Artificial Intelligence is the branch of computer science dedicated to creating systems capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. At its core, AI involves developing algorithms that allow machines to learn from data, recognise patterns, and make autonomous predictions.
The IGO AI category challenges students to harness these technologies to address real-world problems. Participants must align their projects with at least one United Nations Sustainable Development Goal (UN SDG), exploring how AI can contribute to a more sustainable and equitable future.
This category is divided into two age divisions:
- Junior Division: Ages 10–14.
- Senior Division: Ages 15–19.
Students may participate as individuals or in teams of up to three. Projects can be either theoretical proposals or technical implementations. Coding is not mandatory; students are encouraged to use “no-code” AI platforms (such as drag-and-drop machine learning tools or visual chatbot builders) or traditional programming languages if they prefer.
Project Tracks
Students must select one of the following tracks for their submission:
- AI Proposal (Theoretical): Create an in-depth proposal for how AI technologies could be applied to solve a specific community or global challenge. This track focuses on research, ethical analysis, and conceptual design.
- AI Implementation (Technical): Build a functional AI-based solution, such as a mobile app, website, or hardware-integrated device. This can be achieved through coding (Python, C++, Java, etc.) or no-code tools.
Judging Criteria
The IGO judges will evaluate projects based on the following 100-point scale:
| Criteria | Description | Points |
|---|---|---|
| Research Question & SDG Alignment | A clear purpose that identifies a real-world problem and its direct link to a UN Sustainable Development Goal. | 10 |
| Objective and Scope | Well-defined and achievable goals that reflect current trends in AI and machine learning. | 10 |
| Background Research | Evidence of exploring existing solutions and scientific literature to ensure the project offers a unique perspective. | 10 |
| AI Design & Methodology | Logical explanation of the chosen AI methods (e.g., image recognition, natural language processing) and how they function. | 15 |
| Technical Implementation / Logic | Systematic development of the tool or proposal, including testing, accuracy evaluation, or data validation. | 10 |
| Ethical Awareness & Safety | Consideration of AI ethics, including potential biases, data privacy, and strategies to prevent unintended harm. | 15 |
| Creativity and Innovation | Use of original ideas and creative problem-solving to address the selected challenge. | 15 |
| Oral Presentation | Clear, concise communication of the project’s importance and thoughtful responses to judge’s questions. | 10 |
| Overall Impression | Demonstrates genuine enthusiasm, significant effort, and a solid understanding of the AI principles involved. | 5 |
Ethical and Safe Use Rules
To ensure the responsible development of AI, all participants must adhere to the following:
- Originality & Attribution: Projects must be the students’ own work. All AI tools, platforms, and datasets used must be clearly identified and credited.
- Generative AI Declaration: If generative AI (like ChatGPT or DALL-E) was used to assist in the project, participants must declare which parts were generated and which were human-created.
- Data Privacy: Students must not use or collect personally identifiable information (PII) without explicit consent and must demonstrate an awareness of data security.
- Bias Mitigation: Participants should explain how they attempted to identify and reduce bias within their training data or algorithms.
- Safety: Projects must not encourage illegal acts, discrimination, or harmful behaviour.
How to Present
- Research Paper & Abstract: All participants must submit a written summary detailing their research, lessons learned, and the AI tools utilised.
- Project Journal: Keep a detailed log of the experiment or development process, including iterations and challenges overcome.
- Display Board: Face-to-face participants must use a three-fold display board (maximum 91cm height x 122cm width).
- Media Requirements:
- Proposal Track: A digital or physical poster detailing the conceptual solution.
- Implementation Track: A short video (maximum 4 minutes) demonstrating the functional AI solution.
- Q&A Session: All students must be prepared to answer questions from the judges in English regarding their logic, ethics, and findings.
Example Project Ideas
- Junior (Ages 10–14): “Smart Sort” Recycler (SDG 12: Responsible Consumption and Production) Using a no-code image classifier (like Google’s Teachable Machine), a student trains an AI to recognise the difference between paper, plastic, and glass through a webcam. The project includes a proposal for a “smart bin” that assists community members in sorting waste correctly to reduce landfill contamination.
- Senior (Ages 15–19): Predictive Drought Monitor (SDG 13: Climate Action) A team develops a machine learning model using Python and historical environmental datasets to predict periods of drought in their local region. They evaluate the accuracy of the model and discuss the ethical implications of using AI predictions to influence local agricultural policies and water management.
Analogy for Understanding: Developing an AI project is like training a new apprentice. You must provide them with the right materials (Data), give them clear rules to follow (Algorithms), and constantly supervise them to ensure they are making fair and helpful decisions for the community (Ethics).