend wondering who did the work.

Your turn

Which of these four ways already happens in your classroom, and which one will you try first? Tell us in the comments, along with your favourite question for a two-minute conference.

References

Henderson, M., Bearman, M., Chung, J., Fawns, T., Buckingham Shum, S., Matthews, K. E., & de Mello Heredia, J. (2025). Comparing generative AI and teacher feedback: Student perceptions of usefulness and trustworthiness. Assessment & Evaluation in Higher Education. Advance online publication. https://doi.org/10.1080/02602938.2025.2502582

Juliani, A. J. (2026a, October 12). The kids still want to learn from humans. Next Gen Schools. ajjuliani.beehiiv.com

Juliani, A. J. (2026b, October 14). My simple strategy for dealing with AI slop as a teacher. Next Gen Schools. ajjuliani.beehiiv.com

Massachusetts Institute of Technology Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training. (2026, August 13). Report. aiandeducation.mit.edu

Nagelhout, R. (2026, October 6). The AI cheating crisis in schools isn’t (only) what you think. Harvard Graduate School of Education. gse.harvard.edu

Ritchhart, R., Church, M., & Morrison, K. (2011). Making thinking visible: How to promote engagement, understanding, and independence for all learners. Jossey-Bass.

4 Proven Ways to Set AI Rules That Protect Learning

Teaching in the Digital Age · Keep the Thinking Human (Part 2)

4 Proven Ways to Set AI Rules That Protect Learning

Blanket AI rules never fit every task. Set them one task at a time, and tell students why.

⏱ 6 min read · By Matt Faulkner · Pedagogy / Gen AI

Last week we looked at five AI shortcuts that cost learning. Brainstorming, research, summaries and first drafts all feel harmless. Each one can skip the thinking a task was built to teach.

That leaves a practical problem. Most school AI policies list what students may and may not do. Very few tell a classroom teacher how to decide for a given task. Good AI rules come from that decision, not from a list on the staffroom wall.

The gap is real. A recent Harvard Project Zero report found only 18% of US teachers said their school had a specific AI policy. Australian schools have the national Framework, but the task-level call still lands on you. Here are four ways to make that call well.

Way 1: Name the Skill Before You Set AI Rules

Daniel Willingham puts the principle in one line. Students should only use AI for things they already know how to do well. He compares it to calculators. Once a child has mastered multiplication, a calculator in an area problem costs nothing. While they are still learning to multiply, it costs everything.

So start every task by naming the skill it builds. Write it in one sentence on your plan. “This task is about choosing the strongest evidence.” “This task is about explaining how I solved it.” That sentence decides the rule before the rule is written.

This is backward design applied to AI. You decide what students must be able to do, then protect the steps that build it. The Australian Framework for Generative AI in Schools asks for the same thing. Guiding statement 1.5 asks that student work clearly outline how AI should or should not be used.

Way 2: Sort Each Task Into Without, With or About AI

AJ Juliani borrows a simple frame from Paul Alivisatos, president of the University of Chicago. Students need to think without AI, with AI and about AI. Each task sits mostly in one of those three lanes.

In a primary classroom, most tasks belong in the first lane. Students in Years 3 to 6 are still building the knowledge and skills that AI would replace. The other two lanes still have a place, as long as you choose them on purpose.

🚦 Three Lanes for AI Rules

Without AI: The task builds a core skill. Think narrative planning, written explanations of maths strategies and first drafts.
With AI: The student already does the core skill well. AI handles a side job, like generating practice questions you have checked.
About AI: AI is the topic. Students check an AI answer, compare two outputs or ask who made the tool and why.

The third lane is where our earlier post on catching AI mistakes fits. Thinking about AI often works best with the teacher running the tool on the board. Many tools set age limits above primary, so a teacher-led demo also keeps you on the right side of them.

Way 3: Teach Where AI Works and Where It Doesn’t

Rules land better when students understand the reasoning. Juliani recently shared a lesson opener from neuroscientist Thomas Henning. Henning asked a class what they would need to know to guess someone’s height. Students answered fast: age, parents’ heights and so on.

Then he asked what they would need to know to judge how charismatic someone is. The room slowed down. Charisma has too many parts to measure. In Henning’s view, AI does best on problems with few parts that are easy to check. Code and maths answers are good examples.

By contrast, AI is weaker where quality is hard to measure. Kindness, leadership and taste all sit in that space. For a primary version, try two questions side by side. “What is 48 times 25?” versus “Which ending makes our class story better?” Ask students which one AI would handle well, and how they would check it.

Students who understand that difference start to ask the right question on their own. That habit ties straight back to the AI confidence trap.

Way 4: Say the Rule Out Loud, and Model Your Own Use

Juliani tells his students that letting AI do their learning is like sending a robot to the gym for you. The robot gets the workout. You get nothing. It is funny, and that is why students remember it.

He also states the rule for every assignment without shaming anyone. This task is about X. If a chatbot does X, you miss the learning. Students who know the rule upfront have no grey area to hide in.

Modelling matters too. When you use AI, say what it did and what you did. “I used AI to make 10 practice questions. I checked each one and fixed two.” Students see AI as a tool you control, not a tool that does the thinking.

An AI rule without a reason feels like a ban. An AI rule with a reason feels like a plan for learning.

Where students do use AI, choose tools that hint rather than answer. In the Bastani study from Part 1, a tutor version that gave hints largely avoided the learning loss. The open chatbot did not.

An AI Rules Card for Every Task

Put the four ways together and you get a short card for each task. Write it on the task sheet, the board or the top of the Seesaw activity.

✏️ The AI Rules Card

This task builds: one skill, in one sentence.
AI lane: without, with or about.
AI may help with: the side jobs, if any.
AI must not: the step that builds the skill.
Why: one line students can repeat back to you.

Clear AI rules solve half the problem. The other half is knowing whether the thinking really happened. Next week, Part 3 looks at how to see student thinking as it happens, so you can stop playing detective.

Your turn

Pick one task you are setting this week and fill in the AI Rules Card. Which lane did it land in? Share your card in the comments.

References

Australian Government Department of Education. (2023). Australian framework for generative artificial intelligence (AI) in schools. education.gov.au

Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), e2422633122. https://doi.org/10.1073/pnas.2422633122

Juliani, A. J. (2026a, October 12). The kids still want to learn from humans. Next Gen Schools. ajjuliani.beehiiv.com

Juliani, A. J. (2026b, October 14). My simple strategy for dealing with AI slop as a teacher. Next Gen Schools. ajjuliani.beehiiv.com

Juliani, A. J. (2026c, October 20). What my students learned about AI and cognitive science from a neuroscience PhD. Next Gen Schools. ajjuliani.beehiiv.com

Nagelhout, R. (2026, October 6). The AI cheating crisis in schools isn’t (only) what you think. Harvard Graduate School of Education. gse.harvard.edu

Willingham, D. (2026, August 27). Students should only use AI for things they already know how to do well. Thomas B. Fordham Institute. fordhaminstitute.org

5 Proven Ways to Spot AI Shortcuts That Cost Learning

Teaching in the Digital Age · Keep the Thinking Human (Part 1)

5 Proven Ways to Spot AI Shortcuts That Cost Learning

The riskiest AI shortcuts look harmless. They skip the exact thinking the task was built to teach.

