Basic Information
Competition Structure
CAIO 2026-27 is a two-stage competition consisting of the National Qualifier and the National Training Camp.
• The CAIO National Qualifier will be held in November, 2026
• Selected students will be invited to the CAIO National Training Camp in January 2027
Each stage includes two 90-minute rounds:
• Round 1: AI Fundamentals Challenge
• Round 2: Applied Problem Solving
For detailed schedules and content outlines, please refer to the “Prepare” page.
Registeration Period
Registration will be open in July, 2026.
Eligibility
The competition is open to individuals only, with no restrictions on nationality.
• For the CAIO National Qualifier, all students currently enrolled full-time in secondary schools (Grades 9–12), regardless of country, are eligible to participate.
• Students advancing to the CAIO National Training Camp may include both Canadian and international students. However, only students who are enrolled full-time in Canadian secondary schools are eligible to be selected for the CAIO National Team and represent Canada at the IAIO 2026 competition.
Timeline
Until November 2026
Register and Prepare
Registration will be open in July 2026 for the CAIO National Qualifier Competition, taking place in November 2026. The exam assesses programming and mathematical skills relevant to AI (see syllabus below).
We encourage participants to explore the resources provided in the Resources section below.
November 2026
CAIO National Qualifier
All secondary school students are invited to participate in the CAIO national qualifier in November 2026.
Participants achieving top results will be recognized with official CAIO certificates in three levels of distinction: Distinguished Honor Roll (top 5%), Honor Roll (top 20%), and Certificate of Distinction (top 50%). The top 20 participates will be invited to join the CAIO National Training Camp.
Beginning of January 2027
CAIO National Training Camp
An invite-only, multi-day in-person training and selection program held in the second week of January 2027, the camp features expert-led lectures in AI and competition-style tests.
At the end of the camp, four gold, four silver, and four bronze medals will be awarded based on test performance. All others will receive the certificates of participation. The top four contestants will be selected to represent Canada at the 2027 IAIO.
2027
IAIO Main Contests
The IAIO 2027 will take place in Vietnam.
There will be two contests: the first contest will be paper-based, and the second one will be code-based.
The first contest focuses on solving math or algorithmic problems that test conceptual understanding and problem-solving skills. The second contest focuses on practical coding, with a practice session scheduled one day before the competition.
The specific schedule and timeline will be announced soon.
Syllabus
1. Data Preparation
Data Processing
Python Pandas
Python Numpy
Linear algebra fundamentals
Exploratory data analysis (EDA)
Data Visulization
Python Matpolitlib
Python Seaborn
2. Supervised Learning
Regression Models
Linear regression (Least Squares, Gradient Descent)
Polynomial regression
Classification Models
Decision Trees (entropy, information gain)
Support Vector Machines (SVM) and hyperplanes
Naïve Bayes classification
Regularization & Overfitting
L1 (Lasso) and L2 (Ridge) Regularization
Cross-validation techniques
3. Unsupervised Learning
Clustering
K-means clustering (cost function minimization)
Hierarchical clustering
Gaussian Mixture Models (GMM)
Dimensionality Reduction
PCA and its application in visualization
t-SNE for high-dimensional data representation
4. Reinforcement Learning
Markov Decision Processes (MDPs)
States, actions, rewards
Bellman Equations
Q-Learning
Temporal Difference Learning
Exploration vs. exploitation
5. AI Search
Uninformed Search
Breadth-First Search (BFS), Depth-First Search (DFS)
Uniform Cost Search
Informed Search
A* Algorithm and heuristics
Minimax search (game AI)
6. Logical Reasoning
Propositional & First-Order Logic
CNF, logical connectives
Unification in inference
Resolution Theorem Proving
Satisfiability and SAT solvers
7. Evaluation of ML Models
Classification Metrics
Precision, recall, F1-score
ROC-AUC curves
Bias-Variance Tradeoff
Overfitting detection
K-fold cross-validation
8. Constraint Satisfaction Problems
Backtracking & Constraint Propagation
AC-3 algorithm
Forward checking
9. Kernel Methods
Support Vector Machines (SVM)
Margin maximization
Soft-margin vs. hard-margin SVM
Kernel Trick
Transforming low-dimensional data to high-dimensional space
Radial Basis Function (RBF) kernel
10. Recommender Systems
Collaborative Filtering
User-based and item-based filtering
Similarity metrics (cosine similarity, Pearson correlation)
Content-Based Filtering
TF-IDF and word embeddings
Feature engineering for recommendation models
Hybrid Recommender Systems
Combining collaborative and content-based filtering
Matrix factorization (SVD, ALS algorithms)
Deep learning approaches (Neural Collaborative Filtering, Autoencoders for recommendations)
Resources
Here are some resources that will be useful when preparing for the CAIO contest.
We expect participants who do well in the contest to have some knowledge of the following areas.
