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The Easiest Post to Write

Posted in Change, Parenting, tagged , on September 1, 2026| Leave a Comment »

The easiest post to write this fall would be the one against it. I could open with a warning about screens, line up every worry about AI, about cheating, about the slow erosion of thinking, then call for a ban, promise to protect childhood and close with something stirring about the good old days. It would be a popular post. It might be my most shared one all year.

I am not going to write that post.

Not because those worries are wrong. A lot of them are right. There is a real mood out there this fall, a pushing back against technology in schools, and much of it is healthy. We restricted phones. We are talking again about attention, about presence, about what it means to actually be in a room together. I am glad we are having those conversations. This blog has spent more than fifteen years arguing that the human part of school is the whole point.

The booing

Think back to this past spring. At college and university commencement ceremonies across the United States, graduates booed. Not at anything you would normally expect. They booed at AI. At the University of Central Florida, a speaker described AI as the next industrial revolution and was met with jeers. At the University of Arizona, former Google CEO Eric Schmidt was repeatedly booed as he discussed AI. At Glendale Community College, an AI system used to read the graduates’ names skipped some of them entirely, and the college president was booed when she explained what had happened, which honestly feels like the technology earning its own reception.

I keep thinking about that sound, and I do not think it was simple. Some of it was fear. This was a graduating class walking into a difficult job market, told over and over that a machine might take the work before they ever get to it. Some of it was the overhype, the sheer exhaustion of every product, advertisement and keynote speaker insisting that AI changes everything. Some of it was resentment at watching a handful of companies wedge themselves into every corner of our lives. Some of it was grief for something human that feels as though it is slipping away. And some of it, I suspect, was much simpler than all of that. It was young people, on one of the most human days of their lives, asking the adults at the podium for one afternoon, just one, where nobody mentioned the robots.

All of that was in the boo. That is what makes this hard. I do not think we can dismiss it as ignorance or resistance to change. It felt more like a very human response to being repeatedly told what the future is going to look like, often by people who have a significant stake in that future arriving.

And maybe that is part of what has me thinking differently about this year. I want school to feel more human, not less. More conversation, fewer screens. More looking up, less scrolling. More opportunities to make things, move, argue, wonder and be fully present with one another. If you have read me for a while, you know the word I chose for this year is Alive. That is what I want our schools to feel like.

It is also making me wonder whether we sometimes confuse being ready for the future with being ready for the newest technology. They are not the same thing. AI is certainly part of the world our students are entering, but surely the bigger goal is young people who are curious, adaptable and creative, who can think critically, work with others, make good judgments and learn something new when the world changes around them. AI happens to be one of the things forcing us to think about those qualities right now. It will almost certainly not be the last.

The world is not AI free

I understand the instinct behind a ban. Sometimes the easiest way to protect something is to keep something else out. But AI is not waiting politely at the school gate. It is already in the backpacks, on the phones and in the homework that gets finished at the kitchen table long after the bell. Our students use it. Their parents use it. I use it, and I tell you so at the bottom of every post. The idea that we can draw a clean line around the school day and declare it AI free has some appeal. But that line does not really exist anymore, if it ever did.

And this is not only an American story. A KPMG survey last fall found that 73 percent of Canadian students aged 18 and older were using generative AI for their schoolwork, up from 52 percent two years earlier. Whatever we decide about the place of AI in schools, students are already making decisions about it themselves.

That does not mean it belongs everywhere. In fact, the more I think about this, the more convinced I am that there are times when learning should simply be AI free. Some assignments, some conversations, some assessments and long stretches where students are expected to read, think, write, solve, struggle and create on their own. Young people still need to learn to write a sentence, develop an argument, sit with confusion and produce something that is genuinely their own. Sometimes the struggle is the learning, and if we remove the struggle we may also remove the thing we were trying to teach.

Context matters too. A six year old and a seventeen year old probably should not have the same rules. A student learning a skill and a student who has already developed that skill might reasonably have different access to tools. This is where I find simple answers less satisfying. There are times when restricting AI makes complete sense. There are other times when using it might deepen learning. Figuring out the difference seems much more useful, and much harder, than simply deciding whether AI is good or bad for schools.

That may also be one of the reasons school matters so much in this conversation. We spend a lot of time helping young people question sources, weigh arguments, recognize bias and tell the difference between knowing something and simply producing a good looking answer. AI complicates all of that, but it also gives us another context in which to teach it. A polished answer is not necessarily a thoughtful answer. Confidence is not accuracy. Generating something is not the same as understanding it.

A ban can establish a boundary. I am just not sure it can do all the teaching we need.

Even the builders are asking for caution

Here is another piece that keeps me from becoming much of an AI cheerleader. At the end of July, more than a thousand employees of frontier AI companies signed a statement called Pacing the Frontier. They did not call for AI development to stop. They asked the United States government to support an international effort to build the technical and governance tools that could deliberately pace frontier AI development if it begins moving faster than society can safely understand and manage.

That distinction is interesting. It is not a call for a ban, and it is not quite panic either. It is closer to asking whether we should make sure there is a brake pedal before discovering that we need one. Some of the people closest to these systems worry that the technology could begin advancing beyond our existing capacity for safety, oversight and governance. At the same time, many of them believe AI could contribute to a dramatically better future. Both ideas can apparently be true at once.

You could also fairly read a letter like this as its own kind of hype, insiders talking their book, and maybe some of it is. I do not know. But I find the uncertainty itself worth paying attention to. If some of the people building these tools are saying the outcomes are not guaranteed, it makes me a little wary when those of us in schools talk as though the path ahead is obvious.

Schools are certainly not responsible for solving the future of artificial intelligence. But we do have a role in helping young people live thoughtfully in a world shaped by powerful forces they did not choose and cannot entirely control. That feels like a bigger and more human challenge than teaching them how to write a good prompt.

The grey

Most of the interesting parts of life are grey, and this is no exception. A few years ago, I wrote that AI in education was getting murky. It has not gotten any clearer since. It has only gotten more interesting.

Perhaps the wrong question is whether technology belongs in learning at all. Schools have always been full of things that stand between a student and an idea: books, pencils, worksheets, screens and, sometimes, other people. Every tool mediates learning in some way. What I find myself wondering more now is which of those things deepen attention, understanding and relationships and which quietly diminish them. Which tools give students greater independence, and which make them more dependent? Which tools open possibilities, and which simply make it easier to avoid the work? Which tools help a teacher see a student more clearly, and which put one more layer between them?

I do not think those questions have simple answers. They depend on the student, the teacher, the task and what we are actually trying to accomplish. But I also wonder whether the more capable our technologies become, the more valuable some very old human capacities become. Curiosity, creativity, judgment, the patience to sit with uncertainty, the willingness to change your mind when the evidence changes, and the ability to make something with another person that neither of you could have made alone. We sometimes call these soft skills. There is nothing particularly soft about them. They may turn out to be some of the hardest and most important things we teach.

