CBSE Computational Thinking and AI Curriculum 2026–27 for Classes 3

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What This CBSE Curriculum on CT and AI Is About

The curriculum has one primary goal: build AI-ready learners by first developing strong Computational Thinking (CT) skills.

The logic is this — CT (decomposition, pattern recognition, abstraction, algorithmic thinking) is the same cognitive foundation that underlies how AI and ML systems work. Build CT first, then layer AI literacy on top.

Download the official curriculum PDF →

Two core definitions from the document:

Computational Thinking (CT): A structured approach to problem-solving that breaks larger problems into smaller, logical pieces, building precise, step-by-step solutions that either a person or a machine can follow.

Artificial Intelligence (AI): A broad collection of technologies that enable machines to carry out tasks typically associated with human intelligence — such as learning, comprehension, reasoning, problem-solving, and understanding natural language.

Structure at a Glance

Preparatory: Classes 3–5 -- 50 hours CT embedded in Math & TWAU

Middle: Classes 6–8 -- 100 hours Advanced CT + AI Literacy + Projects

Five Reasons CBSE Gives for This Curriculum

Preparing for the future — problem-solving, data use, pattern identification, ethical AI application
Holistic development — logical thinking, critical reasoning, creative problem-solving, ethical decision-making
Interdisciplinary relevance — connects Math, Science, Humanities, and Technology
Innovation and entrepreneurship — CT & AI are fundamentally about solving problems and creating new solutions
Ethical awareness — sensitising students to bias, fairness, and inclusivity in AI systems

Curricular Goals and Competencies

Classes 3–5

CG-1: Basic problem-solving with procedural fluency. Solve puzzles using visual representations; identify patterns in shapes, symbols, numbers

CG-2: Analytical thinking, verbal and visual reasoning. Systematically count permutations; select appropriate computation methods; connect concepts across representations

CG-3: Basic computer concepts. Parts of a computer, input/output, file management, internet safety, block-based coding (e.g. Scratch)

Classes 6–8

CG-1: CT skills — decomposition, pattern recognition, abstraction, algorithms. Programmatic thinking: iteration, symbolic representation, logical operations; arithmetic reasoning, algorithm correctness and efficiency

CG-2: Spatial and visual reasoning. Visualise, manipulate, and understand spatial relationships

CG-3: Foundational AI knowledge. Apply abstraction; demonstrate knowledge of AI tools through projects

CG-4: Ethics in AIIdentify ethical issues; apply ethical principles to AI usage decisions

CG-5: Computer proficiency. Use computers/devices for data analysis, visual representations, online research, and infographic design

Approach: Classes 3–5

CT integrated into existing Mathematics and The World Around Us (TWAU) subjects
Delivered via worksheets, resource books, and activity-based learning
Focus: logical thinking, pattern finding, ordering — through puzzles, math games, and exercises
Assessment: written CT questions, interactive group activities (e.g. treasure hunts), Teacher Observation Journal
Teachers: Subject teachers (Math/TWAU) using CT worksheets and handbooks

Approach: Classes 6–8

CT advanced to complex applications; AI literacy introduced as a new component
Three components: Advanced CT (40 hrs) + Introductory AI (20 hrs) + Interdisciplinary Projects (40 hrs)
CT worksheets aligned to Mathematics textbooks; AI taught through the AI Foundation Handbook
Projects integrate Math, Science, Social Studies, and English
Assessment: written tests, practical exams, thematic projects, reflective journals, group discussions
Teachers: Subject teachers for CT resources; Computer teachers for AI Literacy

Class-wise Syllabus

Classes 3–5 — CT Learning Outcomes

CT is embedded into Mathematics and TWAU. Each class has a dedicated resource book with CT-focused questions mapped to the textbook's table of contents.

Class 3

Abstract Thinking: Viewpoints of 3-D objects; shapes after flips/rotations; hidden parts in patterns
Pattern Recognition: Simple patterns with 1–2 changes in numbers, shapes, letters, or mixed sequences
Decomposition: Breaking problems with 2–3 clues — number names, 3-D objects, step-by-step transfers, tables
Algorithmic Thinking: Following step-by-step rules for number sequences, grid movements, before/after events, multi-step transfers

Class 4

Abstract Thinking: Moderate-to-complex problems — 3-D viewpoints, flips/cuts/rotations, mirror images, symmetry
Pattern Recognition: Patterns with one or more changes across numbers, shapes, letters, mixed
Decomposition: Cluster of moderate clues — place values, 3-D parts, money/quantity transfers, tables, counting/grouping conditions
Algorithmic Thinking: Elaborate conditions for number sequences, grid movements, step-wise value changes, people/events arranged by attributes

Class 5

Abstract Thinking: Complex multi-layered problems — 3-D viewpoints, clockwise/counter-clockwise rotations, mirror/water images, symmetry
Pattern Recognition: Progressive patterns with multiple simultaneous changes
Decomposition: Higher-order interconnected clues — place values, transfers, tables, visual representations with numerical values
Algorithmic Thinking: Multi-layered rules for sequences, grids, swaps, chronological ordering, counting

