Curriculum alignment · India

CBSE AI and computational-thinking curricula

We checked the LittleAIMaster Grade 1–12 AI course against all 46 rated requirements of this framework: 35 fully covered, 7 mostly covered, 4 partly covered. Open a requirement to see what learners do and the exact lessons.

91%
Fully covered 35Mostly covered 7Partly covered 4of 46 requirements15 taught earlier than the framework asks

Source documents (7)

Computational Thinking and Artificial Intelligence (CT&AI) Curriculum, Classes 3-8
CBSE (Central Board of Secondary Education), Department of Skill Education, with NCERT input, aligned to NCF-SE 2023 / NEP 2020
Session 2026-27 (new curriculum, first implementation)
Session 2026-2027
Session 2026-2027
Session 2025-2026 (most recent primary PDF located; used as closest-current source for Class XI)
Session 2026-2027 (current official curriculum PDF)
Employability Skills (Part A, common across all CBSE Skill Subjects incl. AI 417/843), Classes IX-XII
CBSE, Department of Skill Education (units common to Skill Education subjects; detailed content published separately on cbseacademic.nic.in)
Session 2026-27 unit lists (as printed inside the 417/843 curriculum documents); detailed sub-unit content downloaded separately from CBSE website per those documents
National Education Policy (NEP) 2020 - curricular integration of AI
Ministry of Education, Government of India
2020

4 of 46 requirements are worded from secondary summaries or could not be checked against the primary document; they are marked.

Alignment chart

Every CBSE requirement, rated

5Taught at this grade5Some material5Grades the framework asks for
Open a requirement to see what learners do and the exact lessons.

Showing 46 of 46 requirements

CT&AI Classes 3-8

Abstract Thinking (CT)

Solve problems with hidden, unseen, or partially visible ideas using different viewpoints of 3-D objects, shape transformations (flips/turns/folds/rotations), and identification of missing/hidden parts in incomplete shapes or patterns, progressing to symmetry and mirror-image reasoning by Grade 5.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 4 'Make a Model', learners create a purpose-specific map, explain what they left out, model the states of a bell and adapt its rule to a feeder.

Pattern Recognition (CT)

Identify and, by Grade 5, extend progressive patterns involving one or more changes, using numbers, shapes/images, letters, or a mix.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Patterns and Steps', learners continue shape, size, letter and number patterns that follow two rules at once, explain the rule and make a three-part pattern.

Decomposition (CT)

Break down problems into 2-3 or more clues (numbers, 3-D object parts, step-by-step exchanges/transfers, tables/charts with multiple data points), progressing to conditions for counting/grouping/sorting by Grade 5.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Break It Up Yourself', learners split team preparation into workable jobs. In Grade 4 'Thinking Tools, Real Names', they refine a water task and check that its output meets the whole team's need.

Algorithmic Thinking (CT)

Follow clear, then multi-layered, step-by-step rules to solve problems involving number sequences, grid movements/paths, before/after/in-between event ordering, and multi-step instructions (moves, changes, transfers, swaps).

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Break It Up Yourself', learners develop an everyday job sequence and explain why different orders can be valid. In 'Byte on the Grid' they predict and run a command route.

Digital literacy

Develop familiarity with parts of a computer, input/output devices, file management, internet safety, educational software, and block-based coding (e.g. Scratch) (CG-3, Classes 3-5).

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Pick the Right Chart' and Grade 5 'My Digital Toolkit', learners find an attributed fact, name, store and retrieve versions, combine real project counts in a poster, and revise and present it.

Abstract Thinking / Pattern Recognition / Decomposition / Algorithmic Thinking (advanced CT)

Interpret and solve multi-step, multi-layered problems using advanced 3-D transformations, algebraic/multi-rule numerical patterns, interdependent-clue decomposition, and rule-based algorithms with conditional branching - increasing in complexity from Class 6 through Class 8 (e.g., Class 8 adds number-system generalisation across decimal/binary/ternary/Roman/Chinese numerals, and optimisation problems).