I have also seen enough by now to know this is not only a defensive conversation. I have watched a teacher use AI to clear away administrative work that was keeping her from her students, and spend that reclaimed time actually with them. I have watched it give a student a second way into an idea the first explanation did not unlock. I have watched young people use it to trace a claim back to its source, and push a draft they had already started further than they would have managed alone. None of those examples makes me think we should use AI everywhere. They do make it harder for me to argue that it has nothing useful to offer.

The version of this I keep circling back to is one I have written about before. What if AI, of all things, could make school less about screens and more about people? What if handing off some of the genuinely tedious digital work, without handing off the thinking, gave teachers and students more time to talk, connect, build, move and look one another in the eye? I believed that was possible when I wrote it. I still do.

Of course, that does not happen automatically. Technology has a way of filling whatever space we give it. Getting to a more human version of school will require some intention, some professional judgment and probably some mistakes along the way. It will also require us to be willing to say yes in some places and no in others, and to change our minds when the evidence tells us we should.

Maybe a better question

A couple of years ago, almost every conversation I heard about AI in schools was about what it can do. The demonstrations were irresistible. Look what it can write. Look what it can draw. Look what it can create in ten seconds. There was an understandable fascination with the party tricks.

Lately, I have noticed the conversation changing. Not everywhere, and not all at once, but increasingly I hear people asking not only what AI can do, but what it should do. And, perhaps even more importantly, what it should not do.

I like that shift. It feels less like adoption and more like education.

Maybe that is ultimately what I mean when I talk about preparing young people for the future. It cannot mean accurately predicting what the future will look like, because history suggests we are not particularly good at that. It might instead mean helping young people become comfortable making choices in a world that keeps changing. Choices about which tools to use, which claims to trust, when to ask for help, when to struggle on their own and when to reject something even though everyone around them seems to be embracing it.

AI makes those questions urgent, but they are not really AI questions. They are questions about judgment, independence and agency.

Where I am landing, for now

So perhaps that is where I am at as we begin another school year. I do not want us to pretend AI does not exist, but I also do not want fascination with the technology to become the point. I want us to stay curious about where it might help, cautious about where it might get in the way, and protective of the parts of school that remain deeply human: conversation, mentorship, relationships, belonging, connecting and the experience of working through something difficult together.

None of this feels particularly settled to me, which is probably appropriate. I am less interested than I was a couple of years ago in predictions about how AI will transform education. I am more interested in the smaller and probably harder questions about what we value, what we want students to be able to do for themselves, and where technology helps or undermines those goals.

So I will end where I usually do, with questions. What does a more human school actually look like this year? Where does AI help us get there, and where does it quietly get in the way? When should students use it, and when is the most important lesson learning to continue without it? What capacities will matter most for young people in a future none of us can predict very well? And how do we make sure the time technology saves gets spent on one another, and is not simply swallowed by the next screen?

I do not have all the answers. I am becoming increasingly doubtful that anyone does.

But school still seems like a pretty good place to work through the questions.  

Welcome back.

Note: The image at the top of this post was generated using AI. I also used AI tools for editorial feedback while refining the piece. The arguments and conclusions are my own.

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AI, Cybersecurity and a Room Full of Trustees

Posted in Change, Personalized Learning, Technology, tagged , , , , on August 25, 2026| Leave a Comment »

We opened our session at the Canadian School Boards Association conference with a show of hands.

How many of you have personally used an AI tool in the last month? Almost every hand in the room went up.

How many of you have had a substantive conversation about cybersecurity at your board table in the last year? Well over half.

How many of you feel clear on what belongs to the board and what belongs to staff in these areas?

Maybe a quarter.

We had not planned to stop there, but that pattern was too interesting to move past. So we gave the tables a couple of minutes to talk about it. A room full of experienced trustees, nearly all of whom were using AI personally, most of whom were discussing cybersecurity at their board tables, and only a fraction of whom felt clear on where their role starts and stops.

That gap, between engagement with the issues and clarity about the governance role, was really the whole point of our session.

The pattern did not surprise me, exactly. A few months ago, a trustee said something to me that has stuck: “I feel like I need to understand this better before I can do anything useful.” I have heard versions of that sentence a lot. When a topic is moving this fast, the natural instinct is to reach for expertise. To want to know more before you act.

But the show of hands at CSBA suggested something worth considering: this was not mainly a familiarity gap. It was a role-clarity gap.

And those are very different problems, with very different solutions.

I wrote about the trustee role in a broader way back in May, in a post for people considering running for school trustee ahead of the October elections here in BC. One of the things I tried to say there is that the role is not what most people think it is. Trustees do not run schools. They do not manage teachers. They do not direct operations. What trustees do is govern a system. They set direction, ask questions, build relationships, and hold the system accountable.

That distinction between governance and management is the foundation of everything else. And AI and cybersecurity have become two of the best stress tests of that distinction I have encountered.

That is what drew Mark Pearmain and me into a ninety-minute session earlier this summer, with the national gathering hosted here in BC. Mark leads Surrey Schools, the largest district in BC. I lead a much smaller one. We come at these questions from genuinely different places, which is part of what made building the session together worthwhile. A risk that registers one way in West Vancouver looks different at Surrey’s scale. An innovation that feels experimental in one place might already be routine somewhere else.

We were clear in the room that we were not there as technical experts. What we tried to bring was something different: the perspective of system leaders trying to connect emerging technology, risk, learning and governance.

Here is what I took away.

The trustees in the room were clearer on their role than the conversation about trustees sometimes suggests. We spent time on the distinction between operational expertise (which belongs to staff) and governance judgment (which belongs to the board). I half expected this to be the hardest part of the session. It was not. Many of the trustees who spoke understood exactly where that line sits, and their questions came from the governance side of it.

That was encouraging, and it deserves to be said, because trustees do not always get credit for this clarity.

The anxiety around AI is real, and it is fair. You could feel it in the room. And rather than trying to talk anyone out of it, we kept coming back to a frame that I think holds up: good governance holds risk and possibility at the same time. A board that only sees risk may slow its system at exactly the wrong moment. A board that only sees possibility may leave students and staff exposed.

The anxiety is not a problem to be solved. It is a signal that the stakes are understood.

The cost of cybersecurity belongs in budget conversations. Some of the best discussion in the session came around what cybersecurity actually costs, in staffing, in systems, in preparedness. This is not a luxury line item, and it is not purely an operational detail. Boards approve budgets, and boards should be asking what their district’s cyber readiness costs, what it would cost to be better prepared, and what the cost of being unprepared looks like.

A board does not need to manage the cybersecurity plan. But it does need to understand the investment, the risk, and the consequences of underpreparing.

As Mark put it: the middle of a crisis is a terrible time to discover the board does not know its role.

Education really does look different in every province. One of the best conversations I had after the session was with a trustee from Alberta, who described how differently cybersecurity responsibility is structured there, particularly around the roles of insurance companies and provincial government. I knew this in the abstract. Hearing it described concretely was a reminder that a national conversation about these topics is not about finding one answer.

It is about understanding how differently the same challenge lands across the country, and learning from that variety.

