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Something has been quietly shifting in the way I plan units this spring. In Neuroscience, it is action potentials. In Chemistry, it is single replacement. One concept sits at the center, and everything else is gravity around it. Pathology spins off the anchor. Case studies orbit it. Even the off-the-wall connections (an opioid called Journavx, a periodic paralysis case, a Play-Doh battery that lights a red LED but not a green one) end up tracing back to the same epicenter. I think I am starting to understand what critical thinking actually looks like in a classroom. It might be this. The ultimate concept map. One idea, deeply, with everything radiating outward. The Epicenter ConceptIn Neuroscience this week, the action potential opened up so much room. Opioid addiction, dopamine, the new pain drug Journavx, periodic paralysis as a potassium channel disruption, multiple sclerosis as a story of myelin and demyelination. Each new topic was not a new unit. It was a new orbit around the same gravitational core. The students saw it too. Once they understood the action potential well enough, they could predict what would go wrong in every disease we encountered. Sodium channel mutation? They knew the firing pattern would change. Demyelination? They knew the signal would slow or fail. The anchor was doing the work. I was just pointing at the orbit. Synaptic connections is the other anchor. Once you have it, addiction makes sense. Reward circuits make sense. So much of human behavior makes sense. Two anchors. A whole semester.
Inquiry Before the ExploreI have been pushing the engage phase harder this year. Not as a hook, but as a genuine hypothesis-forming moment that runs underneath the whole exploration. The students should already be reasoning before they touch the lab materials. In Chemistry, we did this with single replacement reactions. I put the reaction of copper and silver nitrate on a time-lapse loop and asked students to watch and guess what was happening. They debated the colors. They argued about what was forming. Then I challenged them to set up the same reaction themselves, also on time-lapse. The engage moment was already an exploration. In Neuroscience, we used a multiple sclerosis case study to launch into brain anatomy. Students started with a patient profile (blurry vision, balance issues, right-side weakness) and used the symptoms to hypothesize which parts of the brain might be affected. From there, they did a sheep brain dissection, located the regions they had predicted, and pulled small biopsies into labeled tubes. The dissection was not a separate event. It was the test of their hypothesis. The Play-Doh battery moment was unplanned but landed beautifully. Students built a battery from Play-Doh, zinc, and copper chips, and tried to light an LED. The red one lit. The green one would not. They started hypothesizing on their own, connecting it back to the flame test activity we did at the beginning of the year. Lower energy required for red excitation versus green. I had not planned the through-line. The anchor concept did it for me. Build the Tool, Not the WorkaroundTwo more apps came out of this week, both because I needed something specific and the existing tool did not fit. I needed a better classroom timer. Not a generic one, just something simple, big, and silent. So I built one. Class Timer took about an hour. It does exactly what I need. I also wanted students using podcasts as a homework vehicle instead of a textbook chapter. I needed an app that would help them find topic-relevant podcast episodes quickly. So I built Classroom Podcast Finder. Now homework is a curated, searchable, audio experience instead of a static page. And inside Spark Learning's Inquiry Studio, I figured out how to generate a QR code for the lecture video and place it directly on the student handbook. That changed everything for the flipped classroom. Students watch the video during class, on their own devices, while I stay in the room. They check in with me when they finish. The video becomes personal again. The structure stays. I do not have to chase anyone down.
Two old blog posts felt newly relevant this week as I worked through this. The Explore-Flip-Apply theoretical framework is the structure. A pedagogy-first approach to the flipped classroom is the why. Worth a re-read if it has been a while. A Question for the Chemistry CrowdOne thing I keep turning over: are we teaching mole conversions and dimensional analysis the way we do because it is genuinely the best path, or because it is the aesthetic we inherited? I am not arguing for tossing it. But I am wondering if we are sometimes loyal to old methods at the expense of student access. Worth thinking about. I do not have an answer yet. I will leave you with this. The anchor concept does not just structure the unit. It structures the thinking. And maybe that is what we have been chasing all along. ResourcesI stayed up late last week trying to record a lecture video on action potentials for my Neuroscience class. I kept redrawing the neuron, restarting the recording, trying to get the sequencing right. After about an hour of frustration, I stopped and asked myself a different question: why am I making a video at all? What I actually wanted was for students to explore the process of an action potential unfolding, to interact with it, to discover the pattern before I named it. A video is passive. It shows. It tells. What I needed was something that let students do. So I built an app instead. Build What You NeedUsing Claude Code, I built a small interactive simulation that scaffolds the action potential process for students. It is tailored specifically to my class, my sequence, my learning goals. No extra features, no unnecessary information, no generic PhET simulation that shows too much too soon. Just the right amount of discovery at the right moment. Here is the thing that surprised me: the app took less time to build than the video would have taken to record and edit. And it does something a video never could. It lets students interact, explore, and arrive at understanding on their own terms.