Classes 6–8 — Three-Component Structure

Class 6

CT — Learning Outcomes

Abstract Thinking: Advanced 3-D cross-sections; combined shape transformations; symmetry across multiple axes; scale and proportion reasoning
Pattern Recognition: Complex patterns — mixed operations, cyclic behaviour, alternation/skipping, dependency rules across numbers + shapes + letters
Decomposition: Interdependent clues — factors/multiples, 2-D/3-D shape properties, multi-step transfers with conditions, cross-referencing tables
Algorithmic Thinking: Multi-layered rules — combined operations, grid navigation with path constraints, multi-step swaps/rearrangements, logical flow with necessary vs redundant information

AI Syllabus — 20 Hours

1. Introduction to AI & Everyday Examples — what AI is; AI vs automation; human vs machine intelligence; supervised, unsupervised, reinforcement learning

2. Basic Data Concepts — data types (numbers, text, images, sound); organisation and representation using tables/charts

3. Simple Pattern Recognition & Decision Making — identifying patterns in data; making decisions from observations

4. Ethics and Digital Responsibility — online safety, privacy, passwords, ethical use, digital footprints

Class 7

CT — Learning Outcomes

Abstract Thinking: 3-D transformations including rotations, reflections, cross-sections, nets; compound transformations; symmetry, congruence, proportional reasoning
Pattern Recognition: Multi-rule numerical sequences; algebraic patterns with variables and functions; geometric growth patterns; integrated numbers + shapes + logical conditions
Decomposition: Number properties (factors, ratios, percentages, powers); spatial/geometry problems; tables/charts with multiple dependencies; translating visual info into structured data
Algorithmic Thinking: Rule-based sequences with conditional branching; grid pathfinding; if–then reasoning and elimination strategies; designing optimal procedural steps

AI Syllabus — 20 Hours

1. AI Domains — classification, regression, clustering; Computer Vision, NLP, Data Science; chatbots, image recognition, translation

2. AI in Industries — healthcare, education, transport, communication

3. Data Visualisation & Analysis — collecting structured data; bar charts, line graphs, pie charts; interpreting patterns

4. Ethics & AI Bias Awareness — what bias in AI is; responsible and fair use; digital citizenship5

Class 8

CT — Learning Outcomes

Abstract Thinking: Powers, factors, remainders, divisibility; generalisation across number systems (decimal, binary, Roman, Chinese numerals); spatial visualisation with overlaps and transformations; ignoring irrelevant data
Pattern Recognition: Powers and exponents; relationships across representations; geometric configurations; conditional patterns with rules and constraints
Decomposition: Separating conditions, constraints, and goals; multi-step distributions; breaking numerical expressions into simpler forms; problems with multiple variables
Algorithmic Thinking: Rule-based transformations of numbers/symbols; conditional instructions (if–then, either–or, must/must not); sequential decision-making; optimisation for maximum/minimum outcomes

AI Syllabus — 20 Hours

1. AI Project Lifecycle — Define Problem, Collect Data, Test AI Tools, Reflect and Improve; how AI learns from data

2. Deeper Dive into AI Applications — AI in environment, healthcare, automation, education; no-code AI tools (image classifiers, chatbots, data prediction apps)

3. Data and Fairness — how AI uses data; identifying bias in datasets; strategies for fairness and inclusivity

4. Ethics and Responsible AI — privacy, misinformation, social impact; responsible AI use; reflection on real-world challenges

Pedagogy

Classes 3–5: Hands-on activities, games, puzzles; visual interpretation; breaking problems into parts; collaborative tasks and peer discussions

Classes 6–8: "Complex puzzles and riddles; AI demonstrations and hands-on experiences; group projects integrating CT & AI; independent data collection and analysis; debates on ethical AI use

Assessment

Classes 3–5: Written CT tests; interactive group activities; Teacher Observation Journal

Classes 6–8: "Written tests; practical exams; thematic projects; reflective journals; group discussions; Teacher Observation Journal

The curriculum explicitly moves away from rote assessment. Focus is on ability to apply knowledge, assess creativity, and evaluate ethical reasoning.

CBSE Official Resources

For Teachers

Download grade-wise Teacher Handbooks directly from CBSE:

Class 3 Download →

Class 4 Download →

‍Class 5 Download →
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Class 6 Download →
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Class 7 Download →
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‍Class 8 Download →

For Students

Download grade-wise Student Handbooks directly from CBSE:

All resources are also listed on the official CBSE CT & AI page: cbseacademic.nic.in/ct-ai.html

Computational Thinking Workbooks (Classes 3–8)

The CBSE curriculum recommends workbook-based practice — class-wise, chapter-aligned, with a progression from basic CT to advanced problem-solving.

We've built exactly that.

Computational Thinking Workbooks for Classes 3–8 →

Our class-wise CT workbooks are aligned to the CBSE curriculum framework, covering decomposition, pattern recognition, abstraction, and algorithmic thinking.