Experience AI - Lessons

Explore our collection of lesson plans, presentations, simulations, worksheets and hands-on projects to help introduce AI to KS3 students (aged 11–14). Whether you're a seasoned teacher or just getting started, you'll find something useful here.

Unit of lessons

Our set of lessons provides everything you need, including lesson plans, presentations, videos, and worksheets.

Unit overview

Updated: 31 Jan 24

Learning graph

Updated: 31 Mac 23

AI glossary of terms

Updated: 18 Apr 23

Lesson 1: What is AI?

In this lesson, students will explore the current state of artificial intelligence (AI) and how it is used in the world around them. They will look at the differences between rule-based and data-driven approaches to programming, and they will consider the benefits that AI applications could bring to society, as well as any negative consequences that their use could lead to.

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Lesson 2: How computers learn from data

In this lesson, students will focus on the role of data-driven models in AI systems. They will be introduced to machine learning and learn about three common approaches to creating models. Finally, they will explore classification, a specific application of machine learning.

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Lesson 3: Bias in bias out

In this lesson, students will create their own machine learning model to classify images of apples and tomatoes. They will discover that a limited data set can lead to a flawed ML model. They will then explore how bias can appear in a data set, leading to ML models producing biased predictions.

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Lesson 4: Decision trees

In this lesson, students will take their first in-depth look at a type of model: decision trees. Students will see how different training data results in the creation of different models, experiencing first-hand what it means for models to be data-driven. Finally, students will see why machine learning is used to create decision trees.

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Lesson 5: Solving problems with ML models

In this lesson, students will be introduced to the AI project lifecycle and use it to create a machine learning model. They will apply a user-focused approach to working on AI projects. They will choose a project, then train a machine learning model, then test their model to determine its accuracy.

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Lesson 6: Model cards and careers

In this lesson, students will finish the AI project lifecycle by creating a model card to explain their ML model. In the final activities, students will explore a range of AI-related careers. They will hear from people working in the field of AI, as well as considering how AI applications and machine learning can be used in fields they are interested in.

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Additional lessons

Large Language Models (LLMs) - PSHE

This lesson is a sequence of activities designed to educate students about the development of large language models (LLMs). The activities will give students the opportunity to explore the purpose and functionality of LLMs, while also examining the critical aspect of trustworthiness in their output.

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Ecosystems and AI - Biology

In this lesson, your students will explore the impact of environmental changes on the organisms in an ecosystem, in this case the Serengeti National Park in Tanzania. They will consider the problems of measuring biodiversity in order to maintain it before learning about artificial intelligence (AI) and considering the benefits that AI applications are bringing to conservation in the Serengeti. There is scope for a wider look at societal attitudes to AI, alongside discovering uses of AI in science with a focus on careers.

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