Syllabus

CT and AI ·

Building Asia's CT & AI Innovation

The AI Olympiad is more than an exam. We prepare students with structured learning pathways in Computational Thinking (CT) and Artificial Intelligence (AI) — aligned with Asia's NEP 2020 goals and CBSE CT & AI curriculum for middle school, then progressing into programming, machine learning, and applied AI for senior grades.

Computational Thinking first

Students learn how to break problems down, spot patterns, design algorithms, and reason with data — the foundation behind every AI system.

AI with ethics

From everyday AI examples to fairness, bias, and responsible innovation — ethics is part of the syllabus, not an afterthought.

Two learning tracks

Grades 5–8 build CT through mathematics and age-appropriate AI. Grades 9–12 advance into Python, ML, deep learning, and projects.

For Grades 5–8

Junior School Pathway

Built on the CBSE Computational Thinking and Artificial Intelligence student handbooks (2026–27) for Grades 5, 6, 7, and 8. Students get a unified interactive course — not four separate grade silos — organised by theme so every learner progresses through foundations, math-powered CT, and everyday AI.

How the Grades 5–8 course is structured

On the platform, learning follows a clear hierarchy: Course → Sections → Chapters → Lessons. Each chapter includes interactive HTML lessons (about 12–18 minutes each), optional hands-on Colab activities, and a chapter quiz (typically 10 MCQs). Progress unlocks as students complete lessons and quizzes.

Grade 5 focus: CT through mathematics companion chapters (patterns, fractions, shapes, data, maps).
Grades 6–8 focus: Deeper CT math + dedicated AI chapters (data, domains, industries, fairness, project lifecycle).
Shared foundations: All grades start with the same CT pillars, NEP alignment, and platform orientation.

Core course sections (Grades 5–8)

Foundations — CT & AI

What CT and AI are, why they matter under NEP 2020, and how to use the course platform.

Number Sense, Patterns & Logic

Place value, patterns, primes, divisibility, and logic puzzles that train algorithmic thinking.

Fractions, Decimals & Ratios

Equal parts, decimals, proportional reasoning, and distribution puzzles.

Geometry, Symmetry & Measurement

Angles, shapes, constructions, distance, weight, time, and spatial reasoning.

Data, Maps & Visual Problem Solving

Pictographs, data handling, maps, area puzzles, and optimisation challenges.

AI in Everyday Life

Everyday AI, data concepts, pattern recognition, ethics, domains, and industries.

An optional Ultimate add-on extends Grades 5–8 with advanced number systems & algebra, data science & AI ethics, and AI projects with responsible innovation.

For Grades 9–12

Senior School Pathway

For high-school students, the pathway shifts from CT foundations into modern programming, mathematical prerequisites, machine learning, deep learning, computer vision, NLP, generative AI, and applied project design — matching the Comprehensive AI & Programming Syllabus used for Olympiad preparation.

How the Grades 9–12 course is structured

Senior learners follow the same platform format — sections → chapters → lessons → quizzes — with longer, more technical lessons, Python coding in Colab notebooks, and progressively harder assessments. The Core track covers essential AI & programming; Ultimate Preparation unlocks advanced depth for competitive readiness.

Core syllabus sections (Grades 9–12)

Modern Programming with Python

Syntax, data types, operators, lists, tuples, strings, and practical coding habits.

Math for Learning AI

Linear algebra, probability, statistics, and the math that demystifies how models learn.

Machine Learning Fundamentals

Supervised & unsupervised learning, evaluation, and classical ML algorithms.

Introduction to Deep Learning

Neural networks, loss, optimisation, and training workflows.

Computer Vision

Images as data, CNNs, augmentation, and vision applications.

Natural Language Processing

Text representation, language models, and NLP pipelines.

Generative AI & LLMs

Large language models, prompting, and generative applications.

Applied AI, Ethics & Projects

Real-world project design, evaluation, and responsible AI practice.