CJAIO 2026-27
CJAIO Participate
CJAIO participation is built for younger learners who want a clear, welcoming pathway into programming and AI-related problem solving.
Eligibility
CJAIO is open to students in Grade <= 10. The competition is suitable for students who have begun learning Python and are comfortable with logical reasoning, arithmetic, and basic problem-solving tasks.
Competition Format
The contest may include a mix of multiple-choice questions, short-answer reasoning tasks, and Python-based programming questions. Problems are designed to assess both conceptual understanding and practical coding ability.
- Python programming and debugging tasks.
- Data structure and algorithm problems appropriate for Grade <= 10.
- Data manipulation, interpretation, and visualization questions.
- Introductory AI and machine learning concepts.
- Mathematical foundations such as logic, patterns, functions, and basic probability.
Learning Goals
CJAIO helps students develop confidence with code, understand how data can be represented and analyzed, and build the habits of careful reasoning that support future study in AI and computer science.
Syllabus
1. Python Programming Fundamentals
Main Topics
Variables, data types, input/output, conditionals, loops, functions, debugging
2. Core Data Structures
Main Topics
Lists, strings, dictionaries, nested lists, stacks, queues
3. Algorithmic Thinking
Main Topics
Searching, sorting, counting, simulation, pattern recognition, simple recursion
4. Computational Problem Solving
Main Topics
Problem decomposition, pseudocode, code tracing, edge cases, testing, basic efficiency
5. Data Literacy and Processing
Main Topics
Tables, datasets, data cleaning, summary statistics, patterns and outliers
6. Data Visualization and Interpretation
Main Topics
Bar charts, line graphs, scatter plots, trends, comparisons, visual explanation
7. Math Foundations for AI
Main Topics
Logic, functions, coordinates, ratios, probability, basic statistics
8. Introductory Machine Learning
Main Topics
Features, labels, train/test split, classification, regression, prediction, accuracy
9. Basic Machine Learning Models with Python
Main Topics
Linear Regression, Logistic Regression, Decision Trees, KNN, Naive Bayes, K-Means
10. Responsible and Human-Centered AI
Main Topics
Bias, fairness, privacy, limitations, human oversight, responsible AI use
11. Integrated AI Problem Solving
Main Topics
Mixed problems combining Python, algorithms, data, math, ML, and AI reasoning