Here is the latest side project getting the train moving remotely but this time by tilting the remote to determine the direction and speed. I've tried a new format / ran it generative Ai to try and improve the visuals, see what you think.
Once I had the power adaptor working with the two motors, I wanted to take the project one step further.
The obvious next challenge was: could I actually fit all of this inside a LEGO train?
The answer, as it turns out, was yes — although it was a bit of a squeeze.
I started by adding lights to the train. I wanted lights at both the front and the back, so I connected one set of lights to motor 1 connector and repeated this for the the second set of lights adding them to the motor 2 connector. This meant that everything could run from the same battery supply rather than needing separate power sources.
The next problem was fitting everything inside.
There are now two motors, the battery holder, the power adaptor, wiring and the front and rear lights all competing for space inside a relatively small LEGO train.
There was quite a lot of carefully arranging wires, moving components around and trying to get everything to fit without stopping the train body from going back together.
Eventually, I managed it.
The result is a LEGO train that looks fairly normal from the outside but has quite a lot going on underneath. The two motors provide the drive, while the battery pack also controls the lights at either end.
It's definitely not the neatest piece of LEGO engineering I've ever produced, but that's rather the point of this project. I'm interested in seeing what can be made to work with the parts I have, rather than designing the perfect solution from the beginning.
And, importantly, it works.
From a cheap adaptor and some wiring to a fully powered LEGO train with two motors and lights front and back.
Here is a video of it working:
https://x.com/ChrisPenn84/status/2096696401845432332/video/1?s=46
If you like this blog check out all the others here
I wanted to experiment with connecting some of my LEGO electronics to a Micro:bit. The problem was finding a simple way of getting the LEGO connector onto the Micro:bit without destroying any expensive LEGO cables.
The answer turned out to be very cheap extension cables from AliExpress.
They were only around £1.46, so I bought a few to experiment with. At that price, cutting one up didn’t feel too painful!
First, decide how long you want the cable to be and cut it to length.
Once cut, carefully expose the four wires inside the cable.
There are four individual wires. Twist the two wires on the right together, and then repeat this with the two wires on the left.
This leaves you with two connections rather than four.
Those two connections can then be attached to the Micro:bit, as shown below.
And that’s it. A £1-ish LEGO extension cable has become a very useful little interface for experimenting with LEGO electronics and a Micro:bit.
Sometimes the simplest solutions are the best ones. I've found that you can also stack them. So I have been able to run a motor and lights from the same connector with no issue.
If you like this blog check out all the others here
Until now, I've always had to cut the original LEGO connector off the motor and wire it directly to the motor controller. For my own projects this hasn't been a problem, but I know a lot of people understandably don't want to permanently modify or sacrifice their LEGO motors.
Today I think I've found a much better solution.
As you can see above, I've attached a LEGO Power Functions connector to the motor output on my motor controller board. This creates a simple adapter that allows a LEGO Power Functions motor to be connected directly to the motor controller without cutting the original LEGO cable.
I've only carried out some initial testing so far, but the early results are very promising. I can now connect a LEGO Power Functions motor directly to the motor controller using my homemade adapter.
Here is my first attempt at running a LEGO motor through the new adapter:
https://x.com/ChrisPenn84/status/2091255420975223166
This could make things much easier for anyone who wants to combine LEGO Power Functions motors with Micro:bit projects, particularly if you don't want to cut or modify your LEGO motors.
The great thing is that there should be no need to sacrifice LEGO motors anymore. Even better, the connectors needed to make these adapters cost only a few pounds from places such as AliExpress.
This is still an early prototype, so I haven't tested it extensively yet. I'm going to try it with some different LEGO motors and use it in some of my upcoming Micro:bit projects.
I'll also write a separate post explaining exactly how I modified the connector and made the adapter. It's actually surprisingly simple, and I'll show the steps involved so you can make your own.
Hopefully this will make it much easier to combine LEGO, Micro:bit and Power Functions without having to destroy perfectly good LEGO motors.