Which connects to the argument Mark made throughout the session, and made well: this work needs national-level connection. Education is a provincial responsibility, but AI and cybersecurity challenges do not stop at provincial borders.

Mark also brought the OECD’s Education for Human Flourishing work into the session, which gave the whole conversation a global context. That matters for trustees because the governance question is not only, “How do we manage the risk?” It is also, “What kind of education are we trying to protect and strengthen?”

The questions trustees are asking about AI are not just Canadian questions. They connect to what education systems around the world are trying to figure out about what it means to prepare young people for a world where the technology keeps getting more powerful and the human elements keep getting more important.

We put together a set of materials for the session, and I want to share all of it here. There is the slide deck and three handouts, including a question bank with twenty questions organized into four areas: learning and equity, privacy and data, risk and readiness, and networks and shared learning. None of the questions require technical expertise to ask.

Please use these however they are useful. Take them to a board meeting. Pull out the three questions that fit your context and ignore the rest. Remix them, adapt them, improve them. They were built to be circulated, not protected.

If one of them helps start a better conversation in your district, that is the point.

 

HANDOUT: Table Discussion Questions 

HANDOUT: 20 Questions Worth Asking This Year

HANDOUT: The Governance Compass

If there is one thing I hope carries beyond the session, it is the closing idea Mark and I landed on: better questions create better conditions, and stronger connections create shared wisdom. What one district learns should not stay trapped in one district. What one province figures out should not have to be rediscovered in another. The way forward on all of this is to keep networking, keep sharing, and keep learning together.

None of us fully knows where this is going. That is exactly why the connections matter.

The image at the top of this post was generated through AI. Various AI tools were used as feedback helpers as I edited and refined my thinking.

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Steadiness as Strategy

Posted in Change, Personalized Learning, tagged , , , , , on February 24, 2026| 6 Comments »

In my year-end reflection last December, I found myself dwelling on something that might seem unremarkable: there wasn’t a lot of drama in BC education this past year. No major controversies. No political firestorms. No headlines.

And I wrote: that’s a good thing.

In a year when AI, politics, and social media all seemed determined to manufacture urgency, the absence of drama stood out. It felt almost countercultural to say it out loud, but it was true.

I’ve spent more than 500 posts championing innovation, asking “what if,” and pushing against “we’ve always done it this way.” I’ve written about AI, about rethinking assessment, about challenging assumptions. This blog is called Culture of Yes for a reason. I believe in trying things.

So let me be clear: this isn’t a retreat from any of that.

But here’s what I’ve come to believe in this work: steadiness is a strategy. And it might be the most underrated one we have.

This tension between innovation and improvement isn’t new. It’s been a sustaining conversation in education for much of this century, and I’ve returned to it in different ways on this blog. In 2011, I wrote about Valerie Hannon’s “split screen approach,” the idea that we need to improve the system of today while simultaneously designing the system of tomorrow. In 2013, I used the movie Groundhog Day to warn against simply repeating each year a little better, noting that we want to teach for 25 years, not for one year repeated 25 times. And in 2017, I explored the tension between getting better and getting different, and found that when we embrace doing things differently, traditional results often improve too.

So what’s changed in my thinking?

Maybe this: I’ve come to see steadiness not as the opposite of innovation, but as its prerequisite.

We live in a world that celebrates disruption. We reward the bold move, the big announcement, the pivot. In education, we talk constantly about reimagining and transformation. The language of change is everywhere.

And some of that is good. Schools should absolutely be places of wonder and joy and amazement. We should try new things. We should ask hard questions about whether what we’re doing is actually working.

But there’s a difference between innovation and improvement. Innovation asks, “What’s new?” Improvement asks, “What’s better?” Both matter. The problem is that improvement is quieter. It doesn’t photograph well. It rarely makes the newsletter.

Innovation introduces variance. Improvement reduces variance. Healthy systems need both.

I often come back to a phrase I’ve borrowed from others over the years: you don’t have to be sick to get better. That reframes the whole enterprise. We’re not in crisis mode. We’re not fixing something broken. We’re refining something that’s working, and that kind of work requires patience, repetition, and a willingness to resist the shiny thing.

What makes that kind of slow, steady improvement possible? Trust.

And trust, in a school system, is built through consistency. When the Board is consistent with its expectations, the executive team can plan. When the executive team is consistent, principals can lead. When principals are consistent, teachers can teach. When teachers are consistent, students can learn. That chain isn’t bureaucracy. It’s infrastructure. It is the solid ground that lets people take risks, because they know the foundation won’t shift beneath them.

Sometimes progress looks like not having to explain the same thing again.

I’ll admit something. Earlier in my leadership journey, I felt pressure to prove myself through visible wins. The flashy initiative. The big rollout. The thing you could point to and say, “I did that.” It’s natural. When you’re newer to a role, you want to show you belong there.

Somewhere along the way, that shifted. Maybe it’s experience. Maybe it’s just getting older. I have become more comfortable letting the work speak quietly. The best days in our schools aren’t the ones that make headlines. They are the ones where a student finally understands something that has been just out of reach. Where a teacher tries something new and it lands. Where a conversation in a hallway changes a kid’s trajectory.

None of that trends. All of it compounds.

So yes, I’ll keep advocating for wonder and joy and amazement in our schools. I’ll keep pushing us to ask whether we’re doing right by every student. But I have also made peace with something: the most important work often looks, from the outside, like nothing is happening at all.

Steadiness doesn’t make headlines. But it makes a difference.

The image at the top of this post was generated through AI.  Various AI tools were used as feedback helpers (for our students this post would be a Yellow assignment – see link to explanation chart) as I edited and refined my thinking.

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In Praise of Assistance (And the Myth of the Unassisted Writer)

Posted in Change, Personalized Learning, Technology, tagged , , on January 13, 2026| 10 Comments »

I have been thinking a lot about assistance lately. Who gets it, who does not, and why we suddenly get moralistic about it the moment the assistance comes from AI.

The spark for this post is Nick Potkalitsky’s Substack essay, “In Praise of Assistance.” It is one of those pieces that does not just add to the AI and writing conversation. It reframes it (Thanks to Adam Garry for pointing me towards it).

Nick starts from the now familiar worry about “cognitive offloading,” students delegating the thinking to a tool, and he agrees the concern is real. But then he names what often sits underneath the concern: not just pedagogy, but ideology.

He argues that the cognitive offloading critique rests on “a historical fiction: the autonomous learner.” Because if we are honest, most of us did not learn to write (or think, or revise) alone.

My own invisible advantage

In high school, I had a huge advantage: my dad was an English teacher, and he read every essay before I submitted it. Not just English essays. All of them, across every subject.

He did not write my essays. But he did what good teachers do. He asked questions I had not thought to ask. He pointed out where my logic sagged. He helped me tighten sentences. He coached me toward clarity.

That continued through university. And years later, when I became a newspaper columnist, he was still my first reader. Every column went to him before it went to my editor. He would call with suggestions, and I would decide what to keep and what to let go.