I shared both apps with my colleagues this week. The Action Potential App and the Demyelination App are live. They are simple, purposeful, and built for inquiry. If you want to see what "build the tool" looks like in practice, start there. Is Software Dead?This experience pushed me into a bigger question that I have been turning over for weeks: is traditional educational software dead? Think about it. We used to search for the right app, the right simulation, the right platform. We evaluated SaaS products, compared features, sat through demos. And most of the time, the tool was close but not quite right. Too broad, too narrow, too cluttered, too rigid. Now students can build their own. A student struggling with organic chemistry can use Claude to generate a tailored study app. A student mapping historical events can create a custom timeline tool. A student learning Arduino can build a simulation specific to their project. The combination of Claude and NotebookLM has quietly made one-size-fits-all software feel like a relic. The question I keep coming back to: should we be teaching students how to dynamically create tools in response to their own learning needs? Not coding for coding's sake, but building as a form of thinking. The app is not the product. The app is the process. Rigor and Creativity in the Same BreathThere is a tension I feel every week between wanting rigorous, measurable learning and wanting students to create freely. Grading makes it harder. Standards make it harder. The instinct to control the outcome makes it harder. But here is what I am learning: when students build something, the rigor is embedded in the building. You cannot build an action potential simulation without understanding action potentials. You cannot create a study tool without deeply engaging with the content. The creative act and the rigorous thinking are not in tension. They are the same thing. The challenge is designing assessment structures that honor this. Standard grade formats were not built for student-created software. They were built for essays and exams. I do not have a clean answer yet, but I think the answer lives somewhere in the process documentation, in the iteration, in the visible thinking that building requires.
I am still working on this. But the action potential app taught me something I did not expect: sometimes the best lesson plan is not a plan at all. It is a tool that did not exist until you needed it. ResourcesA student in my neuroscience class blinked. A lot. That's not remarkable in itself — we all blink. What was remarkable was the question that followed: How many times do we blink in a minute? And does it change when we're focused? In the past, I would have Googled "blink counter app," scrolled through the App Store, found something close, settled for something mediocre, and moved on. Instead, I built one. Right there. During class. An app that counts blinks, times them, and gives students real data to analyze. This is the shift I can't stop thinking about. The Problem-Solver's InstinctWe've spent years training ourselves — and our students — to be tool finders. Need a timer? Find one. Need a quiz platform? Compare five. Need a simulation? Hope someone built one. The entire edtech ecosystem is built on the assumption that teachers are consumers of tools built by someone else. But what happens when you can build the tool yourself? When the distance between "I have a problem" and "I have a solution" collapses to minutes instead of months?
My own app, Spark Learning Inquiry Studio, is an example of this. I had a problem: inquiry-based lesson design is powerful but hard to structure, hard to present, hard to share. No tool existed that thought the way I think about the 5E learning cycle. So I built one. And it continues to evolve because it's mine — it solves my problems. Students as BuildersHere's where it gets exciting: students can do this too. Not hypothetically. Right now. Jacob, a student in my Design for Social Good class, needed a way to test assistive technology connections for the Xbox Adaptive Controller. There's no app for that. So he built one: an Arduino-based testing interface that solves a real problem for real users. He didn't find a tool. He became the toolmaker. This is what I mean by "app slop" used for good — quick, purpose-built applications that solve specific problems. They don't need to be polished. They don't need to scale. They need to work. The Classroom as LaboratoryThis week I used a Slinky to get my neuroscience students thinking about perception and time delay. A simple warm-up in Spark Learning — watch the Slinky drop, notice the delay between what you see and what you feel, and wrestle with why. In chemistry, we used Jenga blocks to model the molecular instability of nitrogen triiodide. The tower wobbles. You hold your breath. Then it collapses — just like the compound. These aren't tech moments. They're inquiry moments. The Slinky and the Jenga tower are tools I built into a learning cycle using the same app I built to solve my own problem. The technology isn't the point. The thinking is the point. The technology just makes the thinking visible, shareable, and structured. The QuestionHere's what I keep coming back to: What's a problem you have? Not a tool you need — a problem. And what would happen if you — or your students — just... built the solution? I think we're entering an era where the ability to identify a problem and invent a solution is more valuable than the ability to find and evaluate existing tools. That's a different kind of critical thinking. And AI makes it accessible to everyone — not just developers. Something to sit with this weekend. — Ramsey Resources
Cycles of Learning — Ramsey Musallam When most people hear I’m teaching a new class called AI and Media Literacy, they assume it’s a sharp turn from chemistry. In some ways, they’re right. My TED Talk, 3 Rules to Spark Learning focused on curiosity and inquiry in science classrooms, and chemistry has long been my home base. But this new course grows from that same philosophy: it’s about giving students safe, hands-on ways to play, question, and create with the unknown. In this case, the “unknown” is artificial intelligence.