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

Learners solve multi-rule problems in Grade 7 'The Sudoku Champion' (interdependent clues), 'The Delivery Challenge' (route optimisation) and 'If-Then-Else Logic', and in Grade 6 'How Computers Count - Binary' and Grade 7 'Colors for Computers' work with binary and base-16.

AI awareness - foundations

Summarise basic ideas and concepts of AI and its applications; describe key differences between machine intelligence and human intelligence; explain the difference between automation and AI using real-world cases; differentiate supervised, unsupervised, and reinforcement learning (Class 6).

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'What is AI?' and 'AI in Your Home', learners define AI and sort real gadgets as AI or not AI, using three levels, five clues and rule-based versus learning-based tests, giving reasons.

Data literacy

Organise and represent data in various forms (text, numbers, images, sounds); recognise simple patterns in data and make decisions based on observations (Class 6); collect/organise structured data and create bar/line/pie charts (Class 7).

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'What is Data?' and Grade 8 'The Phone Book Problem' and 'The Living Dictionary', learners collect and type data, then store, retrieve and modify it in lists, dictionaries and nested structures in Python.

AI ethics and digital citizenship

Demonstrate understanding of ethics and digital responsibility (digital footprints, privacy, responsible technology use), practice internet safety protocols, and explain what bias in AI means and identify situations where AI can give unfair results (Class 6-7); recognise how bias leads to unfair conclusions and understand accountability, privacy, and fairness/inclusivity strategies (Class 8).

We start Grade 5 · framework Grade 6Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'AI and Privacy', 'Being a Good Digital Citizen' and 'AI Can Be Unfair', learners judge which digital footprint traces can be erased, apply a permission need-test, and see bias as unfair results scored group by group.

AI domains and predictive techniques

Distinguish key predictive techniques - regression, classification, clustering - and explain key AI domains: Data Science, Computer Vision, Natural Language Processing; explain uses of AI in healthcare, education, transport, communication (Class 7).

Framework grades 6–8
Mostly covered
Our Grades 6–8 Mostly covered
What learners do

In Grade 7 'How AI Sees' learners judge applications as computer vision or not; in 'Unit 4 Review' they spot language AI. Grade 8 'The AI Toolkit' matches computer vision and language models to what they do.

Still to add

Data science is never named as an AI domain beside computer vision and NLP, and no activity sorts applications into all three domains.

AI project cycle and applied AI

Describe the stages of the AI project cycle (Define Problem, Collect Data, Test AI Tools, Reflect and Improve); apply no-code tools to tackle real-world problems and reflect on their utility/effectiveness; analyse contributions of AI to fields like healthcare, automation, and education, understanding both benefits and risks; describe AI ethics as values/guidelines for responsible AI (Class 8).

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 8 'The Wrong Solution' and 'Capstone Project', learners scope WHO, WHAT, WHY and HOW MUCH and order the ask, collect, label, train, test, explain and deploy steps, including stakeholder interviews and data sourcing.

AI Code 417, Class IX

AI foundations and domains

Identify and appreciate AI and describe its applications in daily life; relate, apply and reflect on Human-Machine Interaction across the three domains of AI - Data, Computer Vision, and Natural Language Processing; undergo assessment for AI-readiness skills (Unit 1: AI Reflection, Project Cycle and Ethics).

Framework grades 9–10
Partly covered
Our Grades 9–10 Partly covered
What learners do

Grade 10 teaches computer vision and text/NLP as separate units. Learners sort network types into mainly images or mainly text, and match computer vision tasks to industries.

Still to add

Grades 9-10 do not present data science, computer vision and NLP together as the main AI domains or have learners match applications to them.

AI Project Cycle

Identify the AI Project Cycle framework (Problem Scoping, Data Acquisition, Data Exploration, Modeling, Evaluation, Deployment); learn problem scoping and goal-setting for an AI project; identify stakeholders; brainstorm ethical issues involved in the problem selected; identify data requirements and reliable sources.

Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 9 'The Failed Project' and Grade 10 'Spark of Inspiration' and 'Deployment Basics', learners order the six-stage lifecycle, scope a problem, acquire and explore data, choose and evaluate a model, and deploy and monitor it.

Modeling, evaluation, deployment, ethics

Understand types of modeling (rule-based and learning-based); understand evaluation terms (true/false positive/negative); apply knowledge of deployment in future AI projects; reflect on ethical issues around AI (Moral Machine activity); gain awareness of AI bias and AI access; analyse advantages and disadvantages of AI.

Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 9 'Model Evaluation Basics', 'Accuracy and Errors' and 'Confusion Matrix', learners use a train/test split, the accuracy, precision, recall and F1 formulas and confusion matrices, and consider the user cost of each error type.

Data literacy

Define data literacy and recognise its importance for informed decision-making and critical thinking; apply the Data Literacy Process Framework; differentiate Data Privacy vs Security; identify data breach risks and best practices for cyber security; determine best methods to acquire, classify, and interpret data (Unit 2: Data Literacy).

Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 9 'The Right Question', 'The Data Hunter' and 'Graphs Tell Stories', learners decide what data is needed, acquire it ethically, then collect, clean, visualise, analyse and act to support a decision.

Data visualization

Recognise the importance of data visualisation and discover different visualisation methods; build an interactive dashboard and presentation (Unit 2 sub-topic: Project Interactive Data Dashboard).

Framework grades 9–10
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 9 'Graphs Tell Stories', 'Bar Charts and Histograms' and 'A Picture Worth 1000 Rows', learners choose among chart types, build line, scatter and histogram charts and a topic-filtered four-panel dashboard.

Still to add

Data and chart choices are largely prescribed or synthetic; independently chosen charts for a real collected pattern are limited.

Where it is taught

Math for AI (Statistics & Probability)

Analyse data (numbers/images), find patterns/relations, and understand the role of Math (statistics, linear algebra, probability, calculus) in AI; understand statistics and probability concepts and apply them to real-life scenarios such as disaster management, sports, disease prediction, weather forecast, traffic estimation (Unit 3: Math for AI).

Framework grades 9–10
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

Learners compute correlation and run a permutation hypothesis test on real Iris measurements, and work out descriptive group statistics on real Titanic records, in Python Lab Module 33 and Module 30 and in Grade 9 lessons such as 'Spread and Variance'.

Still to add

Matrix and vector operations and regression on a real dataset are not shown together in the tasks reviewed for these grades.

Generative AI

Define Generative AI and classify different kinds; explain how Generative AI works and how it learns, differentiate from conventional AI; apply Generative AI tools to create content; understand ethical considerations of using Generative AI (Unit 4: Introduction to Generative AI).

We start Grade 7 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 7 'AI Understands Words', Grade 8 'Maya's Discovery' and Grade 10 'Transformer Preview', learners contrast generative with sorting AI, and study diffusion and text-to-image, and Transformers and generation.

Introductory programming

Acquire introductory Python programming skills: variables, arithmetic/logical/comparison/assignment operators, data types, print()/input(), conditional and iterative statements (if/for/while), and lists, with hands-on practical exercises (Unit 5: Introduction to Python; Part C practical exam).

Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Python Lab Module 6 and later modules such as 47 and 48, learners write functions with parameters and return values, plus loops and conditionals, with automatic checks. In Grade 10 they write a report function from scratch.

Note

No Grade 9 chapter lesson links to the Module 47 or 48 tasks, and the one chapter-linked task is self-checked.

Applied project (SDG-linked)

Complete a Part D project/field visit/student portfolio related to Sustainable Development Goals, e.g. building an AI model with Teachable Machine or Machine Learning for Kids, or maintaining a portfolio of AI activities (4Ws canvas, system map, research on AI for SDGs).

We start Grade 5 · framework Grade 9Framework grades 9–10
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 8 'Global Goals', learners research a local problem, train a mini Bin Buddy in Raise Your AI, plan evidence that counts harms, and pitch an SDG project. In Grade 5 they weigh impact beyond the score and choose keep, change or stop.

