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To make your AI storytelling friend, 
you’ll train a machine learning,  

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or ML, model to recognise when 
a toy moves in different ways. 

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You’ll then combine this model with code 
to make different sounds and show different  

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icons on the micro:bit’s LED display.

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Then you’ll download the model and the code to a micro:bit and use it on your toy to help tell a story. 

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Our story is about a bear called Lucy, but 
you can change the project to fit your own.

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[MUSIC]
This is Lucy the bear.

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She wants to be a gymnast when she grows up, so every 
day when she wakes up, she practices her jumping.

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She jumps as high as the ceiling.
Then after breakfast she practices her rolling.

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She rolls round and round until 
her whole world is spinning. 

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Then she takes a break and has a little nap.

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To start making your AI storytelling friend, click ‘Open in micro:bit  CreateAI’ to launch the project. 

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This project comes with 8 samples 
of movement data for three different actions:

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jumping, rolling and sleeping.

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micro:bit CreateAI collects movement data samples using the accelerometer, the micro:bit’s movement sensor. 

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To add your own data samples, you need 
to make a data collection micro:bit. 

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If your computer has Bluetooth enabled, then 
you'll just need 1 micro:bit and a USB data lead. 

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If you don’t have a Bluetooth connection, 
you’ll need to use 2 micro:bits. 

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Follow the instructions on screen to connect.

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Once your data collection micro:bit is connected,
attach it to your toy like this.

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You’ll see the lines on the live graph
change as you move your toy.
As this project already includes quite a lot

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of data samples, we suggest you add 1 sample for 
each action for now and collect more data later. 

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Click on the ‘jumping’ action so 
you can add more data samples to it. 

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You will get a countdown before 
a 1 second recording starts. 

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Click record and start moving your toy immediately 
to make sure you get a clean data sample. 

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A clean sample is one where you’re 
moving for the entire sample,  

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you don’t start late or finish moving early.
Next try adding an extra data sample to the  

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‘rolling’ and ‘sleeping’ actions.

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You’ll notice that when your toy is asleep, the x,y, and z lines change places depending on the orientation of the micro:bit. 

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Click ‘Train model’ to build the ML model.
The tool now builds a mathematical  

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model that should recognise different 
actions when you move your micro:bit. 

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As soon as the model has been trained, 
you’ll see the Testing model page. 

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Your data collection micro:bit can now be 
used to test how well the model is working. 

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It should still be connected to the tool, and 
you’ll see that as you move it, CreateAI is  

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estimating what action you are doing.

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Move your toy in different ways to see 
the estimated action and the certainty bar graph change. 

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The percentage shows how certain, or confident, 
the model is that you are doing each action.

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You may notice your model is not 
estimating some actions accurately. 

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In that case it’s a good idea to click on 
‘Edit data samples’ and improve your model. 

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Machine learning models usually work best with 
more data, so record some extra samples for each  

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of the actions, or focus on collecting more data 
for the action that was problematic in testing. 

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Clean data samples also help an ML model 
work better so examine your data set and  

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identify any samples that could confuse the 
model. You can delete these by pressing X. 

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Once you’ve added more data and checked your 
data set, click ‘Train model’ again. Then test  

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the model again on the ‘Testing model’ page.

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Once you’re happy with how the ML model is behaving, you can use it with 
the ready-made project code. 

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Click on ‘Edit in MakeCode’ to see the code 
blocks in a special version of Microsoft MakeCode. 

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You can return to see your 
data in CreateAI at any time  

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using the arrow in the top left of the screen.
These blocks use the model you’ve created in code. 

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The ‘on ML… start’ blocks react 
when the ML model decides your  

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toy is making a particular movement, or action.
Depending on the action, the code shows different  

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icons on the micro:bit’s LED display output 
and plays different sounds on its speaker. 

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If it’s not sure what action your toy is doing – 
if the action is ‘unknown’ – it clears the screen. 

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And when each action stops, the code 
stops the micro:bit making any sound. 

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To make the code and the ML 
model run on your micro:bit,  

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you just need to download it to a micro:bit. 

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Press ‘Download’ and follow 
the instructions on screen. 

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Now test the finished project on 
a micro:bit attached to your toy.

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Do the correct sounds play and icons display 
when your toy makes different movements?

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Does it work equally well when 
someone else moves the toy? 

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If not, you can go back and collect more 
data from them and re-train the model. 

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Congratulations, you’ve trained your toy 
to react to different kinds of movement  

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using data you have collected, training an AI 
machine learning model, and combining it with  

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code to make an interactive storytelling toy!
What other actions, or movements might your toy  

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make, perhaps as part of telling a story? Can 
you add them using the micro:bit and CreateAI?