At the time, nobody called this cheating. We called it support. Nick puts it simply: “Students have always learned through assistance. From peers, from teachers, from resources…” 

We rarely worry students are “offloading” onto classmates in a discussion. We celebrate it. But when AI enters the picture, suddenly assistance becomes suspect.

That is the tension.

The question is not “help or no help”

When we talk about AI and writing, the debate often collapses into a binary: real writing (alone, unaided) versus fake writing (assisted, scaffolded).

But that binary does not match how writing actually works. It does not match how learning has actually  ever worked.

The better question is the one Nick keeps pointing us toward: what kind of assistance builds thinking, rather than replacing it?

That is where his essay becomes more than a defense of AI. It is a critique of an unspoken standard that has been unevenly distributed for a long time. The idea that “authentic struggle” is the price of admission to learning.

Nick names the class based reality bluntly: affluent students often have “small seminars, writing conferences, office hours, peer review sessions” while others are in systems where meaningful feedback barely exists. And then comes the sentence: “The outcome depends on whether we recognize assistance for what it is: not a threat to learning, but its precondition.”

What I have been writing toward

In October, I wrote “Modeling AI for Authentic Writing.”  If AI is here (it is), then our job is to model the kind of use that keeps the writer in control. In that post, I tried to move the conversation from “Don’t use AI” to “Show your decisions.”

Because the heart of authentic writing is not whether you had help. It is whether your thinking is present. What did you accept? What did you reject? Why? What did you learn in the revision?

I wrote then: “None of this replaces judgment. I accept or reject every change.”

For years, Tricia Buckley, and before her Sharon Pierce and Deb Podurgiel, have played a similar role here on this blog, reading every post before publication and offering feedback. The byline is still mine because the ideas, voice, and final choices are mine.

That is the point.

Assistance is not the enemy of learning. Abdication is.

What I want to add

There is a system design question underneath that I keep circling back to.

If we accept that all learning has always been assisted, what changes about how we run schools?

A few weeks ago I wrote about the tutoring revolution and found myself wrestling with a similar tension. For years, success in certain courses quietly required something extra: a tutor. Parents traded recommendations, students admitted they needed help, and the whole system ran on an unspoken understanding that school alone was not enough. At least not for everyone.

AI is changing that. But here is the part that worries me: the digital divide is no longer just about device access. It is about knowing how to use the tool well. A student with strong digital literacy might turn ChatGPT into a Socratic tutor. Another might never get past using it as a homework completion machine.

Nick writes about elite students who have always had access to “assistance made flesh.” The risk now is that we create a new version of the same divide. Some students learn to collaborate with AI in ways that deepen their thinking. Others use it to bypass thinking altogether. And if we are not intentional, digital confidence becomes the new proxy for privilege.

The question is not whether students will have AI assistance. They already do. The question is whether we will teach them to use it in ways that build capacity or let the gap widen on its own.

A Culture of Yes stance

A Culture of Yes does not mean saying yes to every tool or every shortcut.

It means saying yes to the conditions that help more people learn well.

So here is where I am landing, at least today.

Writing has always been assisted. The myth of the autonomous writer has always favoured students with the most support. AI can absolutely be used to bypass thinking. But it can also be used to invite thinking, especially where feedback is scarce.

Our job is to design and model practices where assistance makes thinking visible and growth possible.

Nick’s essay refuses the easy frame. It asks us to stop policing help and start building learning communities where help is normal, explicit, teachable, and more equitably available.

That feels like the kind of “yes” worth defending.

The image at the top of this post was generated through AI.  Various AI tools were used as feedback helpers (for our students this post would be a Yellow assignment – see link to explanation chart) as I edited and refined my thinking.

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The Tutoring Revolution We Did Not See Coming

Posted in Change, Technology, tagged , , , , on December 15, 2025| 2 Comments »

For years, there has been a quiet understanding in many high schools that success in certain courses, especially senior math and sciences, required something extra. Not more effort or better attendance, but a tutor. Parents would trade recommendations, students would quietly admit they needed one, and tutoring centres would advertise that “everyone needs help.” In some, especially affluent communities, paid tutors became part of the culture, almost an unspoken prerequisite to keeping up.

That world may be coming to an end.

AI has entered the tutoring business, and it does not take nights or weekends off. For the first time, students have access to personalized, immediate feedback and explanations any time they need it. They can ask follow up questions without embarrassment, get alternative explanations and have complex problems broken into smaller steps. All of this is available for free, or for the price of a phone app. The model that tutoring companies built around scarcity and exclusivity is being replaced by abundance and accessibility.

It is not only about convenience. Tools like ChatGPT, Claude, and Magic School AI can act as math coaches, writing mentors and language partners. They remember the work, adapt to a student’s level, and adjust explanations when the learner gets stuck. The value proposition that human tutors once held, personalization, is becoming a default feature of modern AI systems.

Just last week, one of our Grade 12 students shared how she had been struggling with integration by parts in calculus. Instead of waiting for a weekly tutoring session, she worked through problems with an AI tutor at 11 p.m., asking it to explain the same concept three different ways until it clicked. “It never got frustrated when I asked the same question again,” she said. “And I could be honest about what I did not understand.”

When I first started drafting this piece, I was ready to declare the end of the tutoring era. The evidence seemed clear. The assumption that you need a tutor to survive Pre Calculus is being upended. For many students, the AI sitting quietly on their laptop or phone now fills that role, often better and more patiently than the Saturday morning sessions they once dreaded.

Then I started reading the research. And my thinking got more complicated.

What the Research Actually Shows

The October 2025 edition of AASA’s School Administrator magazine dedicates significant space to the state of tutoring in American schools. AASA is an American based organization, but the questions it raises cross borders easily. The tension between equitable access and quality instruction, the challenge of sustaining initiatives beyond initial funding, the promise and limits of technology in supporting learners: these are Canadian conversations too. The research may come from Texas and Massachusetts, but it speaks directly to what we are wrestling with in British Columbia and across the country.

Liz Cohen, in her article drawing from her book The Future of Tutoring: Lessons from 10,000 School District Tutoring Initiatives, documents an unprecedented expansion. Within a year of the pandemic’s onset, 10,000 U.S. school districts were offering some form of tutoring after years of almost none. By May 2024, 46 percent of public schools reported providing high dosage tutoring, and just 13 percent said they offered no tutoring at all.

Research from the Johns Hopkins Center for Research and Reform in Education, featured in this issue, offers evidence that virtual tutoring with human tutors can produce meaningful results. Grade one students assigned to Air Reading, a structured virtual tutoring program, four times a week for a semester gained nearly 1.6 additional months of learning. Those who attended at least 40 sessions saw even greater progress.

But here is the tension that caught my attention: the research consistently shows that the most effective tutoring models still rely on human tutors. Studies on AI tutoring directly with students remain in early stages, and even the most promising work positions AI as supporting human tutors rather than replacing them

I had to sit with that for a while.

The Hybrid That Works

One case study which helped my framing was learning about the work happening in Ector County ISD in Texas. In partnership with Stanford University, they developed something called Tutor CoPilot. It uses AI not to tutor students directly, but to coach human tutors in real time, suggesting questions to ask, concepts to revisit, hints to offer.