Still to add

Learners do not yet research a local problem with their own evidence or judge the impact of their own solution.

Where it is taught

AI Code 417, Class X

Revisiting AI Project Cycle & ethical frameworks

Understand the stages of the AI Project Cycle and the three AI domains with illustrations of practical applications; learn about ethical frameworks for AI and their categories, including Bioethics as a framework used in healthcare (Unit 1).

Framework grades 9–10
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 10 'Building Your AI Ethics Framework' and Grade 9 'The Biased Hiring Model', learners place a hiring AI in EU AI Act risk tiers and name the missing UNESCO principle, then write principles their own project meets.

Still to add

Applications to real cases are fixed-answer with pre-written feedback; learners never write a justified judgement on a real case.

Where it is taught

Advanced modeling concepts

Differentiate AI, ML, and DL and common data terminologies; familiarise with supervised, unsupervised, and reinforcement learning-based approaches; understand subcategories (classification, regression, clustering, association, ANN, CNN); understand neural networks and how AI makes a decision (Unit 2: Advanced Concepts of Modeling in AI).

Framework grades 9–10
Partly covered
Our Grades 9–10 Partly covered
What learners do

In Grade 10 'Welcome to Grade 10!', learners compare traditional machine learning with deep learning and sort algorithms into each. Grade 9 'Three Ways to Build AI' shows machine learning as one of three AI families.

Still to add

The nesting of AI, machine learning and deep learning is never stated or drawn; deep learning is always shown beside traditional machine learning.

Where it is taught

Evaluating models

Understand the role of evaluation in AI systems; understand train-test split for evaluating a machine learning algorithm's performance; understand accuracy and error; learn evaluation metrics for classification (accuracy, precision, recall, F1 score) and build a confusion matrix; understand ethical concerns around model evaluation (bias, transparency) (Unit 3: Evaluating Models).

Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 9 'Model Evaluation Basics', 'Accuracy and Errors' and 'Confusion Matrix', learners use a train/test split, the accuracy, precision, recall and F1 formulas and confusion matrices, and consider the user cost of each error type.

Statistical data / no-code AI

Define Statistical Data and its applications; define No-Code and Low-Code AI and identify differences from coded AI; use a no-code tool (Orange Data Mining) to perform data exploration, modeling, and evaluation, relating stages to the AI project cycle (Unit 4: Statistical Data, assessed via practicals only).

We start Grade 7 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 8 'Maya's Discovery', Grade 7 'Aisha's Big Idea' and Grade 9 'The ML Toolkit', learners use Teachable Machine and Scratch AI extensions to train, test and deploy, and contrast ready-made with custom-trained tools.

Computer Vision

Understand and visualize the computer's ability to identify alphabets and handwriting; hands-on Computer Vision unit content (Unit 5: Computer Vision, 20 practical hours).

Framework grades 9–10· secondary source
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Introduction to CNNs', 'Object Detection Basics' and 'Face Recognition', learners study image acquisition, preprocessing, convolutional feature extraction and analysis, plus classification, detection and face recognition.

Natural Language Processing

Study Natural Language Processing concepts and applications as Unit 6 of the Class X curriculum (20 theory + 7 practical hours); exact sub-unit learning outcomes not extracted from the source PDF pages read.

We start Grade 8 · framework Grade 9Framework grades 9–10· secondary source
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 9 'The Spam Invasion' and Grade 10 'Unit 4 Review', learners cover speech recognition, translation, summarisation, sentiment, entity recognition and spam filtering, and build a Python chatbot in Grade 8 'Inside a Chatbot'.

Advanced Python

Extend Python programming skills as Unit 7 (Advance Python, 10 practical hours), building on Class IX Python basics; exact sub-unit content not extracted from the source PDF pages read.

Framework grades 9–10· secondary source
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Python Lab Modules 29 to 34 and Grade 9 'Loading Data', learners load files with pandas, analyse arrays and tables, plot with Matplotlib and fit and evaluate a scikit-learn model.

