Basic Information
Competition Structure
CAIO 2026 is a two-stage competition consisting of the National Qualifier and the National Training Camp.
• The CAIO National Qualifier will be held in November 2025
• Selected students will be invited to the CAIO National Training Camp in January 2026
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 this page and the “Prepare” page.
Registeration Period
Registration is from July to October 30th 2025.
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 7–12), regardless of country, are eligible to participate.
• Students advancing to the CAIO National Training Camp must be enrolled full-time in Canadian secondary schools (Grades 7–12). Proof of enrollment will be required.
Timeline
November 23rd 2025
Register and Prepare
Registration is now open for the CAIO selection test, taking place in November 2025. The exam assesses programming and mathematical skills relevant to AI (see syllabus below).
We encourage participants to explore the resources provided in Recourses section below.
November 23rd 2025
CAIO National Qualifier
All secondary school students residing in Canada are invited to participate in the CAIO national qualifier on November 23, 2025 (subject to change).
Participates achieving top results will be recognized with official CAIO certificates in three levels of distinction: First Prize (top 5%), Second Prize (next 15%), and Third Prize (next 30%). The top 12 to 30 participates (subject to change based on total participants) will be invited to join the CAIO National Training Camp.
3rd-17th January 2026
CAIO National Training Camp
An invite-only, multi-day training and selection program held over three consecutive Saturdays in January 2026, 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 participates will be selected to represent Canada at the 2026 IAIO.
Early February 2026
“Home task” of IAIO
The selected team will have extra training sessions in February 2026 in preparation for the IAIO. Team members will be assigned a mentor from academia or industry.
The first round of the IAIO starts in early February 2026 with an at-home task. All team members will be expected to work on this before leaving Canada.
23rd-27th February 2026
IAIO Main Contests
The IAIO takes place in Ljubljana, Slovenia.
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.
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.
