When curiosity is sparked...

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. 

Every year I begin chemistry with the flame test lab. It is a simple and colorful way for students to wonder about electrons. They see different salts burn with different colors, and they start asking questions about why that happens. Those questions lead into the Bohr model, ground and excited states, and eventually orbital diagrams. In my own framing of the inquiry cycle, this lab is the “call to adventure.” Students engage with something mysterious and exciting. The next steps of exploration and explanation follow naturally as they try to connect color to electron energy levels.

Where I have often struggled is the extend phase and first year chemistry students can find quantitative line spectra, a classic extension, overwhelming at this point in the course. What I noticed, however, is that many of them become curious about visible light itself. They want to understand wavelength, color, and how light relates to the fall of electrons. This year I leaned into that curiosity by exploring lasers as an extension of the flame test. At the atomic level, lasers and flame tests share the same foundation: electrons are excited, fall back, and emit photons of very specific energy. The difference is that lasers add amplification through mirrors, which produces an intense beam of a single wavelength.

To make this connection hands-on, we used inexpensive red, green, and purple laser pointers along with a simple diffraction grating made from the back of an old CD. Shining each laser on the CD interferes with the light waves in a specific way that creates bright spots at intervals related to the wavelength of the light.  The spacing of those spots is related to the wavelength of the light. Students measured the distance between the bright spots and the central beam for each laser, then compared the ratios. By setting up proportions, they could use the known wavelength of one laser, such as red at about 650 nm, to estimate the wavelength of green or purple. This was surprisingly accurate and gave them a sense of how scientists measure what we cannot directly see. It became a natural extension of the flame test: the unseen behavior of electrons revealed through the visible evidence of light.

Steps for the Laser Diffraction Lab

1. Gather materials: red, green, and purple laser pointers, a CD or DVD (shiny side out), a meter stick or ruler, a piece of graph paper taped to the wall.
2. Set up the diffraction grating: tape the CD upright so that the laser beam shines directly on its surface. Mark the central bright spot on the wall.
3. Measure diffraction: for each color laser, measure the distance from the central spot to the first diffraction spot, as well as the distance from the CD to the wall.
4. Calculate proportions: use the red laser as a reference. Create a ratio of diffraction spacing (red to green, red to purple) and compare it to the known wavelength of red light. Solve for the approximate wavelengths of the other colors.
5. Discuss energy: connect the results back to the flame test. Shorter wavelengths correspond to higher energy transitions, showing again how the color of light is linked to the behavior of electrons.

The below video outlines the general procedure for creating the diffraciton grating discussed above: 

For years, my Design for Social Good class has focused on building assistive technology devices, specifically computer switches for people with disabilities such as quadriplegia, cerebral palsy, and spinal cord injuries. These projects have been meaningful for students, giving them the chance to learn CAD, 3D modeling, 3D printing, soldering, circuit board development, and coding. They have also helped students think about how design intersects with real human needs.

Much of our earlier work centered on creating instructions and publishing them online. For example, our work with Makey Makey was featured here. Students also published guides on Instructables that allowed caregivers and teachers to build simple switches. And for those ready to go beyond Makey Makey, we have an archive of Arduino Leonardo hub systems here.

​These projects were important, but they often stopped short of true service. Students were convinced they were helping by making instructions, but in reality they were not directly interfacing with the people who rely on these devices. Pairing students with individual users has not been sustainable either, since it depends on me connecting them to someone.

This year we are moving in a new direction. Instead of limiting ourselves to pseudodesign service, we are partnering with Makers Making Change. Through this organization, students can publish their switch designs to a community where real users can request devices. Makers are then paired with those requests. This adds a direct service element to our curriculum and allows students to see their work move from idea, to prototype, to something used by a person who needs it.

The concept of a switch may seem simple, but designing one that is user-friendly, durable, and creative opens the door to complex learning. Students practice soldering, electronics, coding, and design thinking while also considering the medical and human applications of engineering. From air pressure systems to joystick capacity, small design choices lead to big creative outcomes.
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You can follow our past and current work through this link collection. It includes our websites, Instructables, and now our contributions to Makers Making Change. For a behind-the-scenes look at how ideas take shape in the classroom, you can browse our Google Photos album, which shows the iteration process in real time.The hope is that by building for real users, students will see their design work not only as practice, but as direct contribution.I’m always amazed by my students’ creativity.

​The image below shows a DIY "Sip-and-Puff" controller they built with custom 3D-printed parts and a MakeyMakey. Expect more posts on this project as we continue through the semester. I highly recommend introducing your STEM students to adaptive controllers and assistive technology in general. It’s rewarding work and such a natural way to put purposeful design into action.