Social-impact AI project

Empower students to create positive change through AI-driven social impact projects, and equip students with skills to develop AI solutions addressing societal challenges (course objectives).

We start Grade 5 · framework Grade 9Framework grades 9–10
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 8 'Global Goals', learners research a local problem, train a mini Bin Buddy in Raise Your AI, plan evidence that counts harms, and pitch an SDG project. In Grade 5 they weigh impact beyond the score and choose keep, change or stop.

Still to add

Learners do not yet research a local problem with their own evidence or judge the impact of their own solution.

Where it is taught

AI Code 843, Class XI

Introduction to AI for everyone

Communicate effectively about AI concepts and applications; describe the historical development of AI; differentiate types and domains of AI including their applications; recognise key terminologies/concepts related to machine learning and deep learning; formulate informed opinions on benefits and limitations of AI (Unit 1).

We start Grade 6 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

Grade 6 chapters cover the Turing test, Dartmouth 1956, AI winters and deep learning. Grade 11 'The Grand Finale!' adds ordering Transformer, generative AI and reinforcement learning milestones.

Careers in AI

Articulate the demand for AI professionals and diverse career opportunities; identify requisite skills and tools for AI careers; understand roles/responsibilities of AI professionals across industries; evaluate own interests/skills to determine career pathways in AI (Unit 2: Unlocking your Future in AI).

Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 11 'AI Careers' and Grade 12 'Your AI Path', learners match machine-learning engineer, data engineer, ethics specialist and product manager roles to their day-to-day work and skills, and assess themselves against their interests.

Python programming

Explain basics of Python programming language and write programs with tokens; use selective and iterative statements effectively; gain practical knowledge of libraries (NumPy, Pandas, Scikit-learn) for efficient use (Unit 3: Python Programming, Levels 1-2).

We start Grade 9 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 9 lessons such as 'Math in Python', 'Spread and Variance' and 'Scatter Plots', learners use NumPy statistics, pandas loading and cleaning, matplotlib plots and scikit-learn fit and predict on files.

Where it is taught

Capstone project methodology

Decompose any problem using the 5W1H method; apply Design Thinking methodology; create empathy maps; align problems to Sustainable Development Goals; express a solution to a problem in non-technical words (Unit 4: Introduction to Capstone Project).

We start Grade 8 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

Learners work through six-step design thinking in Grade 8 'Designing AI Solutions', with empathy and problem statements. Specification-driven scoping with user interviews and personas follows at Grade 12.

Where it is taught

Data literacy and analysis

Explain the importance of data literacy in AI; identify data collection methods and applications; comprehend matrices and their operations; apply basic data analysis techniques (mean, median, mode, standard deviation, variance); visualise data using line/bar/histogram/scatter/pie charts with matplotlib (Unit 5: Data Literacy - Data Collection to Data Analysis).

Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'Designing Great Experiments' and 'Research Journey Complete', learners compute p-values by hand and read them correctly. In Grade 11 they work matrices and vectors by hand, and base-rate probability appears. The numbers are invented, not real data.

Machine Learning algorithms

Differentiate types of machine learning methods (supervised: regression, classification/k-NN; unsupervised: clustering/k-means); understand the concept behind each method; apply methods to develop solutions for day-to-day situations, e.g. Pearson correlation and linear regression in MS Excel/Python (Unit 6: Machine Learning Algorithms).

Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 11 'AI in Retail', learners run k-means on shoppers and pick k by the elbow. Python Lab Modules 46 and 49 and 'AI in Healthcare' apply SVMs, DBSCAN and other methods, and Grade 12 uses nearest-neighbour matching.

Natural Language Processing (linguistics)

Develop understanding of the complexities of human language and challenges in NLP tasks (emotion detection, sentiment analysis, classification, chatbots); learn techniques/algorithms for NLP tasks and phases of NLP; create a chatbot using a conversational platform (Unit 7: Leveraging Linguistics and Computer Science).