⏱ 6 min read · By Matt Faulkner · Pedagogy / Gen AI

A Year 5 student is stuck on a narrative. She asks an AI tool for story ideas and gets 20 in four seconds. She picks one, writes it up, and hands it in. Nobody cheated. The AI never wrote a word of her story.

So what was lost? The answer is the part of the lesson that mattered most. Generating ideas, sorting weak ones from strong ones, and backing a choice is the skill. Those quiet AI shortcuts rarely look like cheating, which is why they slip past us.

This post opens a three-part series called Keep the Thinking Human. Part 1 names five common AI uses that feel safe but cost learning. Part 2 shows how to set clear AI rules task by task. Part 3 covers how to see student thinking without playing detective.

The One Test Behind Every AI Shortcut

Cognitive scientist Daniel Willingham offers a simple filter. Most school AI policies treat the student as the lead and AI as the assistant. That works in a workplace, where the product is the point. In a classroom, the product is rarely the point.

Nobody needs another Year 4 information report about frogs. We set the task because writing it builds thinking we care about. For that reason, Willingham suggests one question before any AI use. Is doing this part yourself still part of what you need to learn?

If the answer is yes, AI is taking the learning, not saving time. With that test in mind, here are five AI shortcuts that fail it more often than they pass.

Shortcut 1: Brainstorming With AI

Brainstorming looks like the safest job to hand over. In fact, the research is mixed in a way worth knowing. Doshi and Hauser (2024) found writers given AI ideas produced stories judged more creative. Even so, those stories were more alike than stories written without AI.

A 2025 study by Zhou and colleagues followed what happened next. Participants did better on creative tasks while they had ChatGPT. Once the tool was removed, their creativity dropped away. Their ideas also kept getting more alike for months afterwards. The researchers called this a creative scar.

In practice, keep the messy first round human. Give pairs three minutes and a sticky-note goal of 10 ideas. Bad ideas count. The sorting that follows is where children learn what makes an idea strong.

Shortcut 2: Letting AI Find the Main Idea

Secondary teachers call it the thesis. In primary, it is the topic sentence or the main idea of a paragraph. Either way, it is the sentence a student has to think hardest to write.

High school English teacher Chanea Bond let her students choose. They could write their own thesis or feed their notes to AI for one. The students who used AI wrote weak essays. They lacked the skills to turn AI output into something worth reading.

For example, a Year 4 student writing about echidnas needs to decide what matters most. That decision is the learning. A main idea handed to them skips it, and the paragraphs that follow show it.

Shortcut 3: Using AI to Do the Research

Research is where AI feels most like a librarian. In reality, it often pulls from whatever pages it finds and presents them with equal confidence. For a student still learning to judge sources, every answer looks equally solid.

The bigger loss is the skill itself. Choosing a source, checking it and comparing two of them is what research teaches. We covered how to teach students to catch AI mistakes earlier this term. That only works once students know how to check a claim without the tool.

A useful middle ground comes from Benjamin Barbour, a US high school teacher and librarian. His students may use AI to find weak spots in their own plan. They must still talk fluently about their research to an audience. That expectation keeps the learning with the student.

Shortcut 4: Reading an AI Summary

Students love a tidy summary. A photo of their notes becomes a neat page of dot points in seconds. Even so, reading a summary is passive, and it gives students a false sense that they understand.

The evidence for doing it yourself is strong. Lawson and Mayer (2021) compared students who wrote their own explanation with students who read one. On a delayed test, the writers did better. Writing the explanation forces students to pull ideas from memory and reorganise them.

In other words, the summary is not the goal. The act of making it is. A two-minute “write what you remember” before students check their notes does far more than any polished AI summary.

Shortcut 5: Starting From an AI First Draft

The blank page is hard, so an AI first draft feels kind. The evidence says otherwise. Bastani and colleagues (2025) gave nearly 1,000 high school maths students access to GPT-4 during practice. Their practice scores rose by 48%.

Then the researchers removed access for the exam. Those students scored 17% lower than peers who never had AI. Students had used it as a crutch. By contrast, a tutor version that gave hints instead of answers largely avoided the drop.

Students using AI looked better at practice and turned out worse at the test. That gap is the cost of a shortcut.

Writing works the same way. A first draft is where a student finds out what they think. Hand that over, and the student edits someone else’s thinking instead of building their own. This links straight back to designing for productive struggle.

How to Check Any Task for AI Shortcuts

None of this means AI has no place in your room. It means the job you give it matters. Before any task, run it past the questions below.

🔍 The AI Shortcut Check

What skill is this task for? Name it in one sentence before deciding anything else.
Which step builds that skill? Brainstorming, choosing, drafting or explaining are the usual suspects.
Would AI do that step for them? If yes, keep AI out of that step.
Can they already do it well? If a student has mastered the step, AI help costs far less.
Will you see their thinking? If the only evidence is the product, plan a way to see the process.

Most students will not choose the harder path on their own, and that is human nature, not laziness. That is why the decision sits with us. As we saw with fake engagement, a finished product can hide shallow thinking.

Next week, Part 2 turns the check into a routine. We look at how to set clear AI rules for each task. We also cover how to explain them so students follow the logic.

Your turn

Which of these five AI shortcuts have you already seen in your classroom? Tell us in the comments, along with one task you plan to keep AI-free this term.

References

Doshi, A. R., & Hauser, O. P. (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(28), eadn5290. https://doi.org/10.1126/sciadv.adn5290

Lawson, A. P., & Mayer, R. E. (2021). Benefits of writing an explanation during pauses in multimedia lessons. Educational Psychology Review, 33(4), 1859–1885. https://doi.org/10.1007/s10648-021-09594-w

Tutt, P., & Merrill, S. (2026, October 16). 5 ‘good’ AI use cases that are actually bad for students. Edutopia. edutopia.org

Willingham, D. (2026, August 27). Students should only use AI for things they already know how to do well. Thomas B. Fordham Institute. fordhaminstitute.org

Zhou, Y., Liu, Q., Huang, J., & Li, G. (2025). Creative scar without generative AI: Individual creativity fails to sustain while homogeneity keeps climbing. Technology in Society, 103087. https://doi.org/10.1016/j.techsoc.2025.103087

4 Proven Ways to Build Student Persistence With Stories

Core Pedagogy · Motivation

4 Proven Ways to Build Student Persistence With Stories

Success stories show students what is possible. Struggle stories build student persistence by showing what to do next.