And if you like this project, check out my other LEGO and Micro:bit projects for more experiments and ideas.
If you like this blog check out all the others here
When Ai(my only experience so far is Co-Pilot and more recently ChatGPT) first appeared, like many teachers, I was curious.
Not because I thought it was going to revolutionise education overnight, but because I wanted to know whether it could genuinely help with some of the more repetitive jobs that come with teaching.
Just after Christmas last year, I knew I had a much bigger project ahead of me. Our KS3 Computer Science curriculum needed a new Ai unit and I wanted to create a completely new Scheme of Learning. Before trusting AI with something as important as curriculum design, I decided to start much smaller.
Could it create useful recall quizzes?
Creating retrieval quizzes is one of those jobs that takes longer than people realise. Writing one quiz isn't difficult, but when you're creating one for every lesson across an entire Scheme of Learning, the hours soon add up.
It seemed like the perfect place to begin my experiments.
Can generative AI create recall quizzes that I'd actually be happy to use in the classroom?
Not just quickly.
Not just in large quantities.
But quizzes that accurately assess the key knowledge from a lesson and help students retrieve what they've learned.
My first attempt was deliberately simple.
I uploaded the PowerPoint from an existing lesson and asked ChatGPT to generate a 10-question multiple-choice recall quiz based on its contents.
The goal was simple.
If this worked, I could potentially create recall quizzes for every lesson in a Scheme of Learning in a fraction of the time it would normally take.
My original prompt looked something like this.
The results were surprisingly good...but they weren't always consistent.
The short answer?
Yes...but not consistently.
ChatGPT had no problem producing quizzes at scale. Within seconds I had a complete recall quiz.
The challenge wasn't speed.
It was quality.
Some quizzes were excellent and tested exactly what I wanted students to remember.
Others focused on minor details, occasionally missed important concepts or simply didn't have enough information to work from.
I quickly realised something that seems obvious now.
My teaching PowerPoints weren't written as standalone learning resources.
They were designed to support my explanations during a lesson.
AI only sees what's written on the slides.
It doesn't hear everything you say.
Like most things with AI, better input produced better output.
Instead of simply asking ChatGPT to create a recall quiz, I became much more specific about what I wanted.
After several iterations my prompt evolved to include instructions such as:
That last point might sound trivial, but one of my earliest quizzes had a worrying habit of making Option B the correct answer far too often!
Here's the updated version of the prompt.
I also realised that the quality of the source material mattered just as much as the prompt.
Where possible I began uploading:
The richer the information, the better the quizzes became.
For A Level topics, I found that uploading the specification alongside the lesson PowerPoint produced particularly reliable results.
One of the examples I tested was from OCR A Level Computer Science – Systems Software (1.2.1 a–d).
The resulting quiz looked like this.
What is the primary purpose of an operating system?
A. To permanently store files
B. To create application software
C. To manage the computer's hardware and software resources
D. To connect computers to the internet
E. To increase the speed of the CPU
Which memory management technique divides memory into fixed-size blocks called pages?
A. Paging
B. Segmentation
C. Virtual Memory
D. Caching
E. Defragmentation
What is an interrupt?
A. A type of CPU register
B. A section of RAM
C. A scheduling algorithm
D. A program stored in the BIOS
E. A signal that requests the CPU's attention
Which scheduling algorithm always executes the process that arrived first?
A. Round Robin
B. First Come First Served (FCFS)
C. Shortest Job First
D. Shortest Remaining Time
E. Multi-Level Feedback Queue
Why is virtual memory used?
A. To make the processor run faster
B. To permanently store application software
C. To reduce the size of RAM modules
D. To allow programs to continue running when RAM is full by using secondary storage
E. To improve internet performance
What happens immediately after the CPU receives an interrupt?
