Chris Lehmann

Learning, Doing and Generative AI

The other day I was building a data visualization tool for the teachers at SLA, and for the first time in over a decade, I had to write a regular expression. The wikipedia definition of regular expression is: “A regular expression is a sequence of characters that specifies a match pattern in text.” Regular Expressions (RegEx as it’s often called) are a royal pain to write, so much so that programming languages and spreadsheet function scripting have developed a bunch of functions for common RegEx uses so that you don’t have to write them.

But what I was looking to do wasn’t common, so I had to write a RegEx. I spent about a half-hour trying to remember how to do it, and then I realized that I could probably plug what I wanted to do into one of the GenAI sites and get it to work. Interestingly, I couldn’t make the code Gemini generated work, but I was able to get the code Claude created to work.

But here’s the thing — I didn’t learn how to write Regular Expressions by doing what I did. I figured out how to make Regular Expressions work for me, but that’s not the same thing. Fiddling around to make the code work gave me a better general working knowledge of RegEx, but if you asked me make some code using RegEx work without GenAI, I couldn’t do it. Now, for me as a working principal trying to just make a tool that works for my school, that’s all I needed. I wasn’t trying to learn, I was trying to build. And in that case, GenAI was a good tool for me to use… as long as I wasn’t fooling myself that I was learning RegEx. So if my goal was to learn how to build smart data visualization tools, mission accomplished. If this was a CSE class where I was supposed to be learning how to create regular expressions, I didn’t accomplish that goal.

This presents educators with a pretty big problem when it comes to thinking about when, how or why to incorporate Generative AI into the classroom. In the world of progressive education, the concept of “Learning by Doing” is pretty core to how we think about how kids learn. Fundamentally, it’s the idea that process matters as much as product. And when we assess product, we are using that product – in some part – as an manifestation of the learning process.

We can’t assume that anymore. And that creates a problem.

It’s a conundrum, because I think we want to expose kids to GenAI in school. It seems to fall into that category of “It’s a tool they’re going to use, and we probably want to make sure they have a working understanding of when and how to use it.” (And if you noticed the overwhelming use of hedge words in the beginning of this paragraph, they’re on purpose. The only think I am certain of about AI use in schools is that I am not certain of much.)

This is going to require all of us who love project-based learning and student writing and such to think deeply about when process matters and what that process has to look like. There will be clear uses of AI — I think every student who isn’t interested in being an artist but had to illustrate a story will find use for image generation AI. If a science teacher is more concerned with the analysis of data than the actual writing in the lab report, a thoughtful student might use GenAI to craft the writing. I know there are AI tools in video and audio creation that make it easer to make professional

And then there’s the use cases when GenAI can do interesting analysis that require students to ask better questions… Here’s an example. I asked Claude.ai the following prompt:

Do an analysis of the winners of the national league batting title for the last twenty years and draw conclusions about whether or not hitting has gotten harder.

I did that knowing that my analysis was pretty thin, but I was curious what Claude would say. Sure enough, Claude knew that the conclusions it drew weren’t that strong:

However, it’s important to note that this analysis is limited to the top performer each year. A more comprehensive study including league-wide averages and other offensive metrics would provide a more definitive answer about the overall difficulty of hitting in the National League over this period.

Would you like me to elaborate on any specific aspect of this analysis or explore any additional factors?

So sure… I asked it to include league-wide averages, and I got a better level of analysis… and because the analysis comes so quickly, I thought of more data to include. And I know from watching baseball that hitters aren’t worried about striking out like they used to be, so I thought that might be interesting to analyze. And there’s a stat OPS (on-base percentage plus slugging percentage) that would be interesting to look at too.

What I ended up with was some really interesting analysis that, if I wasn’t using AI, would have taken me hours and hours to do — more time than I think we’d want students to do. (Go check out the analysis. I would have pasted it here in its entirety, but it’d be longer than the blog entry.) Now, I think the chart is actually the most interesting part of the analysis, with the bullet points summarizing the chart also really important. I actually think that the conclusions Claude draws are the worst part of the artifact. As a baseball fan, there are things I think Claude misses, the addition of the designated hitter in 2022 and the banning of the defensive shift in 2023, for example. But the data collection is amazing, and the idea that we can give kids the ability to ask questions and gather data at speed does seem to matter. And it’s not hard to think of an assignment that asks kids to do data collection and draw their own conclusions.

So yes, I think we can create some really thoughtful ways students will use AI in schools, and it’s important we show students ways we can GenAI in ways to create and do and learn in ways we could not before, and yes, we probably should consider the promise this brings as much as we think about the concern we very much need to have about how GenAI can allow students to create product without going through a meaningful learning process.

But I’m not sure of any of this. I’m really not. I think about my own use of GenAI, and almost none of my uses of it could be considered learning. Mostly, I use it when I want a shortcut (writing a letter for a teacher who needs a jury duty postponement during state testing) or I want to do something I currently can’t do (the RegEx use case), but I don’t end up learning much from my use of it. And it’s not a leap to imagine that if I use it in those ways, so will students.

And that worries me a lot when I think about school. Because I think we have to think a lot about what it means to truly learn something. For a long time, doing a thing meant you learned a lot about the thing, so we could look at the product and understand that there was a learning process at play in most cases. That’s harder now.

For now, I think we’re really going to have to be even more intentional than we have been about considering the question of what it means to really learn something, and how we, as educators create the conditions for kids to learn by doing and how we document both product and process so everyone – students, teachers and other stakeholders – can reasonably understand what kids have really learned.

And I worry that this is going to get harder.