The results are striking: students whose tutors used the AI prompts scored 14 percentage points higher than those whose tutors did not. The AI shifted tutors toward stronger pedagogy, guiding student thinking rather than simply giving away answers. And here is the part that matters most for equity: the greatest benefits went to less experienced tutors. The tool essentially democratized tutoring quality, helping novice tutors perform nearly as well as veterans.

This is not AI replacing humans. This is AI and humans amplifying each other.

What AI Cannot Yet Do

Cohen’s research surfaces something that pure AI cannot yet replicate. The success of tutoring, she argues, is deeply rooted in human relationships. It helps young people feel they matter. It builds motivation through productive struggle in a high support, high standards environment Cohen (This podcast is also a good background on Cohen’s work).

There will still be families who seek human tutors, especially for accountability or emotional connection. Some students need the structure of showing up, the social pressure of not wanting to disappoint someone, or simply the reassurance of a person saying “you’ve got this.” AI has not yet mastered the art of knowing when a student needs a break, a pep talk, or someone to believe in them.

The question is whether it will, and how soon.

The New Digital Divide

For schools, this raises urgent questions. Do we teach students how to use AI tutors effectively? How do we ensure that all students, not only the digitally confident, benefit from these new tools?

The digital divide is no longer just about device access. It is also about knowing how to prompt effectively, when to question an AI response, and how to use these tools for learning rather than answer getting. A student with strong digital literacy might turn ChatGPT into a Socratic tutor. Another might never get past using it as a homework completion machine. If we are not careful, digital confidence becomes the new proxy for privilege, only with different packaging.

There is another issue to face. If every student has a tutor at all hours, what does authentic assessment look like? How do we measure understanding when the line between getting help and getting answers is blurred? This is not a reason to resist change. It is a reason to rethink what we are measuring and why.

What I Got Wrong, and What I Got Right

The shift is cultural as much as it is technological. For years, tutoring companies helped reinforce the idea that school alone was not enough. Now, AI is challenging that notion and putting powerful learning tools directly in the hands of students. I was right about that.

But the real revolution may not be the end of tutoring. It may be its transformation.

This changes the teacher’s role as well. When information delivery and step by step support are available on demand, teachers become something more valuable. They become learning architects who design rich tasks. They become coaches who know when to push and when to support. They become mentors who help students navigate not only content, but the process of learning itself. The human element does not disappear. It becomes more essential, only with a different focus.

We may soon look back on the tutoring era the way we look at encyclopedias and phone books. Useful for their time, but unnecessary once the world changed. Or we may find that the future looks more like Ector County: AI and humans working together, each amplifying what the other does best.

Maybe what we should have wanted all along was not a system where extra help was a luxury, but one where every student has access to the support they need, when they need it, in the form that works best for them. Whether that form is human, AI, or some combination we have not yet imagined.

The question is not whether this change is coming. The question is whether we will shape it with intention, or let it happen to us.

Thanks to Liz Hill and Andrew Holland with whom I had recent conversations that helped inspire this post.

 

The image at the top of this post was generated through AI.  Various AI tools were used as feedback helpers (for our students this post would be a Yellow assignment – see link to explanation chart) as I edited and refined my thinking

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Is Prompt Engineering Really the New Future Skill?

Posted in Change, Technology, tagged , on November 19, 2025| 4 Comments »

Last week, I sat in an education conference listening to a keynote speaker who was absolutely unequivocal about it: students must learn prompt engineering or they will be left behind. The speaker was passionate, convincing even, about how this was the essential skill for the next generation. And as I sat there, I found myself thinking: really? Is this truly the skill we should be racing to embed in every curriculum?

Lately, I keep hearing that prompt engineering, the ability to write clever and precise instructions for AI, is the new super skill every young person needs to master. The idea is that those who can “talk to the machine” will be the ones who thrive in the age of generative AI.

And I get it. For now, it is true. Anyone who spends time with AI knows that the way you ask matters. A well-structured prompt can turn an average response into something remarkable. I have seen entire professional development sessions focused on how to write the perfect prompt.

But I keep wondering if this is really a future skill or simply a transitional one.

We have been here before. About a decade ago, coding was the next great literacy. We were told that all students needed to learn to code or they would be left behind. And while understanding logic, pattern recognition and computational thinking remains valuable, few would now argue that every student must become a programmer. The tools evolved. The interfaces changed. Knowing how to code shifted from a universal requirement to an optional asset.

I suspect the same will happen with prompting. The models are already becoming much more forgiving. Early versions of AI required carefully worded instructions and detailed context. But each new generation of large language models has become better at interpreting vague or natural language. They are now more context aware, more visual and better aligned with human intent. The need for carefully engineered prompts is already beginning to fade.

Even the interfaces are changing. Most people will not type directly into chatbots in the future. They will use AI features inside tools such as Google Docs, Canva or Notion that quietly handle the prompting behind the scenes. The software will translate our natural requests such as “summarize this,” “improve the tone,” or “make it more visual” into optimized prompts automatically. Just as we no longer type code to open a file, we will not need to craft perfect prompts to get great AI output.

There may be a split happening here. For most of us, prompting will become invisible, handled by the interface layer. But specialized roles might still require deep prompt engineering expertise for critical systems or highly creative work where nuance matters. It could mirror how we still have systems programmers even though most people never write a line of code.

Modern AI systems are also being trained on millions of examples of strong instructions and responses. They have learned the meta-skill of interpreting intent. Clear and simple language now produces excellent results.

So if the technical part of prompting is becoming less necessary, what remains essential? The human part. Knowing what to ask. Evaluating whether the answer is right. Recognizing when a response is insightful, biased, or incomplete. The real differentiator will be judgment, not phrasing. The skill will not be in writing prompts but in thinking critically about what those prompts produce.

There is something deeper here too. The enduring skill might be what we could call AI collaboration literacy—the ability to iterate with AI, to recognize when you are not getting what you need, and to adjust your approach, not just your words. It is less about engineering the perfect prompt and more about developing a productive working relationship with these tools.

It reminds me of the evolution from coding to clicking. Early computer users had to memorize complex commands. Now, we all navigate computers intuitively. Prompt engineering feels like today’s command line, a temporary bridge to a more natural future.

So yes, teaching students to think like prompt engineers has value. It helps them be clear, curious and reflective. But perhaps the goal is not to create great prompters. It is to create great thinkers who can:

  • Articulate clear goals and constraints

  • Recognize the difference between excellent and mediocre output

  • Maintain healthy skepticism and verification habits

  • Understand when AI is the right tool versus when another approach works better

  • Iterate and refine their collaboration with AI systems

These capabilities feel more durable regardless of how the interfaces evolve.

Maybe I am wrong. Maybe prompt engineering will become a lasting communication skill. But before we rush to build it into every curriculum, it is worth asking whether we are chasing a moving target, and whether we should focus instead on the deeper cognitive skills that will matter no matter how we end up talking to machines.