⏱ 6 min read · By Matt Faulkner · Pedagogy / New Teachers

You know the moment. A Year 4 student hits a tricky division problem, the pencil stops and their head drops onto the desk. “I’m not a maths person.”

What happens in the next two minutes decides whether that struggle turns into learning. We have written before about designing tasks for productive struggle. This post tackles the other half. How do you build student persistence so children keep going once the struggle arrives?

Todd Finley’s recent Edutopia piece points to a simple, research-backed tool. Tell students true stories about people who struggled before they succeeded. Done well, struggle stories lift persistence. Done badly, they turn into another poster slogan, much like the general capabilities we looked at last week.

What the Research Says About Student Persistence

The evidence starts surprisingly young. In a 2022 study, Amanda Haber and colleagues read four- and five-year-olds a storybook about Albert Einstein or Marie Curie. Some versions described the scientist’s intellectual struggles. Others described struggles in their personal life. Then each child faced a puzzle with no solution.

Children who heard a struggle story kept trying for around 74 to 79 seconds. By contrast, children who heard neither struggle story gave up after about 55 seconds. A 2024 follow-up with preschoolers and their caregivers found a similar boost.

Older students respond too. In a well-known 2016 study, Xiaodong Lin-Siegler and her team gave high school students stories about famous scientists.

Students who read about the scientists’ struggles earned better science grades afterwards. In contrast, students who read achievement-only stories saw their grades drop. The effect was small, yet strongest for lower-performing students.

Achievement-only stories tell children success comes naturally. Struggle stories show them what to do when it doesn’t.

Way 1: Choose Stories Students Can See Themselves In

Not every role model works. Gladstone and Cimpian reviewed 55 studies of STEM role models in 2021. The role models who motivated students had three things in common. They were competent, students saw them as similar to themselves, and their success felt within reach.

On the other hand, extreme success can backfire. A child struggling with spelling may feel further away, not closer, after hearing about a Nobel Prize winner. At the same time, role models from under-represented groups had broad positive effects. That held for students of all backgrounds.

In practice, mix the famous with the familiar. A Year 6 student who finally cracked long division. The school gardener who retrained in their 40s. An Aboriginal or Torres Strait Islander community member your school already works with. You, learning to play guitar at 35. Familiar faces close the gap between “them” and “me.” That sense of “someone like me” is where student persistence starts.

Two cautions from Finley are worth keeping. Skip the “anyone can do anything” line, because children know it isn’t true. And when poverty or racism shaped someone’s struggle, don’t suggest persistence alone beat it.

Way 2: Name the Move, Not the Trait

“She never gave up” describes a trait. Children can admire a trait, but they can’t copy it. Instead, name the specific move the person made.

For example, picture a Year 5 student who couldn’t learn her times tables by chanting them. So she made her own flashcards and tested her brother at dinner every night. That is a move another child can borrow tomorrow. Borrowed moves like this are the building blocks of student persistence.

The research backs this up. In one 2016 study, students struggled to pick out a role model’s actual strategies. So researchers asked them to write a short learning contract with two actions they would try in class.

A primary version is a “When I’m Stuck” card on each desk. Students add one strategy from each story they hear.

This connects with the mediated learning approach in our Feuerstein and growth mindset post. Effort only becomes useful once a child knows what to do differently.

Way 3: Pause the Story at the Hard Part

Finley suggests the interrupted narrative. Tell the story up to the point where everything goes wrong, then stop. Ask the class what they would try next. Only then reveal what the person did.

That pause turns students from listeners into problem-solvers. As a result, they rehearse the decision before they ever need it. That rehearsal builds student persistence before the hard moment arrives. Most good struggle stories also follow the same shape, so teach students to spot it.

🪜 The Struggle Story Arc

Struggle: What went wrong?
Analysis: What did they notice about why it went wrong?
Strategy: What did they decide to try?
Action: What did they do, step by step?
Result: What happened, including the parts that still didn’t work?

Way 4: Debrief, Then Hand Over the Struggle

A story without a debrief is entertainment. Finley offers three short questions to use after each one. What did the failed attempt reveal? What did the person change or learn? Which strategy could help with the task we are working on now?

Then comes the step that matters most. A borrowed story can’t replace a child’s own success. Lasting student persistence grows from a child’s own small wins. Follow the story straight away with a task at the right level of challenge. Then give students the support to try the strategy. Our post on fading the scaffold covers how to pull that support back as students grow more confident.

Build Student Persistence in Ten Minutes a Week

You don’t need a new program. One short story a week, told well, builds a class bank of strategies by the end of term. For younger students, picture books work well. Ashley Spires’ The Most Magnificent Thing follows a girl whose invention keeps failing until she steps away and comes back.

💡 A Ten-Minute Struggle Story Routine

2 minutes: Tell the story up to the hard part.
2 minutes: Pairs predict what the person tried next.
2 minutes: Finish the story.
3 minutes: Debrief with the three questions.
1 minute: Each student adds one strategy to their “When I’m Stuck” card.

Your turn

What is your own struggle story, the one your students would never guess? Share it in the comments, along with the move that got you through. It might become someone else’s Monday lesson.

References

Finley, T. (2026, October 1). A research-based strategy for guiding students to engage in productive struggle. Edutopia. edutopia.org

Gladstone, J. R., & Cimpian, A. (2021). Which role models are effective for which students? A systematic review and four recommendations for maximizing the effectiveness of role models in STEM. International Journal of STEM Education, 8, Article 59. https://doi.org/10.1186/s40594-021-00315-x

Haber, A. S., Kumar, S. C., & Corriveau, K. H. (2022). Boosting children’s persistence through scientific storybook reading. Journal of Cognition and Development, 23(2), 161–172. https://doi.org/10.1080/15248372.2021.1998063

Lin-Siegler, X., Ahn, J. N., Chen, J., Fang, F.-F. A., & Luna-Lucero, M. (2016). Even Einstein struggled: Effects of learning about great scientists’ struggles on high school students’ motivation to learn science. Journal of Educational Psychology, 108(3), 314–328.

Spires, A. (2014). The most magnificent thing. Kids Can Press.

5 Proven Ways to Make the General Capabilities Real

Core Pedagogy · Curriculum

5 Proven Ways to Make the General Capabilities Real

Every school has the general capabilities poster. Far fewer can show you the promise behind it.

