CYCLES OF LEARNING
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When curiosity is sparked...

deep cycles of learning can occur.

The App IS the Lesson

4/19/2026

 

I 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 Need

Using 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.

The shift: I was not looking for a tool. I was not shopping for a simulation. I was building exactly what my students needed, and the act of building it forced me to think more carefully about the learning experience than any lesson plan ever would.

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 Breath

There 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.

The real question: How do we build classrooms where rigor and creativity are not competing values but inseparable ones? Where the standard and the project are the same thing?

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.

Resources

  • Action Potential Interactive App
  • Demyelination Interactive App

Build the Tool, Don't Find the Tool

4/9/2026

 

A 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 Instinct

We'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?

Building apps to solve problems rather than looking for tools to solve problems is very metal. Solving your own problems through invention just might be the next AI-powered critical thinking.

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 Builders

Here'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 Laboratory

This 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 Question

Here'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

  • Spark Learning Inquiry Studio — Inquiry-based instructional design
  • Jacob's Arduino Assistive Tech App — Student-built problem solving

Cycles of Learning — Ramsey Musallam
cyclesoflearning.com

AI and Media Literacy Lessons

11/4/2025

 
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 Curiosity with Google Vids: Using Masks to Create Mystery

10/1/2025

 
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. 

Pedagogical Deep Dive: Teaching the Neurochemical Link Between Epilepsy and Alcohol Withdrawal

9/30/2025

 
Throughout the semester, our units on addiction often focus on systems like the opioid pathway, which is common in medical biochemistry. This year in Neuroscience, I restructured our unit to specifically create an "Aha!" moment by contrasting different types of withdrawal crises. We aimed to answer a high-stakes question: Why is alcohol withdrawal uniquely and acutely life-threatening via seizures, unlike the severe but typically non-seizing nature of opioid withdrawal? The goal of this unit was to create a connection between the fundamental neurobiology of epilepsy and the brain's forced adaptation to chronic alcohol use, thereby illustrating the diverse chemical dangers of addiction. The learning cycle focused on three key steps to generate this understanding, using a combination of foundational videos and interactive tools.

We began by establishing a clear understanding of what causes any seizure, setting the Seizure Baseline. We used a simple analogy: the brain operates a seesaw of electrical activity, balanced by two primary forces—Inhibition, handled by (the brain’s main brake), and Excitation, handled by Glutamate (the brain’s main accelerator). A seizure occurs when this seesaw tips violently toward excitation, usually due to too much Glutamate activity (NMDA/AMPA receptors) or too little GABA activity. We reinforced this concept using this video and discussing. he clinical approach: anti-seizure medications work by either enhancing GABA activity or reducing Glutamate signaling. We further discussed the role of voltage-gated Sodium Channels in action potentials and how their dysregulation contributes to hyperexcitability, detailed in this clip . 

With the rules of seizures established, the lesson shifted to Investigating Addiction and Alcohol's Unique Chemical Signature. We utilized the fantastic Mouse Party simulation  where students analyzed different substances to compare and contrast their neurotransmitter impacts. We focused specifically on alcohol, noting that its acute effect (when drinking) is that of a powerful depressant, working by increasing GABA activity and decreasing Glutamate activity, heavily favoring the inhibitory brake. Students were guided through this analysis using the Class Workbook/Guiding Document.

The final stage delivered the "Aha!" Moment: The Neurobiological Rebound. We challenged students to synthesize their knowledge: if the brain is constantly fighting a depressant that enhances GABA and suppresses Glutamate to maintain homeostasis, what happens when that depressant is suddenly removed? The brain's dangerous physical and functional adaptations become clear: 1. GABA System Downregulation, where the brain literally removes GABA receptors, leaving the inhibitory system structurally weak. 2. Glutamate System Upregulation, where the brain increases Glutamate receptors, leaving the excitatory system hyper-primed.

When alcohol suddenly leaves, the body's over-compensation
blows up: the weak GABA brakes can't stop the system, and the hyperactive Glutamate accelerator is pressed to the floor. This rapid, massive shift creates an extreme state of neuronal hyperexcitability—the perfect neurochemical storm that causes the generalized tonic-clonic seizure. This powerful rebound effect is the primary reason alcohol withdrawal is distinct from that of drugs like opioids, which do not rely on the GABA/Glutamate seesaw to the same, deadly extent.

To underscore the severity and clinical necessity of this neurochemical phenomenon, we concluded by examining a clinical scene depicting the results of severe alcohol withdrawal (Viewer discretion advised), using this Leaving Las Vegas Seizure Scene  This lesson demonstrated that addiction is not a simple failure of willpower, but a deep, adaptive response where the brain structurally alters itself to survive a toxic, chronic chemical environment, often with deadly consequences when the drug is removed. 

​The overall goal, successfully established for the students, was to tie back to their foundational learning from the beginning of the semester regarding epilepsy and seizures. They ultimately realized that withdrawal from chronic alcohol use essentially mimics that very same pathological state. By tracing the
and Glutamate dysregulation, the lesson successfully merged the clinical realities of acute alcohol withdrawal with the chronic condition of epilepsy, providing a robust, chemically-based understanding of the danger. While the subject matter was undeniably dark, I found this lesson to be profoundly necessary and incredibly impactful in demonstrating the immense power of neurochemical adaptation.
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    Ramsey Musallam is a full-time science teacher in Santa Rosa California.
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