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Using AI with micro:bit to teach students about the climate crisis

26 January 2026

Photo of a microbit and battery pack. The microbit shows a heart image. The background is a sheet of coloured stickers with pictures of the microbit on. This week, I went to BETT, the education technology show in London's Docklands. In 1988, long before the Excel Centre was built, I was on that spot for Jean-Michel Jarre's spectacular Destination Docklands. During the concert, images of cultural and technological revolution were projected onto the Millennium Mills building across the dock.

At BETT, the revolution that everybody was talking about was AI. Some companies were promoting it for administration and grading, with one asking "Are you still marking like it's 1995?". Others had products to help students learn about how AI works and how it can be used safely.

One session was called "Coding for Climate Action". It brought together three things I have a keen interest in: sustainability, AI, and micro:bits. If you're not familiar with the micro:bit, it's a small programmable device with a grid of LEDs, built-in sensors, and GPIO pins for connecting it to peripherals and electronics projects. I saw several micro:bit robots rolling around table tops on various booths.

To give you an idea of what the micro:bit can do, a 15-year old student from Hong Kong showed me his project on one of the booths. It was an assistant for the elderly, featuring a fall sensor, medication reminder, and games to keep the mind sharp. My free Coding Compendium book includes projects to make a pomodoro timer and use the micro:bit as a steering wheel in a Scratch game.

The Coding for Climate Action session, run by Matt Hewlett from charity Digitall, shared a series of lesson plans for years 4 to 8. Projects include logging temperature around the playground to show how trees help regulate temperature; a fitness tracker to start a conversation about how we can reduce the demand on healthcare, which is responsible for a lot of road emissions; and monitoring plant growth.

At the start of the session, we were given a micro:bit and challenged to work out how to make it display a skull image. I was familiar with the device, so I tried tilting it and pushing its buttons in various combinations, but nothing worked. The answer was to whirl it around like the arm movement of a cricket bowler but without letting go. Matt's nine-year-old son had used machine learning to program the micro:bit to recognise that gesture.

Matt demonstrated CreateAI, which enables you to train the micro:bit to recognise custom movements using its combination of sensors. In one of the projects in Coding for Climate Action, students use CreateAI to train their micro:bit to recognise a landslide in what Matt says is their most popular lesson. There's also a project to track storm movements.

The CreateAI training works by recording sensor movements as you perform the gesture you want to recognise. You provide several sample movements to increase the accuracy when the software attempts to recognise the movement later. This process enables your programs to respond to nuanced and sophisticated movements that you couldn't describe in a human-authored program, far beyond the relatively simple x, y, and z axis movements you can easily detect.

One of the best features of the micro:bit is the built-in communication, so Matt demonstrated how we could all move our devices to simulate gentle or extreme winds, and the data can be collected centrally and shown on a map with each micro:bit channel representing a location.

It was good to see the conversation around AI expand beyond ChatGPT and similar generative AI tools, and into machine learning which is widely used in academic and industrial applications. I think an understanding of machine learning also helps to understand how generative AI works, so it's a great starting point for AI education. While AI (especially generative AI) is contributing to the climate crisis because of its intensive energy use, I don't think this application of machine learning is hugely demanding of energy or resources. I'll be looking at whether I can use some of these projects in my Code Club.

Download the Coding for Climate Action projects, including lesson plans and mappings to the National Curriculum, here. You can also download my free Coding Compendium, including two micro:bit projects, and check out my Raspberry Pi and Python tutorials.

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Trimming the silence from hidden tracks using AI

05 January 2026

Although most people are streaming now, I'm still living in the world of CDs and MP3s. I buy CDs and rip them to iTunes, where I playlist them for work, listening out and about, and inspiration when running.

I recently picked up the Alt-J album 'This is All Yours' in a charity shop, and was intrigued to see that the last track was 16 minutes long. About ten minutes of that, though, is silence. It's one of the many albums that have a hidden track, concealed within the final track after a long silence. I understand that bands might want to gift listeners a song that they don't think is part of the main album, or might prefer to surprise listeners with a particular song. But it's an irritating listening experience having a long silence in the middle of a track. It means neither song can be playlisted or shuffled. A couple of much-loved albums by my favourite artists have the same issue, including Kenickie's At the Club and Depeche Mode's Sounds of the Universe. (That's Lauren Laverne from Kenickie pictured on the right in a photo I took when I was writing for Making Music. See more photos here.).

So, I wondered whether this is something ChatGPT could fix for me. I've been doing a number of experiments with it recently, so this seemed like an interesting thing to test, especially since I hadn't tried any audio processing with it before. My plan was to upload the MP3 file and have it split it into two separate files with the silence removed. I could then import these files back into iTunes.

It proved to be harder than I expected. There were several attempts where ChatGPT split the track into two files, but did not remove any of the silence. It seems the silence wasn't perfectly silent, so an alternative approach was used where ChatGPT focused on where the music was instead of where the silence was. At one point, ChatGPT was getting confused because it was overwriting the file I'd uploaded with one of the new files it had created. After a few goes, I thought that ChatGPT understood the problem even though it wasn't providing a solution. I asked it to write me a prompt I could use in a new chat to get the expected results. ChatGPT built some checks into the prompt and indicated how to perform the task, which helped to increase the success of the prompt. It took a few iterations in the new chat, too, but I did end up being able to split tracks easily.

The verdict, then? This is a problem that ChatGPT can solve, but the amount of iteration required isn't justified given I only have a handful of files to edit. It wouldn't take very long to edit the files manually using a tool like Audacity. Nonetheless, it was interesting to see that ChatGPT is technically capable of performing this work.

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