As always, I share these ideas not because I have the answers but because I am still thinking them through. I would love to hear how others are thinking about this from where they sit.

The image at the top of this post was generated through AI.  Various AI tools were used as feedback helpers (for our students this post would be a Yellow assignment – see link to explanation chart) as I edited and refined my thinking.

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The Human Half: AI, the OECD, and the Future of Educational Leadership

Posted in Change, Personalized Learning, Technology, tagged , , , , on November 12, 2025| 2 Comments »


Inspired by the recent Learning Forward BC conversation on human flourishing and AI.

Last week, I spent three hours tweaking a PowerPoint presentation I already had help with. At the same time, I had to decline a visit to an elementary class exploring AI tools. The irony? While I was perfecting slides, they were shaping the very future I was supposed to be leading them toward.

If we are honest, most of us superintendents spend far too much of our energy doing work that does not require the full force of our humanity. We draft versions of the same report again and again for different audiences. We shuffle through data systems, chase signatures, and repackage findings. It is necessary work, but is it what we were called to?

At a recent Learning Forward BC event on The Intersection of Human Flourishing and AI, that question hit home. We were exploring how technology might liberate, not limit, our humanity in education. It made me wonder: What if AI could take over significant portions of our work as leaders? What would we hand over, and what would we fight to keep?

Why This Matters for Leaders

I have written a lot on this blog about how AI is reshaping the work of teachers and students. But we need to look just as critically at our own work as superintendents and senior leaders. If we expect educators to rethink assessment, planning and feedback in an AI-rich world, then we must also examine the way we lead, communicate and make decisions.

The truth is that the same technology that can help a teacher personalize learning or a student write an essay can also help a superintendent analyze data, summarize reports or draft correspondence. AI is not only changing classrooms. It is changing the nature of leadership itself.

And yes, I am sure some superintendents might already be wondering if a chatbot could replace them at board meetings. But since I know my trustees often read this blog, I will not take the chance of testing that particular joke here.

The Question That Changes Everything

The OECD’s (Organisation for Economic Co-operation and Development)  Education for Human Flourishing framework reminds us that our purpose in education is to equip people to lead meaningful and worthwhile lives, oriented toward the future. If that applies to students, it applies to our leadership too.

So whether it is 30 percent, 50 percent, or even 70 percent of what we currently do, the question becomes: What would we hand over to AI, and which tasks would we hold on to because they matter most?

What We Could Let Go Of

AI is already remarkably good at tasks that drain our time but not our meaning:

  • Drafting first versions of reports, memos and letters
  • Crunching and summarizing enrolment or survey data
  • Managing meeting notes, calendars, reminders and task lists
  • Building templates, presentations and standard job postings
  • Drafting policy or procedural documents for refinement

These are automation, not animation. They do not require empathy, judgment, or nuance, only accuracy and speed. That is AI’s strength.

What We Must Protect

What we must protect, deliberately, are the moments of human connection, purpose and complexity:

  • Sitting with a parent whose trust in the system has eroded
  • Listening deeply to a principal wrestling with burnout or vision
  • Reading the room in a board meeting and knowing what not to say
  • Inspiring staff to believe in something greater than their daily tasks
  • Recognizing a student’s spark when they realize someone believes in them

These are leadership moments: irreducible, unautomatable and profoundly essential.

Leading for Human Flourishing

The OECD highlights three human competencies that AI cannot fully replicate: adaptive problem-solving, ethical decision-making and aesthetic perception.

Adaptive problem-solving: When a community crisis hits and there is no playbook, whether a sudden school closure, a traumatic event, or a divided community, we respond with creativity born from experience and intuition.

Ethical decision-making: When budget cuts force impossible choices between programs, when we must balance individual needs against the collective good, when integrity demands the harder path, these moments require moral courage that no algorithm can calculate.

Aesthetic perception: Recognizing when a school’s culture shifts from compliance to inspiration, sensing the exact moment a resistant team begins to trust, and seeing beauty in a struggling student’s small victory. This is what makes leadership an art, not just a science.

AI can mimic these competencies, but it does not feel them. It may calculate empathy, but it cannot experience it or show it. As more of our routine tasks shift to AI, the invitation is clear: we reclaim the human half.

Creating a Culture of Yes

This is where AI becomes an enabler of possibility rather than a threat to purpose. When AI handles the bureaucratic “no” work, the forms, compliance checks and procedural barriers, we create space for the human “yes.”

Yes, I have time to visit your classroom.
Yes, let’s explore that innovative idea.
Yes, I can truly listen.

In a Culture of Yes, AI does not replace us. It liberates us to be more fully present for what matters. Every report AI drafts is a conversation we can have. Every dataset it analyzes is a relationship we can build. Every schedule it optimizes is a moment we can use to connect.

Getting Started

This is not about wholesale transformation tomorrow. It is about small experiments.

What one repetitive task could you delegate to AI this week? What human conversation would that free you to have?

Start simple:

Use AI to draft that routine memo, then spend the saved time walking the halls.

Let AI summarize survey data, then use your energy to discuss what it means with your team.

Have AI create the meeting agenda, then focus fully on reading the human dynamics in the room.

The goal is not efficiency for its own sake, but reclaiming time for what only we can do.

The Real Promise

The promise of AI in leadership is not efficiency, but rediscovery.

It is the chance to release ourselves from the burden of mechanical work and return to the heart of leadership: human connection, meaning and moral purpose.

Imagine walking into your office tomorrow knowing that the reports are drafted, the data analyzed and the calendar managed, all before your first coffee. Now you can spend your morning where it matters most: in classrooms, with people, making meaning.

Because in the end, the future of education will not belong to the most efficient systems. It will belong to the most human leaders, those who use every tool available to protect and amplify what makes us irreplaceably human.

A Question to End With

I wonder if my list looks like yours. What would you hand over to AI, and what would you hold tightly because it feels essentially human? I would be interested to hear how others are thinking about their human half.

 

 

The image at the top of this post was generated through AI.  Various AI tools were used as feedback helpers (for our students this post would be a Yellow assignment – see link to explanation chart) as I edited and refined my thinking

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Beyond the Score: Can AI Make Literacy Screeners More Human?

Posted in Assessment, Change, Personalized Learning, tagged , , on November 4, 2025| Leave a Comment »

Across Canada, and in many other parts of the world, literacy screening is having a moment.

There is broad agreement that we need to be better at identifying students who may be at risk, and that we need to do this earlier. The push toward more consistent and universal literacy screeners makes a lot of sense: earlier identification leads to earlier intervention, and ultimately, better outcomes for kids.

But here’s the question that’s been nagging me: are we simply going to recycle the same kinds of screeners we have used for the last generation? Or can this be the moment to think differently about what screening could look like in an AI world?

What Screeners Do Well

Traditional screeners help us establish a baseline. They can tell us if a student is meeting expected benchmarks in areas like phonemic awareness, decoding, fluency and comprehension. They provide the data teachers need to take action.

The challenge is that screeners often leave a gap between assessment and action. A teacher receives a score and then has to translate that number into the “what’s next” for the student and their family. It’s useful, but not always immediate, personalized or engaging.

