Welcome to Summer!

 Summer School (The Good Kind)

Every year around this time, teachers start doing that particular mental math that only educators understand. How many days left? How many grades to enter? How many “are we doing anything today?” questions can one human absorb before the final bell? Summer is close, and the collective exhale is almost audible from here.

Most teachers I know fill that summer with a genuinely good mix of things: a conference or two, some professional reading they actually want to do, travel, family, and the radical act of sleeping past 6 AM. All of that is necessary and right. But I want to make a small case for adding one more thing to the list, and I promise it does not involve a syllabus or a rubric.

Get curious about GenAI.

Image Created with ChatGPT

Not in a high-stakes, district-mandate, someone-is-watching kind of way. Just curious. Summer is exactly the right time for this because the stakes are low. Nothing you try will scramble your fall classes. No 7:45 class depends on the quality of the output. You can experiment, fail, laugh at the results, and try again. That is almost never the condition under which teachers get to learn anything, and it is honestly the best condition for learning there is. Actually, the work we have done on ArtTEAMS grant for four years did exactly that each summer- but that is a story for another time. Although if you are interested our first publication just came out here.

Here are four things worth trying. These are just examples, feel free to riff…

Punch up an assignment you never had time to fix. We all carry them. The assignment that is fine, technically, but has been quietly bothering you for years because you know it could be better. Give a GenAI model the context, the learning objectives, and what you actually want students to get out of it, then see what it generates. I did this recently with an assignment where I always wanted students to think about instructional grouping using both assessment data and social and behavioral dynamics. I never had time to write meaningful student descriptions that would give the grouping decision real texture. This year I used GenAI to create those descriptions, and the assignment became substantially richer for it. The model did not replace my thinking. It handled the production work that had always been the obstacle.

Write a subject-specific AI policy. Counterintuitively, GenAI is quite good at helping you write a policy about GenAI. Start by feeding the model your district or institution’s existing policy, then describe the specificity and intentionality you want. I took this a step further and created differentiated expectations by assignment type: some asked students not to use AI at all, some encouraged proactive use, and some allowed it with disclosure. The clarity that came out of that process saved real class time and preempted a lot of the ambiguity that tends to eat up energy in the first weeks of a semester.

Make a document or presentation. The productivity tools built into or adjacent to GenAI have gotten genuinely good. I recently used one to produce a one-page project explainer that came out polished enough that I only needed to read and edit rather than draft from scratch. You still have to read it carefully and make it yours. But production time drops significantly, and for teachers who spend hours on materials that students may glance at for forty-five seconds, that math is worth considering.

Build a piece of software. This one surprises people, but it should not. GenAI is remarkably capable of creating small, functional, classroom-specific applications. I built an HTML-based reading fluency tracker that listens to a student read and another that scaffolds phonics practice. Both came out well beyond what I could have produced working alone, and neither required me to know how to code. The tools you actually want for your specific students, in your specific context, are more within reach than most teachers realize.

None of this requires a workshop. None of it needs to be assessed or reported. The only goal is to get your hands on the tools in a context where playing around is the whole point. That is how you develop genuine intuition about what these tools can and cannot do, which turns out to be far more useful than any training session.

So yes, go to the conference. Read the book. Take the trip. Sleep in. Spend lots of time with family and friends. Grill often. Drink occasionally.

And somewhere in there, try a few things. No pressure, no deadlines, no one watching. Just you, a curious question, and a summer with enough room to find out what happens. Or not, after all, it is up to you.

Access, Time, and Permission: The Only AI Implementation Plan Schools Actually Need

 One of my friends told me this week that his company selected him to get an enterprise-linked access to ChatGPT. That was essentially the whole announcement. No training. No onboarding. No dedicated time to figure out what the tool could actually do for him. Just: you are one of the chosen ones, good luck.

I have seen this before. Every educator reading this has seen this before. An emerging technology is advocated for, excitement is rising and somebody decides to be innovative… and get stuff (devices, licences, keynote).

A decade ago, the Los Angeles Unified School District handed out iPads to hundreds of thousands of students with enormous fanfare and enormous hope. The technology was real. The potential was real (my many YouTube episodes on iPad in the Classroom show that I thought so). What was missing was everything else. Within months, students were bypassing security filters, devices were sitting in carts, and the program became a cautionary tale about what happens when you mistake access for implementation. A multi-million-dollar lesson in the difference between dropping technology into schools and actually integrating it into teaching and learning.

Schools and districts tend to make one of two mistakes with new tools. The first is the drop-and-hope approach: put it in teachers’ hands, trust the magic, and assume the early adopters will figure it out while everyone else quietly waits for the whole thing to blow over. Some teachers do flourish this way. They always have. But building a strategy around the people who would thrive under almost any conditions is not a strategy. It is luck wearing a professional development badge.

The second mistake is the overcorrection. Spooked by the chaos of the first approach, a nervous school board meeting, or a headline about students using AI to cheat, administrators reach for control. Approved use cases. Acceptable use policies that arrive before anyone has had a chance to discover what the tool is actually good for. Committees to review whether a teacher can use a particular prompt. I sat on enough meetings to understand the instinct. I do not think it works.

