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Artificial Intelligence: use AI to solve a real-world problem

AI is the part of computer science that builds systems able to do tasks that usually need human intelligence, such as seeing, listening, making decisions and translating languages. AI learns from data, spots patterns and makes predictions on its own.

1How AI works, in three steps

You don't need to be an expert. This is the core idea behind every AI project.

📊

Learn from data

The system is given lots of examples.

🔍

Spot patterns

It finds what the examples have in common.

🎯

Make predictions

It uses those patterns to decide or predict something new.

2Who can take part

The essentials at a glance.

🧒

Junior Division

Ages 10–14

🎓

Senior Division

Ages 15–19

👥

Individual or team

Work alone or in a team of up to 3

🧩

Coding is optional

Use no-code AI tools (drag-and-drop or visual builders) or a programming language

3Choose your track

Pick one of these two routes for your project.

Theoretical

💡 AI Proposal

Plan in detail how AI could solve a specific challenge in your community or the world.

  • Research the problem
  • Think through the ethics
  • Design your idea on paper
You'll show: a digital or printed poster explaining your idea.
Technical

⚙️ AI Implementation

Build a working AI solution, such as an app, a website or a device connected to hardware.

  • Code it (Python, C++, Java, etc.)
  • or build it with no-code tools
  • Test how well it works
You'll show: a video of up to 4 minutes demonstrating it working.

4How you'll be judged

Projects are scored out of 100. The longer the bar, the more the criterion is worth.

Total100 points

Research question & SDG link

A clear real-world problem, directly linked to a UN SDG.

10pts

Objective & scope

Clear, achievable goals that reflect current trends in AI.

10pts

Background research

Shows what already exists, so your project adds something new.

10pts

AI design & method

Explains which AI methods you chose (e.g. image recognition, language processing) and how they work.

15pts

Technical work / logic

Step-by-step development, with testing, accuracy checks or data validation.

10pts

Ethics & safety

Considers bias, data privacy and how to prevent harm.

15pts

Creativity & innovation

Original ideas and creative problem-solving.

15pts

Oral presentation

Explains the project clearly and answers judges' questions well.

10pts

Overall impression

Enthusiasm, effort and a solid understanding of AI.

5pts

5Use AI ethically and safely

Every participant must follow these five rules.

✍️

Original work

The project must be your own. Name and credit every AI tool, platform and dataset you use.

🤖

Declare generative AI

If you used tools like ChatGPT or DALL-E, say which parts were AI-generated and which were yours.

🔒

Protect privacy

Never use or collect personal information without clear permission. Show you understand data security.

⚖️

Reduce bias

Explain how you checked for and reduced bias in your data or algorithm.

🛡️

Stay safe

Nothing that encourages illegal acts, discrimination or harmful behaviour.

6What to prepare

Your checklist for presenting the project.

✓

Research paper & abstract

A written summary of your research, what you learned and the AI tools you used.

✓

Project journal

A log of your process: each version, and the problems you solved along the way.

✓

Display board

Face-to-face only: a three-fold board, max 91 cm high × 122 cm wide.

✓

Poster or video

Proposal track: a poster. Implementation track: a video of up to 4 minutes.

✓

Q&A with judges

Be ready to explain your logic, ethics and findings in English.

7Project ideas to inspire you

One example for each division.

Junior · 10–14SDG 12: Responsible Consumption

♻️ "Smart Sort" Recycler

Using a no-code image tool such as Google's Teachable Machine, a student trains an AI to tell paper, plastic and glass apart through a webcam, then proposes a "smart bin" that helps people sort waste correctly and keep it out of landfill.

Senior · 15–19SDG 13: Climate Action

🌦️ Predictive Drought Monitor

A team uses Python and past environmental data to build a machine learning model that predicts droughts in their region. They test its accuracy and discuss the ethics of using AI predictions to guide farming and water policy.

Think of it like training a new apprentice

Building an AI project means guiding a learner to make good decisions for the community.

📦

Data

Give them the right materials to learn from.

📏

Algorithms

Give them clear rules to follow.

🧭

Ethics

Supervise them so their decisions are fair and helpful.

Ready to build something with AI?

Register your project and start making a difference.

Register your project