Iteration

I was talking with one of our veteran teachers this week… we were talking about our pre-school PD. I’m always a little nervous about how we plan for professional development at SLA. We have a veteran staff — the average SLA teacher has been at the school for nearly ten years at this point — and figuring out how to help masterful, veteran teachers keep getting better at their craft is tough. I want teachers to feel like we’re asking them to do work that matters when they come to staff meetings, and that we can get better and grow together as a staff.

In the conversation we talked about the humility necessary to keep working on our craft and how important it is to ground yourself, year over year, in the core tenets and pedagogy of the school. That’s not to say that we don’t incorporate new ideas, new learnings, new thoughts, but at our root, we recognize that iteration matters… we get better at the things we continuously work on.

So every year, we ground ourselves back into the SLA model of inquiry-driven, project-based teaching, taking apart what we do brick by brick for the purpose of getting better at it. We work to ask ourselves hard questions about how we plan, how we teach, so that we can get past surface-level understanding of the model into the places where it gets hard and into the places where we can learn. It’s exciting to watch folks who have been at SLA for ten, fifteen, nineteen years alongside teachers who have been with us for three or four years unpacking practice and sharing ideas across disciplines toward a common language and model of teaching.

And I think that iteration – the understanding that there is a theory of action and pedagogy that powers what we do – is a big reason why the average SLA educator has been at the school for almost ten years at this point. It’s a little bit wild to reflect on the idea that the average SLA educator has been at SLA for over half of the school’s existence. It’s given us the ability to get better together, and to, over time, build tools and systems and processes that (hopefully) make it easier for the next generation of educators to join us and teach and learn with us.

And because planning for iteration isn’t always easy, here’s the framework for that first workshop we did together this year. The planning was done as a collaboration between our AP, our Academic Standards Committee chair (teacher), and me. We write in shorthand a lot because of our shared language, so I’ve tried to add some more language for folks who may not know the SLA model. This was about an hour long conversation, and it framed the four days we had together before school started.

Kick-off:

Frame the necessity of going through this process – this work of building our practice around our inquiry-driven, project-based beliefs, and continuing to get better at them together. Why is it so important that we continue to grow together as a faculty? How do we talk honestly about the difference between when we are engaging in the deep work of the model vs. the surface-level work?

Brainstorming:

Begin with groups brainstorming an assigned core value (Inquiry, Research, Collaboration, Presentation, Reflection): What is it? Why does it matter? How is it seen in my classrooms? When have I experienced success with this value, and when have I struggled with it? What does it look like to do this core value well, and what does it look like to “phone it in?” (surface-level engagement)? Who else on staff you observed engage well with this core value?

First write personal reflections in Padlet, and then discuss in your groups.

Share out:

for each value, have someone in the group summarize the conversation. During share out – open to the room at the end for others to share what they heard from the group.

Deeper Reading – Give time for everyone to read the entire Padlet.

Individual Reflection + Practice: reflect on best practices of core values shared out, and challenge yourself to implement one change/tweak to some aspect of one of your courses.

To wit: How can I emphasize one of these practices within a particular value within my planning and practice?

David Warlick: A Thank You

For folks who do not know, David has been battling cancer for the past seven years, and about three weeks ago, he posted an update that makes it sound like life’s getting a bit harder of late. It strikes me that all of us who have been influenced by him have a moment to make sure he knows the profound impact he’s had on us. I’ll start.

David was one of the earliest ed-tech evangelists I knew in this space. I think we first met back in the late 1990s. He was writing and blogging and creating web-tools for teachers and students to use at a time when many schools were still just getting online. At a time when few were “in the space,” David was pushing all of us to think critically about what we were doing. His early books – Raw Materials for the Mind and Redefining Literacy for the 21st Century – were important to me in the ways I thought about how I wanted to use many of the new tools around me.

And if that was all David had done with his career, then his contribution to our world and our work would have been profound enough, but David is so much more than that. David is one of the most kind and generous folks I’ve known in this space. He has always been one of the best listeners and best conversationalists I’ve known, and whenever he and I got the chance to hang out at ISTE or when he would come to EduCon, time spent with David always just made me feel better about the world.

There’s a story in the early formulation of SLA that most folks probably don’t know. In Spring 2006, before the school had opened, we had a huge curriculum summit, where I asked every friend in education I had to come help me work with the folks from the School District who were still a little nervous about letting this 35 year old who had never been a principal before create this new school with new ideas. David was one of the folks I invited. Now… to this day…I *swear* that, when I asked him to attend, I DID ask him to kick off the summit with a short keynote. But… the night before, when he and I were out to dinner, that news seemed, well, a bit of a surprise to him. But it didn’t matter to David — or at least if it did, he never made me feel like it did. We (quickly) finished up our food, and he went back to his hotel room to prepare and get a good night sleep. And the next morning, he kicked off the summit and his words went a long way in carving out the space the school was going to need to thrive in those early days.

It’s, to me, the quintessential David story — he is so generous with his time, so thoughtful with his words, and so brilliant with his ideas. Without his keynote that day, I’m not sure the district would have carved out the exemptions we needed to build the school we did. There’s never been a moment when I haven’t been powerfully aware of the gift he gave our school that day — and how that gift helped us to create the SLA we have to this day.

So David… as you continue your fight with cancer, let me just say – on behalf of so many of us whose work you have influenced and whose friendship has been so meaningful – thank you. Thank you for your intelligence, your vision, your friendship and your deep and overwhelming kindness.

Practical Theory

Chris Lehmann

Learning, Doing and Generative AI

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Iteration

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David Warlick: A Thank You

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Practical Theory

Chris Lehmann

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