What AI Could Add

This is where I wonder if we are missing an opportunity. AI could allow us to rethink the very design of literacy screeners. Imagine if…

  • Texts were customized for cultural relevance. Instead of one-size-fits-all passages, AI could generate short reading texts tailored to the learner’s context, interests or community. A child on the North Shore might read about the Capilano River, while another in Surrey reads about the Pattullo Bridge reconstruction. For Indigenous learners, this could mean texts that reflect Indigenous ways of knowing and storytelling traditions, developed in partnership with local Nations. The text would still be controlled for vocabulary and difficulty, but it would feel more real and more personal.

  • Feedback was immediate and audience-specific. A student could receive a friendly message highlighting a win (“You read 80 words per minute—your smoothest word was ship”) and a tip for next time. Families could receive a plain-language summary with simple routines for home (“Read together for 10 minutes tonight; circle the words that start with sh”). Teachers could receive a strand-level profile with small-group suggestions, not just a number on a page.

  • Practice was built-in. Instead of waiting for the next lesson, a screener could instantly generate a few targeted practice items based on the patterns the student struggled with, turning assessment into a learning moment instantly.

What This Isn’t

To be clear, this isn’t about replacing teacher expertise or professional judgment. Teachers would still interpret results, make instructional decisions, and build the relationships that matter most.

And this isn’t about creating more data for data’s sake. It’s about making the data we already collect more immediately useful—for students, for families and for teachers.

Safeguards Matter

Of course, any AI use comes with important guardrails. Automated scores would need validation against human judgment, with teachers maintaining override authority. Generated texts would require review for accuracy, bias and cultural safety. Indigenous content, in particular, would need to be co-designed with local Nations and aligned with principles of data sovereignty, ensuring that AI tools serve rather than appropriate Indigenous knowledge.

Quality oversight would need to be built in from day one, with regular audits and continuous monitoring to prevent the kind of drift that could undermine both accuracy and equity.

A Narrow Window

Here’s what makes this moment unique: jurisdictions are investing in new screening initiatives right now. We have a narrow window to influence how these tools are designed. If we don’t explore these possibilities now, we risk locking in approaches that simply digitize yesterday’s thinking.

I am not a literacy expert. But as someone who has watched technology reshape almost every other part of our schools over the last two decades, I see a pattern. The organizations that thrive are the ones that ask not just “how can we do what we’ve always done, but faster?” but “what becomes possible now that wasn’t possible before?”

The Question We Should Be Asking

The push for literacy screening is the right one. The evidence on early identification and intervention is clear. But we also have a unique opportunity to do more than just import the same tools from the past.

What if, instead of only identifying students who need help, our screeners could also immediately provide that help?

What if they could engage families in ways that feel supportive rather than clinical?

What if they could give teachers not just data, but insight?

AI won’t replace the expertise of our teachers or the relationships that matter most. But it might make our tools more immediate, more relevant and more effective for every child.

The question isn’t whether we should innovate. The question is whether we will seize this moment to innovate thoughtfully—or let it pass by.

What new possibilities are you seeing in your corner of education? And how do we make sure we are not just replicating the past with shinier tools?

Thanks to West Vancouver District District Vice-Principal Mary Parackal who really pushed my thinking in creating this post around what might be possible with AI.

The image at the top of this post was generated through AI.  Various AI tools were used as feedback helpers (for our students this post would be a Yellow assignment – see link to explanation chart) as I edited and refined my thinking.

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Modeling AI for Authentic Writing

Posted in Change, SD45, Technology, tagged , , , , on October 9, 2025| 4 Comments »

How I draft, edit, and stay human in the loop

For years I believed my advantage was “writing.” Lately I’ve realized the real edge was not keystrokes, it was ideas, structure, and voice. AI has not erased those. If anything, it has made them more important. Rather than pretend we are still in a pen and paper world, I have been trying to model what authentic writing looks like now.

We do not protect writing by banning the tools everyone already has. We protect writing by showing what thoughtful use looks like, and by being transparent about our process.

What I am hearing, especially in humanities

Last week, a high school English teacher stopped me. “I can tell when something has been AI generated,” he said, “but I cannot tell when they have collaborated with it thoughtfully. And I do not know what to do with that.”

He is not alone. Across our humanities departments, teachers are working on the fly, trying to maintain academic integrity while recognizing that the old gatekeeping moves, ban the tool and police the draft, do not hold when every student has ChatGPT in their pocket. The fear is real. Are we farming out the exact skills we are supposed to be teaching?

I do not think the answer is choosing between integrity and innovation. It is redefining what integrity looks like when the tools have changed.

How I actually write

I still start the old fashioned way, an outline, a thesis, a few proof points, and usually one sentence I think could be the closer. From there, I treat AI like a colleague, not a ghostwriter.

  • Editing help. I ask for a clarity pass, tighten verbs, fix hedging, and check whether my headings are parallel. Here is what I actually typed for this piece: “Revise for clarity and concision. Keep a conversational, hopeful tone similar to my other blog posts. Offer two options for the opening sentence.” I kept one, rejected the other, and moved on.

  • Skeptic check. “What would a fair skeptic say after reading this” It surfaces blind spots before I hit publish.

  • Reports and formatting. For formal documents, I use AI to turn tables into charts, crunch numbers, and reshape dense text into something readable.

  • Speeches. I keep a base grad speech and add school specific stories and names. AI helps blend those elements while keeping the message consistent.

None of this replaces judgment. I accept or reject every change. If a suggestion dulls my voice, it is out. That is the standard. My judgment stays in control. I also disclose what I did, every time. A short note at the end of a post goes a long way with our community and models the behavior we ask of students.

What I encourage for classrooms and staff rooms

The most helpful shift has been moving from “Do not use AI” to “Show your decisions.”

  • Model, then mirror. I demo my messy paragraph, ask AI for a clarity edit, then accept or reject in real time while explaining why. Students should bring their draft, try the same process, and compare choices.

  • Assess the thinking. Rubrics weight claims, evidence, organization, and audience impact, not who placed the comma.

  • Make the process visible. Version histories in Docs or Word, plus brief process notes that list tools used, prompts asked, and choices made, make learning visible and deter abdication of thinking.

  • Cite the workflow. Not to catch people out, but to name steps we can teach.

Guardrails that keep the work honest

  • No blank page outsourcing. Start with your outline, thesis, or key points.

  • Ask precise questions. “Cut 10 percent without losing meaning. Keep my conversational tone.”

  • Verify facts. If AI offers a claim, check it before it lands in public.

  • Always disclose. If a tool shaped meaning or form, say how.

Is this just cheating with better branding

I have never believed collaboration was cheating. When I wrote a newspaper column, my dad, a retired English teacher, was my unofficial copy desk. He proofread, edited, and offered suggestions on every draft. The byline was still mine because the ideas, voice, and final choices were mine.

Tricia Buckley, and before her Sharon Pierce and Deb Podurgiel, all staff in West Vancouver Schools, have read every blog post here before they were published and provided feedback.