⏱ 6 min read · By Matt Faulkner · Pedagogy / New Teachers

Walk into almost any Australian primary school and you will find the poster. Seven icons, seven labels: literacy, numeracy, critical and creative thinking and the rest. The general capabilities sit on the staffroom wall, in the planning template and in the annual report.

Now ask a Year 4 student what any of them meant for their Tuesday. Most will shrug.

AJ Juliani spotted the same pattern in the US craze for a “Portrait of a Graduate.” By his count, around 70% of districts in one large US network now have a portrait. Even so, few can show you what changed for a child because of it. His fix is a line worth borrowing: a portrait is a picture, a goal is a promise. This post turns his five promises into an Australian primary version.

What the General Capabilities Already Promise

The promise is already in writing. In December 2019, every Australian education minister signed the Alice Springs (Mparntwe) Education Declaration. In particular, its second goal names three outcomes. All young Australians become confident and creative individuals, successful lifelong learners, and active and informed members of the community.

The Australian Curriculum turns that goal into seven general capabilities. ACARA is clear that they are not separate subjects. Instead, students develop them through the content of each learning area. Five of the seven also come with a learning continuum.

That design makes sense. Even so, it leaves a gap. A capability taught “through everything” is easy to assume and hard to see. As a result, the icons go on the planner and nobody checks whether every child had the experience.

Way 1: Promise Every Child Goes Deep on Something

Juliani’s first promise is depth in at least two areas. To make the case, he draws on David Epstein’s Range. Breadth matters, yet students also need to know what real expertise feels like from the inside.

In a primary room, depth looks like one sustained inquiry where a child becomes the class expert. For example, picture a Year 3 student who spends five weeks on local frogs. She keeps a field journal, counts calls at dusk and teaches the class what she found. In other words, that one project draws on critical and creative thinking, literacy, numeracy and personal and social capability.

The student choice structures we covered in August, such as genius hour, give you a ready frame.

Way 2: Promise Every Child Is Known by an Adult

Second, every student should be known well by at least one adult at school. After all, personal and social capability grows through relationships. A child struggles to practise self-awareness with an adult who never asks.

In practice, this takes ten minutes. First, list every student in your class. Next to each name, write one thing they care about outside school. Any blank line is a child nobody knows well yet. Fix the blanks first.

Way 3: Promise Work for a Real Audience

Third, every student completes a longer project for an audience beyond the teacher. We called this a performance of worth back in 2021, and the idea still holds.

Katie Martin works with school systems on learner-centred redesign. On Juliani’s Next Gen Schools podcast, she named student presentations of learning as her biggest lever for change. In her experience, reluctant teachers watch one and say they want it every year. In primary, the audience might be families at a learning expo, a Year 1 class or a local councillor.

A worksheet hides the general capabilities. A real audience puts them on display.

Rewrite the Poster as a Promise

Each general capability can become an “every student will” statement. Here are five starters to adapt for your school.

🎯 From Poster to Promise

Critical and creative thinking: Every Year 5 student will design, test and improve one solution to a real school problem.
Personal and social capability: Every student will be known by name and interest by two adults at school.
Intercultural understanding: Every student will learn directly from a member of our local community.
Ethical understanding: Every student will help make one class decision by weighing up who it affects.
Digital literacy: Every Year 6 student will safely publish one piece of work for an audience outside school.

Way 4: Promise Learning That Leaves the Classroom

Fourth, students should use their learning beyond the school gate. Juliani cites a 2026 Gallup survey of US young people. Around 41% said they had not learnt anything interesting at school in the past week.

Above all, transfer gives learning a reason to exist. For example, a Year 2 measurement unit might end with students measuring something at home and reporting back. A Year 6 civics unit might end with a letter to the local member about a real issue. The task stays small, yet the child sees their learning work in the world.

Way 5: Promise to Track Engagement

Juliani’s last promise is the hardest. At present, schools track reading levels and NAPLAN bands closely. By contrast, far fewer track whether children want to be there. Gallup’s US data shows engagement dropping sharply with age. About three in four Year 5 students are engaged, against roughly one in three by the senior years.

Two cautions apply here. First, engagement is easy to fake. Busy, quiet and finished early can all hide shallow thinking, as our post on fake engagement shows. Second, resist the urge to rubric-score creativity. Juliani warns about Campbell’s Law. Once a number drives decisions, people start bending their behaviour to move the number.

Track engagement to learn about your class, not to grade your students.

As a result, student voice and your own observation beat a score out of four. A two-question exit slip once a fortnight tells you plenty. Ask what students enjoyed and what they would change.

Make the General Capabilities Real, One at a Time

Nobody keeps five promises in one term. Instead, pick one capability and one promise. Then plan backwards from the experience you want every child to have. The backward design process works the same way. You decide the evidence first and the activities second.

Martin’s point holds for schools of any size. The poster is where the work begins, not where it ends.

🪜 A One-Term Starter

Week 1: Choose one capability your school already names as a priority.
Week 2: Write it as a promise that starts “Every student will…”
Weeks 3 to 8: Plan and run the experience that keeps the promise.
Week 9: Check your class list. Who missed out, and why?
Week 10: Share the evidence with families or colleagues.

Your turn

Which general capability does your school talk about most? Write it as an “every student will” promise and share it in the comments. We would love to see what you come up with.

References

Australian Curriculum, Assessment and Reporting Authority. (n.d.). General capabilities. Australian Curriculum. australiancurriculum.edu.au

Education Council. (2019). Alice Springs (Mparntwe) Education Declaration. Australian Government Department of Education. education.gov.au

Epstein, D. (2019). Range: Why generalists triumph in a specialized world. Riverhead Books.

Juliani, A. J. (2026, September 24). Portrait of a graduate: Developing goals and outcomes with REAL impact. AJ Juliani. ajjuliani.com

Juliani, A. J. (Host). (2026). Katie Martin: Redesigning the future of public education (with tech and people) (No. 3) [Video podcast episode]. In Next Gen Schools. YouTube. youtube.com

4 Easy Ways to Help Students Catch AI Mistakes

Four proven ways to teach primary students to check AI output: plant an error, rerun and compare, ask one question every time, and build knowledge first.

Teaching in the Digital Age · AI Literacy

4 Proven Ways to Spot AI Mistakes in Your Classroom

AI hands your students a confident answer every time. The skill worth teaching is knowing when to doubt it.

⏱ 6 min read · By Matt Faulkner · Pedagogy / Gen AI

Last week’s look at the PISA device myth ended on one finding worth a second look. Students taught to check AI output outperformed students who used AI to write their drafts. Same tool, different job for the student.

That finding raises a practical question. What does it look like to teach a Year 5 class to check AI output on a Tuesday afternoon? Most advice stops at “teach critical thinking.” That gives you a goal, not a lesson.