We start Grade 8 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 8 chapters such as 'Inside a Chatbot' and 'The Smart Assistant', learners build a rule-based chatbot in Python over twelve chapters. Grades 11-12 explain token-by-token generation and chatbot architecture.

AI ethics and values

Demonstrate understanding of the fundamental principles of ethics in AI and the five pillars of AI Ethics; understand AI bias, its sources, real-world implications, and mitigation strategies; identify and apply strategies to promote fairness and transparency; recognise the significance of AI policies in promoting responsible, safe, ethical AI use (Unit 8: AI Ethics and Values).

Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The Council Decision' and 'The Ethics Question', learners apply consequentialist, deontological, virtue and care frameworks to a lethal-drone dilemma, and apply FATE and IRB frameworks to cases.

AI Code 843, Class XII

Advanced Python (Level II)

Apply fundamental concepts of NumPy and Pandas libraries for data manipulation and analysis; import/export data between CSV files and DataFrames; handle missing values; apply the linear regression algorithm (advanced learners) (Unit 1: Python Programming-II, practicals only).

We start Grade 9 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 9 lessons such as 'Math in Python', 'Spread and Variance' and 'Scatter Plots', learners use NumPy statistics, pandas loading and cleaning, matplotlib plots and scikit-learn fit and predict on files.

Where it is taught

Data Science Methodology / capstone evaluation

Integrate Data Science Methodology steps into the Capstone Project; identify the best way to represent a solution to a problem; understand the importance of validating machine learning models; use key evaluation metrics (MSE, RMSE, Precision, Recall, F1, Accuracy) for various machine learning tasks (Unit 2).

We start Grade 9 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 11 'Testing & Evaluation' and Grade 9 chapters such as 'Confusion Matrix', learners study precision, recall, F1, the confusion matrix and train/test splits, using priority scenarios and honest reporting of what the numbers mean.

Where it is taught

Computer Vision (Making Machines See)

Explain computer vision and its significance in visual data analysis; understand key stages (acquisition, preprocessing, feature extraction, analysis); identify real-world applications in healthcare, surveillance, autonomous vehicles; analyse challenges (ethics, privacy, technical limitations); develop basic skills using OpenCV and deploying ML models online (Unit 3).

We start Grade 7 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 7 chapters such as 'How AI Sees' and 'Object Recognition', learners study the capture, preprocess, extract and recognise pipeline. Grade 10 'Introduction to CNNs' adds detection, segmentation and face recognition.

Where it is taught

No-code AI tooling

Develop proficiency using the Orange Data Mining tool - navigate its interface, employ its features, execute data analysis tasks; demonstrate ability to apply Orange across AI domains including data science, computer vision, and NLP through hands-on projects (Unit 4: AI with Orange Data Mining Tool, practicals only).

Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 11 'Finding Your Data', learners explore the iris data with a data table and scatter plot in a block-based tool, score a tree and k-NN with cross-validation, repeat the task in Python Lab, and explain what the blocks hid.

Big Data and Data Analytics

Understand Big Data, its types, advantages and disadvantages, and characteristics; explain the concept and significance of Big Data Analytics; analyse future trends; understand mining data streams (Unit 5).

Framework grades 11–12
Partly covered
Our Grades 11–12 Partly covered
What learners do

In Grade 11 'The 87% That Never Launch', learners meet a definition of Big Data by volume, velocity and variety, a vocabulary card and one recall question. Grade 11 chapters on storage and pipelines mention petabyte-scale storage.

Still to add

Big-data analytics such as distributed processing and batch versus stream is not taught, and large-scale uses across sectors are not surveyed.

Where it is taught

Neural Networks

Explain the basic structure and components of a neural network; identify different types of neural networks and their applications; understand machine learning and neural networks through hands-on projects (e.g. TensorFlow Celsius-to-Fahrenheit model, TensorFlow Playground classification) (Unit 6: Understanding Neural Networks).