The class centers on two big ideas: Predictive AI and Generative AI. Rather than treating AI as a mysterious black box, students learn to work inside it—to build, test, and critique it. I want them to experience the useful side of AI right alongside the problematic side, building the kind of fluency that can only come from making things. Project 1: Predictive AI: Our first project explores Predictive AI, which powers tools that classify, sort, and detect patterns based on trained data. Students use Teachable Machine to build simple but meaningful predictive models—image, sound, or pose classifiers—that serve a purpose in their community. Students start by comparing how different AI models (ChatGPT, Gemini, Claude) define predictive versus generative AI, then move into hands-on modeling. From there, they train AI to do something useful: maybe detect hand signals for accessibility, identify safe vs. unsafe environmental conditions, or recognize actions that can help others. The final challenge is to turn that model into a functioning web app, using a mix of Claude, ChatGPT, and Netlify. The results are surprisingly creative. You can browse their finished apps here on Padlet. If you’re curious about the full structure and rubric, you can view the Predictive AI Project. Project 2: Generative AI: The second project flips the perspective. Instead of using AI to predict something about the world, students use Generative AI tools—Gemini, NotebookLM, Teachable Machine, and Google AI Studio—to analyze the world. Specifically, they build a web app that determines whether an image was created or altered by AI. The process starts with research. Students prompt Gemini to curate ten recent YouTube videos explaining how to identify AI-generated imagery, then feed those into NotebookLM to digest and summarize the key ideas as a mind map and audio overview. From there, they design a rubric in NotebookLM listing 10–12 signs of AI manipulation. They train a Teachable Machine model using real and AI-generated examples, then combine that model with their rubric inside Google AI Studio to produce a working app that analyzes uploaded images and explains why it thinks an image is or isn’t AI-made. The full project guide is available here: Generative AI Project. The real goal of the course isn’t to turn students into coders. It’s to make them critical and confident participants in the age of AI. They learn how predictive systems make judgments, how generative systems can deceive or inform, and how both can be used for creativity and good. For teachers interested in exploring AI in the classroom, I am hopeful that these projects strike a balance between creation and critique. They show students that AI isn’t magic—it’s math, data, and design choices made by humans. And like any good chemistry experiment, the best learning happens when they roll up their sleeves and see what reacts. Sparking involuntary curiosity in students often comes down to creating an awareness of an information gap, a missing piece of knowledge that students naturally want to fill. Loewenstein (1994) described this as the key driver of curiosity, and you can read more in my earlier posts here: Cultivating Involuntary Curiosity and Involuntary Curiosity Sparks: Loewenstein ’94. One of the easiest ways to bring this into the classroom is by showing a video of a phenomenon and covering up a key detail, prompting students to puzzle over what’s missing.
Until recently this kind of editing took time, but with the addition of Google Vids to the Google Workspace in 2025, it can now be done quickly and effectively. The process is simple: locate a clip that fits your lesson, identify the moment you want students to wonder about, import the clip into Google Vids, and use the masking tool to cover the important piece. Playing the masked video creates a sense of mystery, and students are compelled to make predictions before the reveal. Even a basic block covering part of the screen adds to the effect, often making students more eager to uncover what’s hidden. I recently used this approach to set up a lab on polarity, intermolecular forces, and chromatography. Students first watched the masked clip and generated hypotheses, then carried out the lab to see the explanation unfold in their own hands. This ties into the Inquiry Hero’s Journey approach, transforming the video from a passive clip into an active thinking prompt. Subtle as it is, this tweak turns mystery into momentum, fueling curiosity and engagement in ways that drive deeper learning. Watch the screencast of the entire process here. |
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