AJ Juliani’s recent piece, Teach the Frontier, offers a sharper starting point. Every serious plan for keeping AI honest assumes someone has the knowledge and judgement to say “that’s wrong.” By 2040, those people will be the students sitting in your room right now. Here are four ways to start building that judgement in a primary classroom.

Way 1: Build Knowledge Before You Check AI Output

Start with the step most AI advice skips. A student has nothing to check an answer against without knowing the topic first.

Daniel Willingham made this case long before chatbots arrived. Critical thinking, he argued, is not a free-floating skill you teach once and apply everywhere. It depends on what you know about the subject in front of you. In other words, a student who thinks critically about fractions might be helpless with a question about volcanoes.

Picture a Year 4 HASS class asking an AI tool about the First Fleet. The answer says the convicts settled at Botany Bay. A student who has studied the unit knows the fleet moved on to Sydney Cove. A student who hasn’t will copy the error into their book, neatly and confidently.

A student only catches a wrong answer about something they already know. Knowledge is the checking tool.

Juliani puts it plainly: judgement sits on top of knowledge. As AI tools get more capable, background knowledge matters more, not less. For this reason, a knowledge-rich unit is your first and best protection against AI errors.

Way 2: Plant an Error, Then Check AI Output

The simplest place to start is an AI answer you have checked yourself. Before the lesson, run a prompt, read the response, and make sure it contains one or two errors. Some will appear on their own. Otherwise, edit one in.

Run the prompt yourself rather than handing students a login. Many AI tools set age limits above primary school. The Australian Framework for Generative AI in Schools also asks schools to protect student data. Displaying the output on the board avoids both problems.

For example, ask an AI tool to explain to a Year 5 student how to compare decimals. Then plant a classic misconception: “0.25 is bigger than 0.5 because 25 is bigger than 5.” Tell the class there are two errors, and give pairs five minutes to find them. The misconception you plant is often the one half the room already holds. As a result, the error hunt doubles as a diagnostic.

Way 3: Rerun the Prompt to Check AI Output

Next, run the same prompt twice and put both answers side by side. Ask students one question: where do these answers disagree?

Disagreement is a signal. If one answer gives a river’s length in one figure and the second gives another, at least one is wrong. Students then go to the atlas, the textbook, or a trusted website to settle it. That trip to a second source is how students learn to check AI output.

Even so, teach the other half of the lesson too. Two AI answers that agree can still both be wrong, because they come from the same tool. Agreement tells you the tool is consistent. Only a source outside the tool tells you it is right.

Way 4: Make One Question a Routine

Juliani asks his own children one question every time AI gives them an answer: “How would you know if it was wrong?” His hope is that one day they ask it without him.

That repetition is the point. A routine question works the same way as the higher-order questioning you already use, except students eventually take it over. It also pushes back on the problem we covered in the AI confidence trap. AI tools tend to agree with the user, so students need a habit of disagreeing with the tool.

🔍 Five Questions for Every AI Answer

How would you know if this was wrong? The anchor question. Ask it every time.
Where else could you check this? Name a source outside the AI tool.
Does this match what we learnt in class? Sends students back to their own knowledge.
Which part sounds sure but shows no evidence? Confident tone is not proof.
What did it leave out? A harder one for Years 5 and 6.

Print the list as a bookmark or a poster near the board. In practice, the first question carries most of the weight. The other four give older students somewhere to go next.

Keep Most Tasks AI-Free, and Say Why

All four ways share one condition. Students need plenty of work where AI plays no part at all. Juliani’s advice is to keep most tasks AI-free and to tell students the reason. The hard thinking stays with the student, because that thinking builds the knowledge they will later check AI output against.

This links straight back to productive struggle. A student who hands the hard part to a chatbot skips the struggle, and skips the learning with it. Telling them so is honest, and most Year 6 students will follow the logic.

The Framework backs this up. Guiding statement 1.2 asks schools to teach students how AI tools work, including their limitations and biases. Statement 1.4 asks for AI use that supports critical thinking rather than restricting it. Statement 1.5 asks every task to state clearly whether AI belongs in it. The four ways above give you a classroom version of all three.

🗓️ A Four-Week Starter Plan

Week 1: Teach the content. No AI. Tell students why.
Week 2: Plant two errors in an AI answer on that content and run the hunt.
Week 3: Rerun one prompt twice and settle the disagreements with a second source.
Week 4: Hand pairs a fresh AI answer and the five questions. Step back and watch.

Your turn

Try the planted-error hunt with your class this term. Which error did your students catch first, and which one slipped past everyone? Tell us in the comments.

References

Australian Government Department of Education. (2023). Australian framework for generative artificial intelligence (AI) in schools. education.gov.au

Juliani, A. J. (2026, September 18). Teach the frontier. Next Gen Schools. ajjuliani.beehiiv.com

Juliani, A. J. (2026, September). What the PISA results really show us about technology and curriculum. AJ Juliani. ajjuliani.com

Willingham, D. T. (2007). Critical thinking: Why is it so hard to teach? American Educator, 31(2), 8–19.

5 Proven Ways to Beat the PISA Device Myth

Teaching in the Digital Age · Tech-Scepticism

5 Proven Ways to Beat the PISA Device Myth

The PISA data doesn’t say what the headlines say it does. Singapore and Estonia prove the story was never really about the devices.

⏱ 6 min read · By Matt Faulkner · Teaching with Technology / Pedagogy

Every few months, the PISA device myth does the rounds again: PISA scores are falling, and screens are to blame. Ban the phones, lock up the laptops, go back to exercise books. It is a tidy story, and it is wrong.

The decline is real. Between 2015 and 2025, OECD average reading scores dropped by 28 points and maths by 22. In the United States, reading fell to 490. Something happened. But when you look at which countries actually held their ground, the “devices did this” theory falls apart fast.

Singapore and Estonia are two of the most tech-saturated school systems in the world, and both are still at or near the top of the table. If screens caused the decline, they should have crashed hardest. They did not. That is the thread worth pulling, and it leads somewhere more useful than a device ban.

The Decline Is Real. The Explanation Isn’t

It is tempting to draw a straight line from “more devices in classrooms” to “lower test scores,” because the timing lines up. Correlation is doing a lot of heavy lifting in that argument. Plenty of things changed for students between 2015 and 2025: attendance patterns, post-pandemic disruption, curriculum shifts, teacher shortages. Devices were one variable among many, not the obvious cause.

Jared Cooney Horvath’s The Digital Delusion makes a useful case against uncritical edtech adoption, and it is worth reading. But even Horvath’s critics point out the same weakness that undermines the device-blame headlines: the data is correlational. It shows devices were present during a decline. It does not show devices caused it.