Top-down directives have their place, but they tend to work against the specific character of open-ended generative AI, which is a technology that reveals its value through exploration, through iteration, through the kind of messy experimentation that does not fit neatly into a compliance framework. When you over-control it, you do not reduce the risk. You just guarantee it will not add value. The value has to be discovered, while directives should just serve as guardrails. A guardrail like Don’t use a private license is good, but if you overprescribe (here is a list of 5 approved prompts), then the magic of AI solutions will not emerge.

There is a related trap worth naming. We tend to hold new tools to a standard we never applied to the ones they are replacing. A teacher who uses AI to generate a first draft of a differentiated worksheet does not need that draft to be perfect. She needs it to be better than starting from scratch at nine o'clock on a Tuesday night. The relevant question is never "is this flawless?" It is "does this create more value than what I was doing before?" A xeroxed (I love seeing this word in print) worksheet from 1987 never got interrogated for its limitations. A handwritten comment on a student essay full of the same four pieces of feedback never triggered a committee review. But ask a teacher to try an AI tool and suddenly the bar is perfection, or close enough to it that any error becomes evidence the whole enterprise was a mistake. That is not a standard. That is a way of protecting the status quo by demanding that anything new better be perfect before it earns the right to exist. Experimentation requires permission to be good enough before it gets to be great.

What actually works is harder to mandate but not hard to describe. Teachers need protected time to try things and get them wrong, ideally alongside colleagues who are doing the same. The teacher down the hall who figured out how to use AI to generate differentiated discussion questions for her mixed-ability class is more valuable than any vendor demo, and she will share what she learned over lunch if you give her half a chance. That kind of peer-to-peer learning does not happen by accident. It needs structure, and it needs time carved out rather than squeezed in.

Teachers also need genuine permission. Not the kind that comes with a wink and an asterisk. Not “feel free to explore as long as nothing goes wrong and no parents call.” Real permission, backed by administrators who are willing to say publicly that experimentation is part of professional practice and that not every attempt needs to produce a polished outcome on the first try. That kind of permission is rarer than it should be.

And yes, resources matter. Devices and subscriptions, yes, but also the human infrastructure around them. If a district brings in outside expertise, it needs to be the kind that stays. Not a keynote, not a one-day workshop, not a framework delivered from a podium to a gymnasium full of teachers who have four other things on their minds. The support that actually changes practice is boots-on-the-ground, working alongside teachers in their specific contexts over a sustained period, helping them find the nooks and crannies where AI genuinely makes their work better rather than just different.

The formula is not complicated. To discover what a genuinely open-ended technology can do inside schools, you need three things: access, time, and permission. All three, together, sustained long enough for something real to develop.

Some will figure out something useful on their own. Teachers are resourceful. But teachers deserve better than resourcefulness as the plan. They deserve the conditions that make genuine professional learning possible.

AI, Privacy, and the Context Conundrum

Something interesting happened recently in a conversation with Claude. I had been using a series of prompts recommended by Daniel Pink to do a kind of personal audit, and based on those conversations, I made some genuine changes. But I also noticed something that gave me pause.

Claude concluded that I was spending way too much time on administrative tasks and not enough on creative and research work. And while there is probably a kernel of truth in that, it was not quite right. The reality is that I lean heavily on AI for administrative tasks, and far less so for research and creative work, where most of my thinking happens in conversation with colleagues, on walks, or just away from the screen. Claude cannot see that work. What it can see is how I use Claude.

In other words, Claude was making inferences about my whole professional life based on how I have been using Claude. It reminded me of something I tell my students: they assume that because I teach, most of my time must go to teaching. In reality, it is about 40%. The AI was making the same natural, but limited, assumption. It was seeing the visible part of an iceberg and mapping the whole thing.
That was a useful insight on its own. But it pointed somewhere more interesting.

I recently listened to a discussion on the AI in Education podcast about bias in AI grading systems. One recommendation was straightforward: reduce the contextual information you give the AI about students. Remove names, gender, ethnicity. Strip away the signals that could activate bias. The less context, the less opportunity for those patterns to distort the evaluation.

That logic applies to me, too. The less context Claude has about me, the less it can stereotype or misread my work patterns. But here is where the conundrum arrives.

Context is precisely what makes AI more helpful.

Take a concrete example. Let me say, hypothetically, that I have a medical condition that makes me significantly less effective between 3 and 5 PM. If I want AI to help me plan my work week strategically, knowing that fact would make a real difference. It could help me schedule demanding intellectual work for the morning and reserve lighter tasks for those two hours. Without that context, I am just getting generic planning advice.But the moment I share that, I have handed a piece of genuinely private health information to an AI system, and by extension, to the company behind it. I may have no idea how that data is used, stored, or surfaced in future interactions. I have optimized for utility at the cost of privacy.
This is the lesson we already learned the hard way with social media. Early location-sharing felt like a fun, low-stakes way to connect. Foursquare check-ins were charming until they weren’t. The lure of personalization is real. The cost is often invisible until it isn’t. We traded something for convenience, and many of us are still sorting out what exactly we gave away.

