Ai with micro:bit

AI with the BBC micro:bit

AI with the micro:bit often uses machine learning, which is a type of artificial intelligence where the system learns from examples instead of following fixed rules. For example, the micro:bit can be trained to recognise different gestures such as shaking, tilting, or staying still. Each gesture produces different sensor data, and the AI model learns to identify patterns within this data.

How AI works with the micro:bit

The process of using AI with a micro:bit usually follows these steps:

• Data is collected using the micro:bit’s sensors
  
  

• The collected data is labelled so the computer understands what each example represents
  
  

• The AI model is trained to recognise patterns in the data
  
  

• The model is tested to check accuracy
  
  

• Improvements are made if the results are incorrect
  
  

• The final model is converted into code and uploaded to the micro:bit
  
  

Tools and platforms used

Different platforms are used to create and train AI models for the micro:bit:

• Microsoft MakeCode for beginner-friendly AI projects
  
  

• Python-based tools for text-based programming
  
  

• External machine learning platforms that simplify training and testing
  
  

These platforms use visual tools and simple explanations, making AI easier for beginners to understand.

Examples of AI projects using the micro:bit

Using AI with the micro:bit allows students to create smart and interactive projects such as:

• Gesture-controlled systems
  
  

• Activity recognisers
  
  

• Smart alarms
  
  

• Environmental monitoring systems
  
  

These projects help students understand how AI is used in real-world technologies like smartphones, fitness trackers, and smart homes.

Skills developed by learning AI with the micro:bit

Learning AI with the micro:bit helps students develop important skills, including:

• Understanding how data is collected and used
  
  

• Learning how computers make decisions
  
  

• Improving problem-solving and logical thinking
  
  

• Understanding the importance of testing and accuracy
  
  

• Awareness of ethical issues such as fairness and data privacy