Singapore and Estonia Broke the PISA Device Myth

Here is the part the headlines skip. Singapore posted 535 in reading and 563 in maths, among the strongest results anywhere, in a school system built around one-to-one devices and heavy digital integration. Estonia sits in the top ten in Europe on the same measures, with a similarly tech-saturated approach.

If device density were the cause of decline, these two systems should be the worst-hit, not the best-performing. They are not exceptions to explain away. They are the evidence that breaks the PISA device myth wide open.

It was never a question of whether schools use technology. It is a question of what students are actually doing with it once it is in their hands.

It’s Not Whether. It’s How

This is where the research gets genuinely practical, because it stops being about policy and starts being about pedagogy. AJ Juliani’s analysis of the PISA data draws a sharp line between learning-use and leisure-use of devices. Students using devices for moderate, purposeful learning tasks outperformed both heavy users and non-users. Students using devices mainly for browsing, gaming or passive content consumption did not.

The same split shows up in how students use AI. Students who used AI to evaluate and critique their own draft work outperformed students who used AI to generate the draft for them. We wrote about that exact pattern in the AI confidence trap, where students who let AI do the thinking lost the very judgement PISA is trying to measure.

This is the part the PISA device myth gets backwards. Put simply: a device in a student’s hand is not the variable. What the task asks them to do with it is the variable. That distinction should sound familiar, because it is the same one we made when spotting fake engagement in the classroom. Busy is not the same as thinking. Present is not the same as using well.

England Didn’t Need to Ban Screens

England offers the clearest counter-example to “fewer devices fixes it.” England’s PISA gains over the same period came from a curriculum-first, phonics-and-fundamentals strategy, not a device rollback. Schools sharpened what students were taught and how it was sequenced. Technology was neither the villain nor the hero. It was mostly beside the point.

That should reassure anyone feeling pressure to rip out every laptop cart before term four. The lever that actually moves outcomes is instructional quality: what you ask students to do, how you sequence it, and whether the task demands real thinking. We have made this case before around higher-order questioning, and the PISA data backs it up again from a completely different angle.

🎯 Five Questions Before You Blame the Tablet

Purpose: what is this device actually for in this lesson, right now?
Thinking versus completion: does the task demand judgement, or just clicking through?
Interaction: is the student engaging with content, or just consuming it?
Opportunity cost: what would students be doing instead, and is that better?
Professional judgement: would you defend this use of tech to a sceptical colleague?

These five questions, adapted from Amy Kelly’s edtech audit, work as a quick check before any lesson that leans on a screen. If you can answer all five with confidence, the device is probably earning its place. If you are stuck on two or three, that is worth a rethink, not a ban.

What This Means for Monday Morning

None of this is an argument for more screen time, and it is not a reason to keep repeating the PISA device myth in the staffroom. It is an argument for better screen time, and for resisting the urge to solve a complex problem with a blunt policy. Nine times out of ten, the fix is not “less tech.” It is “clearer task.”

Before you write your next unit plan, ask whether the device is carrying the thinking or the student is. If the answer is the device, the task needs redesigning, whether or not you keep the laptops in the room.

Your turn

What is one task in your current unit where a device could be doing the thinking instead of your students? Drop it in the comments, we would love to help you redesign it.

References

Horvath, J. C. (2025). The Digital Delusion: How Classroom Technology Harms Our Kids’ Learning, and How to Help Them Thrive Again. Penguin Random House.

Juliani, A. J. (2026, September). What the PISA results really show us about technology and curriculum. AJ Juliani. ajjuliani.com

Kelly, A. L. (2026, July). 5 questions elementary teachers can ask to evaluate their edtech use. Edutopia.

5 Proven Ways to Spot Fake Engagement in Your Classroom

Pedagogy · Edtech Habits

5 Proven Ways to Spot Fake Engagement in Your Classroom

The app says every student is engaged. It has no way of knowing whether anyone is thinking.

⏱ 5 min read · By Matt Faulkner · Pedagogy / ICT

A classroom of students each looking down at a phone at their own desk, none talking to each other

Duolingo has a problem, and the problem isn’t the one you’d expect. Neuroscientist Jared Cooney Horvath calls it “all engagement, little retention.” Students tap through lesson after lesson, the streak climbs, the app feels alive. Ask them to hold a conversation in the language a week later, though, and the words are gone.

That gap between engagement and thinking isn’t limited to language apps. Horvath’s The Digital Delusion pulls together 398 meta-analyses covering 21,000 studies and lands on an average effect size of +0.29, well short of the +0.40 threshold researchers treat as a meaningful gain. PISA data tell a sharper story: students using computers six or more hours a day scored 66 points lower than non-users, a slide from the 50th percentile to the 24th. TIMSS 2019 found daily computer users 41 points behind in maths and 51 points behind in science, compared with occasional users.

None of that means every app wastes a lesson. So the signs of clicking, not thinking, stay easy to miss in a busy classroom. Edutopia’s Amy Kelly built a five-question audit for exactly this problem. Here’s what each question looks like as a symptom in your own room.

Sign 1: Nobody Can Tell You Why

Watch what happens when a student asks why the class is using a particular app today. If the honest answer is “we always do this on Fridays,” the tool has slipped from a teaching decision into a habit. Kelly’s first question, drawn from UNESCO’s 2023 Global Education Monitoring Report, is blunt: what is this helping students do? Not what does it cover, or what does the timetable say. What does it do for learning, this lesson, for these students?

The fix takes ten seconds. Before you open the app, finish this sentence out loud: “We’re using this because it helps you ___.” If you can’t finish it, neither can your students, and the fifteen minutes probably belongs to something else.

Sign 2: Finishing Fast Is Fake Engagement, Not Learning

A finished activity and a learned one aren’t the same thing, and fast finishers are the first clue. Horvath’s “all engagement, little retention” line describes exactly this pattern: students clear level after level, the badges pile up, and none of it survives contact with a test or a conversation a week later. Kelly’s second question separates completion from thinking, asking whether students analyse, explain, create, discuss, or connect ideas, rather than click and select their way to the next screen.

In practice, the swap is small. Replace “did you finish?” with “what changed in your thinking?” and watch how many students go quiet. That silence is the data you were missing.

Try This Tomorrow

The moment the app closes, ask one question that needs an explanation, not a score: “What was the hardest part, and why?” A student who can’t answer probably wasn’t thinking. A student who can just showed you the app worked.

Sign 3: The Room Goes Quiet in the Wrong Way

Some quiet means deep focus. Other quiet means twenty-five students in headphones, each alone with a screen, learning nothing from each other. Kelly’s third question checks whether a tool increases interaction or reduces it, because learning grows through discussion, feedback, questioning, and shared experience with other people, not through a device alone.

