Teachers are told to review AI output before using it. But what does review actually mean? Most teachers check three things. Is the material accurate? Is it aligned to the standard? Is the tone appropriate? If all three pass, the material goes into the lesson. That is a read-through, not an audit. A read-through catchesContinueContinue reading “Your AI Lesson Plan Passed the Read-Through. It Still Might Fail These Two Tests.”
Category Archives: AI in Education
The Offloading Trap: Why You Must Audit Every AI-Generated Instructional Output
Claude for Teachers launched this month. Teachers can pull in assessment data, lesson plans, and standards from all 50 states, then ask an AI to build personalized instruction overnight. That’s powerful. It’s also the exact moment to be honest about what happens between the prompt and the classroom. I’ve written about this distinction from theContinueContinue reading “The Offloading Trap: Why You Must Audit Every AI-Generated Instructional Output”
Cognitive Offloading vs. Cognitive Outsourcing: The Distinction That Determines Whether AI Helps or Harms Learning
A calculator offloads arithmetic so a student can focus on problem-solving. That’s cognitive offloading: the tool handles the mechanical work so the learner can focus on the cognitive work. Now imagine a student who types a word problem into an AI tool, copies the answer, and moves on. The tool did the thinking. The studentContinueContinue reading “Cognitive Offloading vs. Cognitive Outsourcing: The Distinction That Determines Whether AI Helps or Harms Learning”
Beyond the Hype: An Evidence-First Framework for AI Literacy in Education
An evidence-first framework for how educators should think about AI in the classroom — anchored in cognitive science, grounded in real research, and committed to pedagogy preceding tooling.
I Built a Personalized Study System with Claude. Here Is the Science Behind Why It Worked.
I built a personalized study system using Claude and cognitive science principles: retrieval practice, spaced repetition, cognitive load. Here’s what happened and why it worked.
Creating Examples vs. Non Examples Using Nano Banana Pro on Gemini 3 to Amplify Instruction
Learning abstract concepts is difficult because our students are beginners in many cases. This means they primarily understand new ideas in the context of what they already know, which is usually concrete. To build a robust “schema” (a mental structure of organized knowledge), students need more than a definition; they need to see the concept in action through examples vs. non examples. With this said, it is now ever been easier to create examples vs. non examples using Gemini’s Nano Banana Pro image generator that is associated with Gemini 3. In this post, you’ll see how to do this to support your instruction.
From Digital Consumers to Digital Pedagogues: A Framework for Moving Teachers from Digital Consumers to Tech-Enabled Pedagogues
In my work with teachers, I’ve seen firsthand a significant challenge that has only accelerated in our tech-saturated world: the gap between how pre-service teachers use technology and their capacity to teach with it. This is the Consumer-to-Pedagogue Gap, and it is one of the most critical hurdles we must overcome in modern teacher preparation and on-going professional development and coaching as they progress forward in their careers.
This post outlines a framework to bridge this gap, moving teacher candidates from passive digital consumers to active, tech-enabled pedagogues. The central thesis is straightforward: we must replace passive observation with a system of structured, low-stakes rehearsal. This over time will improve instruction as well as the use of technology when integrated together.
This system is not built on intuition; it is grounded in established learning science, specifically the Technological Pedagogical Content Knowledge (TPACK) framework and Cognitive Load Theory (CLT). These are the foundational lenses I advocate for in all effective instructional design.
Using AI to Support Interleaving & Spaced Practice and Retrieval in Unit Planning
📬 Stay in the loop Join educators getting weekly insights on AI, co-teaching, and instructional leadership. Subscribe As teachers, we are constantly seeking ways to make learning more durable and meaningful for our students. We want them to not just memorize facts for a test, but to fully understand and retain what they’ve learned overContinueContinue reading “Using AI to Support Interleaving & Spaced Practice and Retrieval in Unit Planning”
Creating AI Agent Safeguards (for now) through Pragmatic Instructional Design to Deliver Assignments and Assessments
As you prepare your syllabus for the upcoming semester, a question I’ve explored before looms large. In a previous post, “The Death of the LMS in Higher Ed,” I argued that its role as a simple repository for assignments is becoming obsolete. With AI agents now ubiquitous, how do we prevent the LMS from becoming a mere drop-box for bot-generated work from an autonomous AI Agent?
The solution isn’t to fully abandon the LMS (for now), but to evolve its purpose and create humanizing instructional barriers in the tasks and assessments we deliver to our students. This post presents a few practical strategies (I am implementing this term in my own graduate-level course) to revitalize our courses by shifting the focus from the final product, which AI can generate in seconds, to the learning process, which it cannot. This is going to my attempt to defend against students utilizing AI Agents. While I know this may not be one-hundred percent effective, I do have a hunch it will mitigate AI Agents to a degree, at least for the time being, this semester.
Using AI to Build Powerful Retrieval Practice Activities to Supercharge Student Learning Opportunities
The “testing effect,” a term championed by researchers like Roediger and Karpicke, demonstrates that every time a student actively recalls information, they strengthen the neural pathways associated with it. This effortful retrieval tells the brain that the information is important and makes it easier to access in the future. Ultimately, over time, if students are able to actively recall correctly, this means they likelihood of them learning the content, is much higher. Although the challenge for teachers has always been the time it takes to create a rich variety of material, such as flashcards, practice tests, and quizzes, that facilitate this process.
With powerful AI tools, we can now create engaging, interactive retrieval practice activities in minutes. Tools like Gemini, Claude, and ChatGPT have built-in “Canvas” features that can generate the code for these resources, and you don’t need to be a coding expert to use them.