As part of the ongoing pushback against educational technology in general, several states are considering new legislation to set limits on the number of hours that students may spend using devices. Kentucky is one such state in which legislators are looking to cap screen time based on child psychology research and the age range of the students. While the proposed law would differentiate between student use and faculty use, it would also allow exemptions for some learning management systems. I am optimistic that educators and legislators will work together to make these new laws nuanced enough to protect instructional time.

Houston ISD is launching more of its Future 2 schools next year. This new model is focused on AI-literacy and design thinking. Their goal is to answer the question, “If AI is everywhere, what kind of school should we build?” This is a really interesting concept, that is being expanded from two pilot schools this past year to eventually reach 100 schools by 2031. Very few districts are thinking this deeply about AI’s effect on education, and are willing to explore truly innovative ways of teaching. I will be following this initiative closely, partly because my own district in North Carolina is often compared to Houston’s for its size and diversity.

A recent survey of European students aged 18-25 (and supplemented by separate UK higher-ed survey data) has reached the conclusion that students are using genAI tools for much more than outsourcing their work. In fact, the majority of these students reported using the tools to help them take notes, summarize, and brainstorm. While intriguing, it’s important to note that these young adults are self-reporting their use and even if the data is accurate they are at an educational stage when many are motivated to learn beyond the levels we find in the K-12 space. My own anonymous student survey data has shown that about half of my students say that they use AI in ways that support their learning rather than replacing their thinking. The solution seems to be intentional instruction that forces students to see the benefits of using AI appropriately.

My school year ended a few days ago and my primary summer project is finishing a draft of my next book “The Learning Forge”. While “50 AI Prompts for Teachers” was focused on helping teachers use AI tools to enhance their teaching, this new book is designed to help teachers with our biggest challenge right now: teaching students to use AI to help them learn.

As any classroom teacher will tell you, it’s too late to stop secondary students from interacting with genAI. It’s embedded in the tools they use every day and they’ve already learned that it can complete assignments for them. What we need is a framework that helps them see the harm that some uses of AI can cause to their learning, and the benefit that can come from other uses. As part of that work in my own classroom, I employ a student-facing tool that I call the CAMPE Meter.

I have found that many students crave a clearer way to know when they have over-relied on AI for help. They want to stay on the right side of cheating policies, but they also realize that building a stronger “brain muscle” requires exercise. No gym bro is going to sit down at a machine that uses motors to lift weights for them. No serious gamer is going to take a shortcut to the end of a video game. But how do we teach our students the metacognitive skill of evaluating how much AI is too much?

That’s where CAMPE comes in. It’s an acronym for a list of characteristics that differentiate personal work that communicates mastery in a student’s own voice from AI slop:

Best of all, students learn to self-evaluate using a common template. So the metacognition is preserved and the “Am I doing this right?” question eventually gets internalized. You can access a free copy of the CAMPE Meter Checklist here.

The best way to introduce this concept to students is to show them examples of student-written work alongside AI-generated text, and ask them to find differences. Get them to focus on what’s “better” about each one. They will likely pick out the stronger vocabulary and details of the AI-written piece, but they might need help seeing the good qualities of the student-written one, like personal voice and inside jokes/references. My class discusses when something unique and personal might be preferable to something cold and machine-generated.

Then, we use the CAMPE Meter to evaluate several pieces of writing. We keep the focus on not just the assessment, but the changes that could improve the CAMPE score. This isn’t an opportunity to slam AI-written work, but rather an exercise in how we balance AI support with human writing and thinking.

Want to see more? Check out the Learning Forge One-Pager for more tips from the upcoming book.

Summer is here but I am just getting started. In between writing stints, I am preparing for presentations at the following events:

If you’ll be at any of these, I’d love to say hi and find out how your using AI as an educator!

In July, I’ll share some of the presentation materials that I’m using.

Paul (and the Codium Educational Consulting team)

P.S.

Don’t forget to email me with examples of how you’ve used the tools and strategies that I’ve shared.