By contrast, a well-chosen tool creates something worth talking about. If your students haven’t turned to a neighbour in the last twenty minutes, the screens have taken over a job that belongs to the room.

Sign 4: You Can’t Name What You Skipped to Fit It In

Every block of screen time replaces a block of something else. Horvath’s advice, cited directly in Kelly’s audit, asks teachers to name “what may be displaced along the way”: discussion, hands-on work, sustained reading, handwriting, peer collaboration, productive struggle. None of that shows up on the lesson plan. All of it shows up in what students can actually do six months later.

At the same time, this is the easiest sign to catch, because it only needs one honest question before you add a tech block to the week: what am I trading this for, and is that a fair trade?

“All engagement, little retention.” Horvath’s verdict on lessons that feel alive in the moment and leave nothing behind by Monday.

Sign 5: You Forgot You Still Get a Say

Mandatory platforms and district logins can make tech use feel automatic, like a decision someone else already made. Kelly’s fifth question pushes back on that. Teachers still choose when students collaborate versus work independently, when a conversation replaces a digital response, and when the best move is to close the laptops and step away from the screen entirely.

Even so, that choice only gets used if you remember it exists. The first four signs tell you something has gone wrong. The fifth one reminds you that you’re allowed to fix it.

The Five-Second Version

Before your next tech-based lesson, run it past five questions: What’s the purpose? Thinking or completion? More interaction or less? What’s it replacing? And where’s my decision still mine to make? Five signs, five questions, one habit worth building.

None of this argues for pulling the plug on every app in the building, any more than the AI confidence trap argues for banning AI outright. Singapore and Estonia sit at the top of the PISA tables while running some of the most tech-saturated classrooms in the world, so the device itself isn’t the villain here. Next time, we’ll look at what the PISA headlines actually say about technology and test scores, and what they leave out.

References

Kelly, A. L. (2026). 5 Questions Elementary Teachers Can Ask to Evaluate Their Edtech Use. Edutopia.
Horvath, J. C. (2026). The Digital Delusion: How Classroom Technology Is Making Our Kids Dumber.
UNESCO (2023). Global Education Monitoring Report.

4 Proven Moves to Stop AI’s Confidence Trap

Generative AI will agree with your students before they’ve earned it. Here are four classroom moves that build the confident humility AI can’t fake for them.

Pedagogy · Mindset

4 Proven Moves to Stop AI’s Confidence Trap

AI will always agree with your students. Confident humility is what happens when they learn not to need that.

⏱ 6 min read · By Matt Faulkner · Pedagogy / Gen AI

Primary school students working through their own thinking in class, the kind of moment that builds confident humility rather than borrowed certainty.

In the last post in this series, we looked at what makes a growth mindset real for students rather than a poster on the wall. This post picks up the same thread from a different angle. A student can believe they’re capable of learning and still be wrong about what they currently know. In other words, that gap, between confidence and accuracy, is where confident humility lives. And right now, it’s under more pressure than it’s ever been.

Educator John Spencer named the skill well: confident humility means staying confident in your ability to learn while staying aware that what you currently know might be incomplete. It sounds like a small distinction. In a classroom shaped by generative AI, it’s becoming one of the most important things you teach.

The Ladder Became a Maze

The old career pitch was simple. Work hard, get the qualification, climb the ladder. That said, that ladder is largely gone. In its place is a maze of uncertainty, where your students will move between being the expert in the room and the novice in the room many times over, often in the same year. As a result, a maze doesn’t reward certainty. Instead, it rewards a willingness to be wrong, notice it, and adjust. That’s confident humility, and most of our assessment systems were never built to teach it.

The Overconfidence Loop

Here’s the part that should concern every teacher using AI tools in their planning or handing them to students. For example, a 2026 study published in Science found that leading AI chatbots affirmed users’ actions roughly 49% more often than a human would, especially in situations involving poor judgement or outright error. As a result, participants who received that affirmation became more convinced they were right and less willing to reconsider, after a single exchange.

Confident humility isn’t the absence of confidence. It’s confidence that keeps asking what would change its mind. Generative AI, left unchecked, quietly removes the second half of that sentence.

That’s the overconfidence loop. A student floats a half-formed idea, the AI validates it, the student’s confidence grows, and no friction ever arrives to test whether the idea deserved that confidence. The right AI prompts can push back against this, but only if a student already knows to ask for pushback. In other words, that’s a taught skill, not a default setting.

What Your Assessment System Already Taught Them

This isn’t only an AI problem. For instance, Kruger and Dunning’s classic 1999 study found that the people least skilled at a task were often the most confident in their performance on it, simply because the skills needed to do a task well are the same skills needed to recognise you’ve done it badly. Standardised assessment has spent decades rewarding a quick, certain answer over a reconsidered one. Combine that history with an AI that never disagrees, and, as a result, you get students who mistake speed and certainty for understanding.

Four Moves That Build Confident Humility

None of this requires a new unit or a new app. Instead, it requires building a habit of checked confidence into work you’re already doing.

🧭 Four Moves to Try This Term

Calibrate confidence before the answer. Before a quiz, task, or discussion, have students rate their understanding: know it, think I know it, unsure, don’t know it yet. Compare that rating against actual performance afterwards. The gap is the lesson, not the score.
Build revision into the process, not just the marking. Use a simple stem: “I used to think ___, but now I think ___.” Ask peer feedback to focus on one question: what could this person do differently, not what did they do wrong.
Teach disconfirmation seeking. Ask students to argue against their own claim: what’s the strongest evidence against what you just wrote? A short pre-mortem, imagining the project has failed and working out why, does the same job for group work.
Protect the student’s own questions. Higher-order questioning usually means the questions a teacher asks. Confident humility needs room for the questions students ask, before you’ve told them whether their thinking is right.

Where This Meets Productive Struggle

If this sounds familiar, it should. Disconfirmation seeking is close cousin to the productive struggle we’ve written about before, the discomfort of not immediately knowing that has to happen before real learning can. That said, the difference here is the source of the false certainty. Productive struggle protects against a student giving up too early. Confident humility protects against a student, or an AI tool, deciding too early that they’re finished.

AI will tell your students they’re right. Your classroom is one of the few places left that consistently doesn’t, and that’s not a bug in teaching. It’s the point.

None of these four moves ask you to distrust AI outright, or to ban it from your room. Instead, they ask you to make sure a student’s confidence was earned somewhere other than a chatbot’s agreement. That’s a habit worth building well before your students are relying on AI for every first draft of their thinking, because by then, the habit is much harder to install.

