Computational Thinking & Artificial Intelligence Class 6
Class 6 Syllabus - An official CBSE curriculum designed to build AI-ready learners through logical thinking, pattern recognition, and responsible use of technology.
This handbook is divided into two parts. Part 1 (Computational Thinking) is designed as a companion to the Class 6 Mathematics textbook and is used alongside regular classroom teaching. It introduces students to thinking-based questions involving decomposition, pattern recognition, abstraction, and algorithmic reasoning. Part 2 (Artificial Intelligence) provides a structured introduction to AI - how machines learn from data, recognise patterns, and make decisions - using simple explanations and real-life examples from healthcare, education, transport, and communication.
The National Education Policy 2020 specifically mandates the teaching of Computational Thinking and AI during school education, and the CBSE is implementing this curriculum from Classes 3–8 starting the 2026–27 session.
Breaking complex problems into smaller, manageable parts using numerical clues, shapes, grids, and multi-step conditions.
Identifying, extending, and justifying patterns in numbers, shapes, letters, and symbols - including cyclic and mixed rules.
Interpreting 3D objects, transformations, symmetry, mirror images, spatial relationships, and hidden visual structures.
Following multi-step procedures involving sequences, grid movement, swaps, ordering, and logical flow of instructions.
These chapters are aligned with the Class 6 Mathematics textbook and are taught alongside regular Maths lessons.
| # | Chapter | Key focus area |
|---|---|---|
| 1 | Introduction | Overview of computational thinking approach |
| 2 | How to use this book? | Pedagogy guide for teachers and students |
| 3 | Patterns in Mathematics | Number and shape pattern recognition |
| 4 | Lines and Angles | Spatial and visual reasoning |
| 5 | Number Play | Decomposition with numerical clues |
| 6 | Data Handling and Presentation | Data collection, organisation, and representation |
| 7 | Prime Time | Factors, multiples, and algorithmic patterns |
| 8 | Perimeter and Area | Visual reasoning and measurement logic |
| 9 | Fractions | Abstract thinking with proportional relationships |
| 10 | Playing with Constructions | Geometric abstraction and spatial thinking |
| 11 | Symmetry | Multiple axes, mirror and water image reasoning |
| 12 | The Other Side of Zero | Negative numbers and abstract number line thinking |
Each chapter includes foundational AI concepts, real-life examples, activities, and a focus on ethical and responsible use of technology.
| # | Chapter title | What students learn | Ethical focus |
|---|---|---|---|
| 1 | Introduction to AI and Everyday Examples | Meaning of AI, AI in daily life, automation, human vs machine intelligence, types of learning in AI (supervised, unsupervised, reinforcement) | Responsible usage |
| 2 | Basic Data Concepts | Understanding and types of data (text, numbers, images, sounds), collecting, organising, and representing data | Data handling care |
| 3 | Simple Pattern Recognition and Decision Making | Understanding and identifying patterns, drawing observations and conclusions, decision-making processes | Logical thinking |
| 4 | Ethics and Digital Responsibility | Responsible use of technology, online safety, privacy, password safety, digital footprints | Online behaviour |
Computational thinking skills - Decomposition, pattern recognition, data representation, generalisation, abstraction, and algorithms to solve problems.
Spatial and visual reasoning - Developing the ability to think in shapes, space, and visual structures.
AI foundations - Gaining foundational knowledge of AI, its types, and real-world domains of application.
AI ethics - Understanding key ethical terms such as bias and fairness in relation to how AI systems work.
Digital proficiency - Using computers and applications for data analysis, visual representation, and communication of ideas.
🧠 Understanding AI
- Summarise the basic ideas and concepts of AI and its everyday applications
- Describe key differences between machine intelligence and human intelligence
- Explain the difference between automation and AI using real-world examples
- Differentiate supervised, unsupervised, and reinforcement learning
📊 Data and patterns
- Organise and represent data in various forms - text, numbers, images, and sounds
- Recognise simple patterns in data and make decisions based on observations
- Apply introductory predictive techniques including regression, classification, and clustering
🔒 Digital safety and ethics
- Understand digital footprints, privacy, and responsible technology behaviour
- Practice internet safety - creating secure passwords and maintaining safe online behaviour
- Apply basic privacy measures while using digital and AI tools
🌎 Applying AI knowledge
- Apply conceptual AI knowledge to everyday activities
- Recognise human-centred design and ethical principles in AI systems
- Understand the role and impact of AI across sectors like healthcare, education, and agriculture
🧩 Experiential learning
Students engage with complex puzzles, riddles, and hands-on real-world problems. The emphasis is on thinking through challenges rather than memorising answers.
🤝 Collaborative work
Group discussions, debates, and collaborative projects help students solve multidisciplinary challenges together - building teamwork and communication alongside technical skills.
🔧 Project-based learning
Students use AI tools and data analysis to create solutions for community or fictional city challenges - two major interdisciplinary projects are part of the curriculum.
👀 Facilitative teaching
Teachers guide through prompts and discussions rather than providing direct answers. Creating a safe space where mistakes are welcomed and multiple strategies are explored is essential.
Assessment has shifted from rote memorisation to continuous, formative, and competency-based evaluation. There is no separate board exam for CT & AI in Classes 3–8.
📚 Performance tools
Project presentations, assignments, and reflective journals that capture thinking processes and learning growth.
📝 Practical evaluation
Written tests with CT puzzles, practical examinations, and interactive classroom activities that test application skills.
💬 Qualitative feedback
Teachers use clear rubrics and Observation Journals to track each student's development consistently across the year.
2026–27 - NOW ACTIVE 🟢
CT & AI curriculum launched for Classes 3–8 across all CBSE schools. Internally assessed; no board exam. Your child's Class 6 year.
2027–28 - Structured CT/AI modules for Classes 9–12
Expanded curriculum modules rolled out for higher classes, including structured AI learning for Class 9 students.
2029 - AI becomes a board-examined subject
Today's Class 6 students will be in Class 9. AI formally enters the CBSE board examination framework for Classes 9–10.
2031 - First Class 10 board exam under new scheme
The cohort beginning Class 6 in 2026–27 will sit their Class 10 boards under the fully reformed NEP 2020 framework.
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