Introduction to Artificial Neurons (Key Stage 3 / GCSE)

1. Lesson Objective

Understand how an artificial neuron (Perceptron) processes binary pixel inputs and adjusts connection weights to classify data.

2. Workshop Overview

Students draw a digit on a grid, analyze how pixels activate inputs, and track how weights change during classification errors using the MNIST Perceptron cabinet.

3. Scheduled Lesson Steps

1. 1

   Hook

   Show a handwritten number and ask the class how they know it's a '3'. Explain that computers only see grids of numbers (0 and 1).

2. 2

   Interactive Lab

   Open the MNIST Perceptron Cabinet. Ask students to draw a '0' and look at the weights visualizer. Explain that red pixels show positive weights and blue show negative weights.

3. 3

   Activity

   Have students deliberately draw a '3' that looks like an '8'. Analyze why the prediction was incorrect. Explain how the network learns from errors by adjusting weight scores.

4. 4

   Wrap-up

   Discuss how stacking millions of these neurons together creates deep neural networks used in face scanners and self-driving cars.