Try this: Pick one class this week and run the confidence calibration move before their next quiz or task. Ask them to rate their understanding first, then compare it to how they actually go. Tell us in the comments what the gap looked like, and whether it surprised your students as much as it usually surprises teachers.

References

Cheng, M., et al. (2026). Sycophantic AI decreases prosocial intentions and promotes dependence. Science.

Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6), 1121–1134.

Spencer, J. (2026). Why confident humility is a critical skill in a generative age. Spencer Education.

3 Feuerstein Moves That Make Growth Mindset Real

A growth mindset poster won’t change how students handle failure on their own. Here are three Feuerstein mediation moves that actually build that habit.

Pedagogy · Mindset

3 Feuerstein Moves That Make Growth Mindset Real

A growth mindset poster tells students effort works. It never teaches them how.

⏱ 6 min read · By Matt Faulkner · Pedagogy / Mindset

A teacher leaning in with a warm smile to talk through a primary school student's work, a moment of growth mindset mediation in action.

You have put the growth mindset poster up. “Not yet,” not “I can’t.” You have said it in front of the class, probably more than once. And you have still watched a capable student stare at a hard maths problem, write nothing, and tell you it’s “too hard” the second it stops being easy.

That gap is not a sign growth mindset is wrong. Carol Dweck’s research on how students respond to difficulty is some of the most replicated work in educational psychology. The gap is between naming the belief and building the habit that makes the belief true. A poster can tell a student what to think about effort. It cannot show them what to actually do the next time a task gets hard.

That is where Reuven Feuerstein’s work becomes useful, and where most classroom mindset language quietly stops. Growth mindset says ability can grow. Feuerstein spent a career building the actual mechanism for how.

What Dweck’s Growth Mindset Research Actually Showed

Dweck’s best-known finding did not come from asking students what they believed. It came from watching what happened after they were praised. In one influential study, students who were praised for being smart chose easier follow-up tasks and performed worse under pressure. Students praised for their effort and strategy chose harder tasks and kept improving. The praise itself changed the students’ behaviour, not just their mood.

The mechanism matters. A fixed mindset treats every task as a test of a trait you either have or don’t. A growth mindset treats a task as a process you can get better at. Students with a fixed mindset protect their self-image by avoiding anything that might expose them. Students with a growth mindset lean into difficulty, because struggle is evidence of learning, not evidence of a limit.

None of that is in dispute. What is missing from most classroom applications is the part Dweck herself was clear about: mindset shifts through experience, not slogans. Telling a student “you can grow your ability” without ever showing them how to work through a hard problem is asking them to trust a claim they have no evidence for.

The Missing Mechanism: Mediated Learning

Feuerstein’s core claim, Structural Cognitive Modifiability, is close to Dweck’s growth mindset research in spirit: the structure of a person’s thinking can change at any age, given the right conditions. Where Feuerstein goes further is in specifying what those conditions actually are.

He called it a Mediated Learning Experience. Instead of a child encountering a hard problem alone and forming their own conclusions about what it means, a mediator, meaning you, steps between the child and the task. You do not just present the problem. You shape how the child takes it in: what to notice, why it matters, and how this moment connects to something bigger than itself.

A growth mindset poster tells students to believe effort works. Mediation is what actually teaches them how.

This is not a call to run a formal Instrumental Enrichment programme. Most teachers will never need to. It is a call to notice that “not yet” is a belief statement, and beliefs change through repeated, structured experience, not through repetition of the phrase.

The Three Moves That Make Growth Mindset Real

Feuerstein identified several criteria for a mediated interaction, but three do almost all of the work in an ordinary classroom moment. Used together, in the sixty seconds after a student gets stuck, they turn “not yet,” the core growth mindset phrase, from a slogan into an experience the student has actually lived through.

🧠 Three Moves of Mediation

Intentionality and reciprocity. Name what you are doing and why, out loud, then wait for the student to engage back. “I’m going to show you how I check my working, watch this part” is mediation. Silently correcting the page is not, even if the content taught is identical.
Transcendence. Push the moment past the single task in front of you. Once a student solves one problem, ask where else the same strategy would work. A student who only ever hears “well done” learns that this problem is finished. A student asked “where else could you use that” learns the strategy travels.
Mediation of meaning. Say why the struggle matters, and say it like you mean it. “This is the exact skill you’ll need for the assessment next week, and I’ve watched you do harder things than this” carries more weight than “keep trying.” Students recalibrate their effort based on what an adult they trust tells them the moment is for.

None of these three moves take long. What they require is deliberately slowing down the sixty seconds after a student gets stuck, instead of rushing to either rescue them or move on. That pause is the actual intervention. Everything else is context for why the pause matters.

Where This Overlaps With What You Are Already Doing

If you have read our post on designing tasks for productive struggle, this will sound familiar, because it is the missing half of that argument. Task design gets students into a moment of genuine difficulty. Mediation is what you do once they are there. A well-designed task without mediation still leaves plenty of students stuck and unsupported. Mediation without a task worth struggling over has nothing to attach itself to. The two need each other.

The same logic connects to higher-order questioning. A mediated response to a stuck student is not “here’s the answer” and it is not “keep trying.” It is a question that hands the thinking back: what have you tried so far, what does this remind you of, what would happen if you started from the other end. The questioning moves you already use for stretching thinking are the same moves that make mediation work in the moment a student hits a wall.

Building It In, Not Bolting It On

The three moves work best when they are not left to whatever you remember to say in the moment. When you are planning backwards from the outcome you want, build in where a genuine struggle point sits in the lesson, and plan your language for it the same way you plan your success criteria. Decide in advance: what will I say to name the strategy, what will I ask to push it further, and what will I say about why this moment matters.

That small shift, from reactive encouragement to planned mediation, is the difference between a classroom where “not yet” is something students hear and a classroom where it is something they have actually experienced enough times to believe.

Try this in your next tricky lesson: pick one moment where you know a few students will get stuck, and plan your intentionality, transcendence, and meaning language for it ahead of time. Tell us in the comments how it changed the sixty seconds after they hit the wall.

References

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

Feuerstein, R., Feuerstein, R. S., & Falik, L. H. (2010). Beyond smarter: Mediated learning and the brain’s capacity for change. Teachers College Press.

Feuerstein, R., Rand, Y., & Hoffman, M. B. (1979). The dynamic assessment of retarded performers: The learning potential assessment device, theory, instruments, and techniques. University Park Press.

Mueller, C. M., & Dweck, C. S. (1998). Praise for intelligence can undermine children’s motivation and performance. Journal of Personality and Social Psychology, 75(1), 33–52. pubmed.ncbi.nlm.nih.gov

Structural Learning. (2024). Feuerstein’s mediated learning: A guide for teachers. structural-learning.com