Solving problems with AI
Why not explore the AI glossary?
AI glossary of terms
Updated: 22 Sep 25
Updated: 22 Sep 25
This module builds on the learning from Understanding AI and helps young people begin to solve real-world problems using AI tools.
Updated: 13 Jan 26
Updated: 23 Jan 26
Updated: 13 Jan 26
Updated: 13 Jan 26
Updated: 13 Jan 26
Learners will explore how AI is used in everyday life and learn to tell predictive and generative AI apart. They will investigate problems they can solve using AI, and reflect on when AI tools are an appropriate way to solve a problem.
By the end of this lesson, learners will be able to describe the stages of an AI project lifecycle — from ideation to deployment of a model. They will see how these stages are used in real AI-based research projects that help people all over the world.
In this lesson, learners will use the AI project lifecycle to begin creating their own machine learning model to solve a problem of their choice. They will choose a problem and a user from two options. They will be introduced to a ‘user-focused’ design approach and then prepare their data by splitting it into classes.
In this lesson, learners will continue to apply the AI project lifecycle. They will begin to train a machine learning model to solve a problem of their choice and test it. They will examine the relationship between confidence scores, confidence thresholds, and accuracy in machine learning model predictions. Finally, they will measure the accuracy of their machine learning (ML) models.
In this lesson, learners will evaluate and explain their AI model using a model card, which documents the model’s purpose, test results, and limits. They will then explore the range of career opportunities available in AI and fields using AI applications, including roles at Google DeepMind and areas of personal interest.
