Participants will follow a structured sequence of guided online lessons and independent project work. They will begin by exploring how AI systems learn from examples, including the basic stages of collecting data, training a model, testing its performance and improving results. Instead of writing code, students will use accessible visual and no-code tools to experiment with image, text, sound and classification-based AI tasks.

During the course, each young person will complete practical activities such as designing prompts, comparing AI-generated outputs, training simple machine learning models, testing predictions and reflecting on how accurate or reliable the results are. They will also consider responsible AI use, including bias, data quality, copyright, safety and the limitations of AI tools.

As the course progresses, students will build a small portfolio of completed work. This may include screenshots, short written reflections, project notes, model results, prompt experiments and final creative outcomes. The focus is on helping students understand the process behind AI, not simply using AI as a shortcut.

By the end of the activity, each participant should have created several AI-based project outcomes and be able to explain what they built, how it works and what they learned from improving it.