What to prepare
Prepare a working project, its editable source or project files, a report and a demonstration video. Identify external resources and your own contribution clearly.
Sub-Category
Artificial Intelligence Systems
SW02 · Software, AI & Simulation
Develop innovative AI solutions using machine learning, deep learning, natural language processing or computer vision technologies.
What happens in this category?
Teach a system to recognise and reason.
Build a project in which artificial intelligence has a meaningful job to do. It might recognise patterns in images, make sense of data or support a useful decision. Start with a clear problem, then choose data and a method that suit it.
During the demonstration, your team shows what the system produces and how you tested it, including examples it has not simply memorised. Explain where it works well, where it makes mistakes and why its results should be useful to the people using it.
The jury considers the model’s performance, implementation, practical value and responsible use. Students should be able to explain their own contribution and the limits of their claims. Dataset declarations, test evidence and submission requirements are described in the official rulebook.
FOCUS AREAS
Project Focus Areas
Machine Learning
Applying supervised and unsupervised learning models, neural networks and deep learning techniques.
Data Analysis
Processing and visualizing large datasets to extract meaningful insights.
Ethical AI
Developing explainable, transparent and socially beneficial artificial intelligence systems.
PROJECT DEVELOPMENT
Shape the future with intelligent systems
Participants develop AI projects in areas such as image recognition, natural language processing, prediction or recommendation systems.
Model accuracy, dataset quality, innovation and ethical usage are important evaluation factors.
From preparation to competition
Use this guide to understand the experience. Read the official rulebook for the complete entry and technical requirements.
On competition day
Run the project live, demonstrate its main functions and answer questions about your choices. Be ready to explain tests, errors and improvements in your own words.
What the jury looks for
Assessment follows the category rubric and your registered level. A working, well-understood solution matters more than feature count or unnecessary complexity.
Levels 01–07 • Live demonstration • Maximum 100 points
Report: maximum 10 pages. Video: maximum 3 minutes. Include source code and setup instructions, dataset declarations where applicable, an appropriate performance metric and evidence of testing on unseen data.
| Evaluation criteria | Points |
|---|---|
| AI Model Performance | 30 |
| Complexity & Implementation | 25 |
| Real-World Applicability | 25 |
| Ethical Considerations | 20 |
Project Presentation Area
OFFICIAL DOCUMENT
Competition Rules & Guidelines
Review the official competition rules, evaluation criteria, project requirements and submission guidelines before preparing your project.
Participation implies acceptance of the official rulebook. All teams, mentors, and students are expected to read, understand, and comply with the rules.