AI sits in that same category for me, a helper, not a ghostwriter, and always subject to human judgment. What changed with AI is speed, scale, and availability. I can get feedback at 11 p.m., run ten drafts in twenty minutes, and the tool is always on. What did not change is my judgment, my responsibility for choices and my name on the work.

If the goal is proving you can type unaided, then yes, tools muddy the waters. Our goal in schools is thinking for real audiences. We have always used supports, outlines, spellcheckers, style guides, writing partners, rubrics and colleagues. The standard should be integrity and evidence of learning, not tool abstinence.

Equity

AI is a ramp, not a shortcut.

It helps stuck writers get moving, the student staring at a blank page who needs a sentence to react to, or the English language learner who can articulate ideas verbally but struggles with syntax. AI can generate that first sentence, and suddenly the student has something to revise, reject, or build on. For strong writers, it is a way to go deeper, test alternate structures, get a skeptic to read, or polish a conclusion without losing momentum.

The equity move is not banning tools for everyone. It is teaching how to use them responsibly, and ensuring access to good instruction is not the new dividing line. When we teach tool literacy, we level up. When we ban tools students already have, we make the learning invisible.

Prompts that actually help

  • Clarity pass: “Revise for clarity and concision. Keep a conversational, hopeful tone. Offer two options for the opening sentence.”

  • Skeptic lens: “List the strongest fair minded critiques of this piece and one concrete improvement for each.”

  • Structure check: “Are these headings parallel? Tell me how to fix them without changing the ideas.”

  • Audience flip: “Rewrite the conclusion as guidance to parents in about 120 words.”

  • Report polish: “Turn this table into three plain language insights and a simple chart title. Flag any numbers that look inconsistent.”

What I tell our community

  • We are pro-writing and pro-truth. We will use modern tools and we will say when we did.

  • We value voice. Your voice should be recognizable across drafts and tools.

  • We lead with learning. If a tool helps learning, we will teach it. If it replaces thinking, we will not.

If you want more

Last week I facilitated a Hot Topic discussion, “The Future of Writing in an AI World,” at the Canadian K12 School Leadership Summit on Generative AI. 

North Star

I can spend my time lamenting that writing once felt like my competitive edge, or I can double down on the edge that still matters, clear thinking, vivid stories and the courage to be transparent about how we work. That is the blended human and AI writing world I want to model for students and staff.

The teacher who stopped me in the hallway was right to be uncertain. We are all figuring this out in real time. I would rather figure it out in the open, and model a messy and honest process, than pretend the tools do not exist.

AI transparency note: I drafted this post myself, then used ChatGPT and Claude for a clarity edit and a skeptic read. I accepted some wording suggestions and rejected others to preserve voice. The image at the top of the post was created through a series of prompts using Claude.

 
 

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Reimagining Learning in the Age of AI: A Culture of Yes

Posted in Change, Technology, tagged , , , on August 18, 2025| 7 Comments »

I recently gave a virtual talk on AI in schools which forced me to solidify my current thinking and I tried to make some direct linkages to the Culture of Yes belief. I have included the video at the bottom, and this post is an adaption of the talk:

This summer, AI in education has gone from a quiet undercurrent to a headline wave. Major corporations have announced new AI powered tools for classrooms. Governments, particularly in the United States, have released statements, strategies, and funding commitments to “prepare schools for the AI era.” There is a growing sense, both excitement and urgency, that this technology will profoundly reshape learning.

As we head into the fall, the question for me is not whether AI will change education. It already has. The real question is: Will we guide this change with wisdom, or will it guide us?

Where We Are:

We are in a moment of intense attention and investment. For the first time in history, students have instant access to a form of intelligence that can write, create, and problem solve alongside them. The conversation has shifted from “Should AI be in schools?” to “How do we use it well?”

The opportunities are extraordinary, and so are the risks. In our rush to adopt tools, we can easily mistake activity for progress. AI is not a magic box. It reflects the data and the biases we feed it. Without careful integration, we risk amplifying inequities instead of closing them.

At the same time, teachers are navigating new pressures: learning unfamiliar tools while managing existing workloads, and working with students who arrive with vastly different levels of AI experience and access.

What I Hope:

In West Vancouver, our innovation priorities are as bold as they are deliberate: AI and physical literacy. Together, they reflect our belief that the future belongs to students who are digitally fluent, physically confident and deeply human.

My hope for AI is that it:

Amplifies human wisdom rather than replacing human intelligence.

Delivers personalized learning that has long been promised but rarely achieved.

Serves as a force for equity, not by assuming all students need the same thing, but by providing each student with the individualized support they need, regardless of their school’s resources or their family’s circumstances.

Frees up teachers’ time for what matters most: relationships, mentorship and inspiration.

In a Culture of Yes, we approach these possibilities with openness while remaining thoughtful about implementation.

What We Need to Do:

Focus on the Shift: From Memory to Meaning

For over a century, schools rewarded students who could store and retrieve information. AI changes that rote memorization game. We must now prioritize what students do with the knowledge — how they apply it, question it, and create from it.

Equip Students as Creators, Not Just Consumers

In a Culture of Yes, we say yes to new possibilities while maintaining academic integrity. AI becomes a collaborator for composing music, designing solutions to local challenges and exploring ethical dilemmas we have never faced before, not a replacement for student thinking.
Imagine a Grade 9 student co writing a play with AI, then performing it with peers, learning as much about collaboration and creativity as they do about technology.

Develop New Literacies

AI literacy is more than knowing how to use a tool. It is the ability to:

Prompt effectively and creatively.

Evaluate outputs for accuracy and bias.

Reflect on whether AI use aligns with human goals and values, and recognize when not to use it.

Understand the difference between AI assistance and AI dependence.

Lead Through Diffusion, Not Mandate

A Culture of Yes means saying yes to teacher curiosity and experimentation. The best AI integration spreads from teacher to teacher, classroom to classroom, through shared practice and professional learning, not top down directives that ignore classroom realities.  When your colleague in the classroom next to you has something exciting to share, you are keen to listen to them. 

Keep Humanity at the Core

AI can provide information, but only people provide inspiration. AI can offer feedback, but only people offer hope. We must ensure that every learning experience remains fundamentally about human connection and growth.

Looking Ahead

The age of AI is not coming, it is here. As educators, leaders, and communities, we face a choice that will shape the next generation’s relationship with both technology and learning itself.

A Culture of Yes means we choose:

Curiosity over fear

Collaboration over competition

Wisdom over efficiency

Human potential over technological convenience

If we embrace this approach, saying yes to AI’s possibilities while saying yes to our students’ humanity, we will not just reimagine learning. We will create classrooms where technology serves human flourishing, where every student can thrive, and where the future we are building together reflects our highest aspirations for education.

The conversation about AI in education is just beginning. As we step into this new school year, I invite you to share your hopes, your experiments, and your questions. We learn best when we learn together.

 

Various AI tools were used as feedback helpers (for our students this post would be a Yellow assignment – see link to explanation chart) as I edited and refined my thinking.

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