Prompt Engineering
Students learn roles, constraints, examples and iteration so outputs become more controlled and explainable.
GenAI guide
Generative AI can be more than using tools. Students can learn prompt structure, evaluate outputs and build a creative evidence portfolio.
Use this orientation to match the advice with the student's DofE Skills section decision.
Parents and students comparing this route for a DofE Skills section activity.
A strong generative AI activity should build judgement, structure and reflection.
Students learn roles, constraints, examples and iteration so outputs become more controlled and explainable.
Students compare results, identify weaknesses and explain why one prompt worked better than another.
Students create images, slide drafts, audio or video-style outputs with notes on how they were produced.
Generative AI appears most strongly in Bronze, then the pathway moves deeper into machine learning foundations and Python ML.
Prompt engineering, image generation, presentations, audio and video-style showcase tasks.
Blockly AI, regression, neural networks and sentiment classification build the model foundation.
Python machine learning and data science projects give advanced students a deeper technical route.
It can be suitable when the student learns and documents a skill over time, such as prompt design, output evaluation and responsible creative production.
GenAI is most beginner-friendly at Bronze, but the wider AI pathway can progress into Blockly AI, machine learning foundations and Python ML.
Evidence may include prompt logs, before-and-after outputs, reflection notes, generated media examples and a final showcase.
Bronze AI is the best starting point for students interested in prompt engineering and creative GenAI.