For our own data, adults get to make that call. It is a tradeoff, and reasonable people will land in different places depending on their values, their risk tolerance, and how much they trust the platforms they use.

But student data is not ours to trade.

This is where I want to be unequivocal. The legal frameworks around student data, FERPA in the United States among them, exist for good reasons. Student data belongs to students and their families. When we use AI tools in educational settings, we are not making personal decisions about our own information. We are making decisions about children and young people who have not consented, who may not fully understand the implications, and who deserve protection.

So the practical guidance here is not subtle. Use only systems that are legally and contractually committed to protecting student data. Minimize the information you expose, even when a tool feels helpful. Resist the temptation of a quick AI fix that requires feeding it student names, identifiers, or demographic information.

The conundrum for adults using AI tools is real and worth sitting with. The tradeoff between context and privacy is genuinely complex.
For students, it is not a conundrum at all. It’s a responsibility.

The Corridor Conversation Deserves a Room of Its Own

 I just got back from Philadelphia, where I spent a few days at the SITE conference, hoping to catch the pulse of where AI in education research is heading. I had a genuinely wonderful time. The people were sharp, the conversations were warm, and there was something quietly reassuring about realizing that the researchers you respect are wrestling with the same questions keeping you up at night.

But I left with a nagging feeling I couldn’t quite shake.

The formal papers left me with a sense of “and…”. This is not because the sessions were not good; many were, and some were excellent. It was just a sense of slow progress. Usually, this is how research advances, and that is fine. But in the age of AI, I felt that it was almost indulgent.

The conference format itself may be the problem. We submit proposals months in advance about research we wrapped up even earlier. By the time we stand at the podium, we are essentially reporting from a different era. In most fields, a year-long lag between doing and sharing is an inconvenience. In AI research right now, it is equivalent to geological time. We are presenting postcards from a past that no longer exists.

So here is what I keep thinking about: what if we flipped the whole thing?

Keep the research sharing, but strip it down to the essential finding. A nugget, not a novella. Tell me what you learned, and your evidence, and then let’s get to work. Because the real value of getting a few hundred serious thinkers into the same building isn’t the formal presentations. It’s what happens in the hallways, waiting for the elevator, over coffee, at the margins. The corridor conversation is where the good stuff lives. Why are we so committed to keeping it out of the rooms?

An unconference model built around AI in education could do something genuinely useful. Picture it in four movements. First, we share what we are actually doing right now. Not a polished study with clean findings, but live, messy, in-progress work. The experiment still running. The instrument we’re not sure about yet. The classroom observation that hasn’t found its theoretical frame. Second, we surface emerging technical solutions and research tools while they are still warm enough to shape. Too often, by the time a new instrument reaches the field through traditional channels, half the community has already improvised its own version, and nobody knows what anybody else is measuring, or everyone is using an instrument that was quickly put together with the notion that we will fix it later, though later never comes. Third, we find the collaborators to move forward with large scale studies. Some of the most generative research partnerships I’ve seen started with someone saying “wait, you’re looking at that too?” in a hotel lobby at 10pm. SITE has created these in the past but let’s build that moment into the schedule. And fourth, we stress-test ideas before they calcify. Bring your half-formed hypothesis, your shaky design, your nagging methodological doubt, and subject it to the kind of rigorous, generous pushback that only happens when you’re in a room with people who actually care and have no incentive to be polite about bad ideas. Here’s the part that excites me most: we could even do research on site. Instrument development happening in real time, with the expertise in the room feeding directly back into the design. That’s not a conference. That’s a lab with better snacks.

But there’s a larger argument underneath all of this, and I think we need to say it plainly. If we want our research to shape the direction of AI in education rather than simply document its wake, we cannot afford to keep working in parallel silos, each of us producing careful (sometimes barely powered) studies that trickle out through journals on an eighteen-month delay while the technology rewrites the classroom underneath us. The speed of AI is not going to slow down to match the pace of peer review. So we have to build something that can move alongside it.

What I am imagining is a kind of AI in education brain trust. Not a new professional organization with dues and bylaws and a nominating committee. We have enough of those. Something leaner and more intentional. A networked group of researchers who agree to aggregate what we know in real time, share findings quickly and in plain language, and respond together when the field needs guidance. A parallel research infrastructure, less well-funded than the AI labs driving these tools, but not beholden to their interests either. Our independence is the asset. The research community knows things about learning, about classrooms, about equity, about what teachers can realistically sustain, that no product team is going to discover on its own. The problem is that knowledge is scattered across conference presentations, working papers, faculty websites, and email threads between people who happened to meet in Philadelphia. A brain trust would gather it, synthesize it, and get it into the hands of practitioners and policymakers fast enough to actually matter.

Because here is what keeps me up at night. The decisions being made right now about how AI enters classrooms, which tools get adopted, what counts as learning, what gets automated and what gets protected, those decisions are being made with or without us. The question is whether the research community shows up to the conversation early enough to influence it, or whether we arrive, as usual, with a beautifully designed retrospective study about something that already happened.

Let’s bring the corridor back into the room. And then let’s build a room that the whole field can use.