A. The CPU finishes executing its current instruction before responding
B. The computer immediately shuts down
C. The current program is deleted
D. The interrupt is ignored until the program ends
E. The BIOS restarts automatically
Which scheduling algorithm gives every process a fixed amount of CPU time before moving to the next process?
A. First Come First Served
B. Shortest Remaining Time
C. Round Robin
D. Multi-Level Feedback Queue
E. Priority Scheduling
Which statement best describes segmentation?
A. Memory is divided into equal-sized pages.
B. Secondary storage replaces RAM.
C. Programs are stored alphabetically.
D. Each process is allocated exactly the same amount of memory.
E. Memory is divided into variable-sized sections based on logical program components.
An operating system is using the Shortest Job First scheduling algorithm. Four processes are waiting with estimated execution times of 8 ms, 3 ms, 6 ms and 2 ms.
Which process will run next?
A. 8 ms
B. 2 ms
C. 3 ms
D. 6 ms
E. The one that arrived first
A user is editing a document when they press a key on the keyboard.
Which sequence correctly describes what happens?
A. The ISR runs → Interrupt occurs → CPU saves state → Program resumes
B. CPU saves state → Program resumes → Interrupt occurs → ISR runs
C. Interrupt occurs → Program resumes → ISR runs → CPU saves state
D. Interrupt occurs → CPU completes the current instruction → CPU saves its state → ISR executes → Original program resumes
E. Interrupt occurs → Computer restarts → ISR executes → Program resumes
| Q | Answer |
|---|---|
| 1 | C |
| 2 | A |
| 3 | E |
| 4 | B |
| 5 | D |
| 6 | A |
| 7 | C |
| 8 | E |
| 9 | B |
| 10 | D |
This experiment taught me something that has stayed with me throughout every AI experiment since.
AI isn't really about writing better content.
It's about giving it better information.
If your PowerPoint contains very little text because you provide the explanation during the lesson, don't expect AI to magically fill in the gaps.
Instead, provide richer sources of information such as:
The better the context, the better the output.
I also discovered that prompt engineering genuinely matters.
Small additions—such as asking for progressively harder questions, believable distractors and randomised answer positions—made a noticeable difference to the finished quiz.
Absolutely.
In fact, it's something I now use regularly.
Once I'd refined both the prompt and the information I was providing, AI became an excellent first draft generator.
Would I use the very first quiz it produced without checking it?
No.
Would I happily let AI produce a first draft that I could review in a couple of minutes?
Every time.
For me, this is one of the first classroom tasks where AI genuinely saved me time without compromising quality.
It doesn't replace teacher expertise.
It simply gives me more time to use that expertise where it matters most.
Would I Use It Again? Yes.
If you like this blog check out all the others here
Technology never stands still, and neither does education so by proxy neither does any teacher worth their salt.
Through this blog I've enjoyed exploring new ideas, building projects, experimenting with technology and sharing what I've learned along the way. Sometimes those projects have been built with Raspberry Pi, micro:bit, LEGO or Minecraft. Today, many of those experiments involve AI.
This isn't a blog about AI.
It's a blog about curiosity.
Whenever a new technology appears, I'm less interested in the headlines and more interested in the practical questions:
Can it actually do this?
Can it save time?
Can it improve learning?
Is it worth using?
Or is it just another shiny new tool?
Rather than making bold claims, I'd rather find out for myself.
Over the coming months I'll be putting generative AI through a series of real-world classroom experiments.
Each experiment starts with a genuine question, follows the same simple format and ends with an honest conclusion.
For every experiment I'll share:
The aim isn't to prove that AI is brilliant or that it's terrible.
The aim is to work out where it genuinely adds value—and where professional judgement still matters.
The first series of experiments will include:
Some experiments will be successful.
Some won't.
I'll share both.
Because I think we learn just as much from the things that don't work as the ones that do.
Thanks for stopping by. I hope these experiments help you discover something useful, challenge a few assumptions and perhaps be helpful.
See you in the next one.
If you like this blog check out all the others here
