World Youth and Teenage Artificial Intelligence Competition

Award Categories

​Individual Awards:

• Special Gold Award (Top 12% of participants in the group)

• Gold Award (Top 12%-25% of participants in the group)

• Silver Award (Top 26%-45% of participants in the group)

• Bronze Award (Top 46%-60% of participants in the group)

• Excellence Award (Top 61%-100% of participants in the group)

 

Group Awards:

• Outstanding Group Award

• Outstanding Mentor Award

Award winners can request customized awards featuring the participant's name in Chinese or English.

 

Award Options:

• Electronic Certificate: HKD 200

• Electronic Certificate + Physical Certificate: HKD 290

• Electronic Certificate + Physical Certificate + Medal: HKD 325

• Electronic Certificate + Physical Certificate + Medal + Trophy: HKD 365

The deadline for award applications is two weeks after the announcement of the results.

All awards will be mailed within eight weeks after the application deadline.

Awards and certificates will be sent via SF Express, cash on delivery.

Once the awards are signed for, no replacements will be issued in case of damage or loss.

The organizers are not responsible for failed deliveries due to incorrect information or communication difficulties.

​

Competition Guidelines:

 

1. Competition Categories
Participants may self-select their competition tier based on question difficulty groupings.

2. Registration & Timing

• Complete registration and submit your exam before the stated deadline.

• A confirmation email will be sent upon registration.

• Access the competition portal at any time before the deadline to complete the exam.

3. Exam Format

• Platform: Online, with multiple-choice, fill-in-the-blank, or short-answer questions.

• Device: Computers are recommended; mobile devices are permitted but may limit functionality.

• Integrity Rules:

  • Strictly adhere to the academic honesty pledge.

  • Switching screens, impersonation, or external assistance is prohibited.

4. Competition Rules

• Duration: 30–45 minutes (varies by category). Answers auto-submit when time expires.

• Attempts: One submission per participant.

• Strategy: Prioritize familiar questions first. Unanswered items may be revisited until time ends.

• Tools: Calculators are permitted.

5. Content & Expectations
Questions assess both foundational knowledge and advanced problem-solving skills.

6. Results & Rankings

• School rankings will be calculated using participant-provided data.

• To ensure fairness, answers and results will not be released immediately post-competition.

• Final rankings will be:

  • Emailed/WhatsApp messaged to registered contacts.

  • Published on the official website and social media.

Entry Requirements：

1. Fundamentals of Artificial Intelligence

This unit focuses on the core concepts of artificial intelligence (AI), including its basic definition and development stages (e.g., the distinction between weak and strong AI, and the evolution from traditional AI to deep learning), the characteristics of common AI technologies (e.g., machine learning, image recognition, and natural language processing), and the basic architecture of AI systems (e.g., data input layer, model processing layer, and output layer). This unit systematically examines the fundamental framework of the AI ​​field, including the classification standards for AI technologies, the meaning and application scenarios of core terms (e.g., "algorithms," "datasets," and "model training"), the similarities and differences between AI and human intelligence, and the technology matching logic for different AI application scenarios (e.g., sensing technology in smart homes, recommendation algorithms in educational AI). This unit covers the application characteristics of AI in various industries and its impact on social life, and provides a deep understanding of the basic classification rules for AI technologies, the operating principles of AI systems, and the technological diversity—including the core elements of AI operations (data, algorithms, and computing power), the iterative process of AI technology, and an overview of major AI fields (e.g., computer vision, speech recognition, and reinforcement learning).

​

2. Artificial Intelligence Principles and Technical Logic

This unit explores the causes, technical mechanisms, and application boundaries of AI phenomena, such as the training and optimization factors of machine learning models, the feature extraction logic of image recognition, and the implementation process of semantic understanding in natural language processing. It analyzes the operating and control principles of AI systems (such as the impact of data quality on model accuracy, the matching rules between algorithm selection and task scenarios), and core AI theories (such as the loss function principle of supervised learning, the clustering algorithm logic of unsupervised learning, and the design of reward and penalty mechanisms in reinforcement learning). It also delves into the interrelationships between different AI technologies (such as the hierarchical relationship between machine learning and deep learning, and the integrated application of computer vision and speech recognition), the driving factors for AI technology upgrades (such as the expansion of big data scale and breakthroughs in computing power), and the operational characteristics of AI systems (such as the trend from data-driven to knowledge-driven, and the balance between model generalization and overfitting). It also explores the impact of human needs on the application of AI technology (such as the restrictions on data use imposed by privacy protection requirements and the constraints imposed by ethical norms on AI decision-making).

 

3. Artificial Intelligence Practice and Application

Strengthen practical AI skills, including the use of common AI tools (such as the simple programming platform Scratch AI module, image recognition software Teachable Machine, and data visualization tools) and the processing workflow of basic AI tasks (such as simple data collection and cleaning, basic model building and testing, and AI application scenario design and verification). Through case analysis simulating real-world scenarios, students will interpret AI data (such as image feature data, text semantic data, and behavioral trajectory data), evaluate the rationality of AI solutions (such as the adaptability of model selection, the expected and verified application results, and the identification and avoidance of potential risks), and design simple AI solutions for specific needs (such as AI recognition solutions for smart school waste sorting bins and AI control logic design for smart home lighting). Students will also initially apply AI tools and analytical methods to carry out simple AI practices, such as template-based AI programming, model training on small datasets, and comparative analysis of AI application results.

 

4、Question Types and Difficulty Levels

The competition features a variety of question types, including multiple-choice, fill-in-the-blank, short-answer, essay, and AI solution design. The difficulty level is adjusted based on the participant's age and knowledge base. The Children's Division focuses on memorizing basic AI knowledge and operating simple tools, while the Youth Division emphasizes a deep understanding of AI principles, designing AI solutions for complex scenarios, and problem-solving skills.​