Month: September 2026

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.

Primary students point at an AI answer on the classroom board and check it for errors while their teacher watches.

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.

Students using a mix of devices and paper, the opposite of the PISA device myth

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

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