We start Grade 10 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 10 chapters such as '86 Billion Neurons!' and 'The Secret Ingredients!', learners study a neuron as a weighted sum plus activation, layer roles, activation functions and parameter counting.

Where it is taught

Generative AI

Explain how Generative AI works; differentiate Generative AI and Discriminative AI and identify use cases; explore ethical, social, and legal concerns; gain hands-on experience with AI tools to generate creative/analytical outputs (images, text, video); use an LLM API to design and deploy a functional chatbot (Unit 7: Generative AI).

Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 11 'The Creative Revolution' and 'Word by Word' and Grade 12 'Art Partner', learners contrast generative with discriminative AI, and study training on large datasets and text, image and audio generation through sorting and quizzes.

Data Storytelling

Understand the benefits and elements of storytelling; appreciate the role of data storytelling in data analysis/science/AI; learn to combine data, visuals, and narrative to present complex information effectively using the Narrative Structure of a Data Story (Freytag's Pyramid); create an effective data story from given data, and understand ethics in data storytelling (Unit 8).

We start Grade 9 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

Grade 9 projects such as 'The Failed Project', 'Science Fair Star' and 'Portfolio Opens Doors' teach storytelling structure, figures and tables, audience adaptation and honest reporting, and apply them to project communication.

Where it is taught

Capstone project (culminating)

Complete a group Capstone Project (3-5 students) aligned with an SDG, following the design-thinking process (define problem, understand users, brainstorm/design solution, identify and collect data, build prototype, test solution, reflect individually and as a team, create a 3-minute video pitch); produce project documentation per the CBSE-IBM AI Project Cookbook (Part C).

Framework grades 11–12
Mostly covered
Our Grades 11–12 Mostly covered
What learners do

In Grade 11 'SDG Research-and-Build Project', learners link a goal to a local problem, vet open data, set a baseline, run and change a canteen forecast, estimate impact and footprint, consider harms and pitch their own project.

Still to add

The learner's own SDG project stops at a written pitch; impact is measured only on supplied canteen data, and no capstone or rubric covers it.

Where it is taught

Employability Skills (Part A), Classes IX-XII

ICT Skills

Complete Unit 3: Information and Communication Technology Skills (Level I-IV progressing Class IX to XII), covering topics such as email introduction and account creation, and networking basics at Class IX per secondary sources.

Framework grades 9–10· secondary source
Partly covered
Our Grades 9–10 Partly covered
What learners do

In Grade 9 'The Right Question' and Grade 10 'Presentation and Demo', learners use an AI chat tool on their own textbook paragraph, write a project Gantt chart, plan a goal with AI, and record and self-score a 3-minute demo.

Still to add

Learners do not practise finding information with search tools, managing information across subjects, or keeping their tool knowledge current; those are only told.

School and system rules (not rated)

These apply to schools and education systems (hours, delivery, assessment, staffing, age rules). A course cannot meet them on its own.

  • Overall curriculum aim: develop 'AI-Ready learners' through CT skills (logical thinking, problem-solving, pattern recognition) and understanding of the role and use of AI in daily life, building strong foundations in CT, digital literacy, and responsible use of technology.
  • Complete IBM SkillsBuild (or another industry) certification; complete a Capstone Project (Part C: 12 hours); participate in a bootcamp/internship/other startup exposure; maintain a practical file with at least one activity per unit.
  • Complete Unit 1: Communication Skills (Level I in Class IX, progressing through Levels II-IV by Class XII) - the exact sub-unit content is published as a separate detailed curriculum downloadable from the CBSE website; this row records the unit's existence and progression, not its detailed learning outcomes.
  • Complete Unit 2: Self-Management Skills (Level I-IV progressing Class IX to XII); detailed sub-unit content published separately by CBSE (this framework's own curriculum note directs teachers to download it from the CBSE website).
  • Complete Unit 4: Entrepreneurial Skills (Level I-IV), covering what entrepreneurship is, its role, qualities of entrepreneurs, types of business activities, and the entrepreneurship development process per secondary sources describing the Class IX content.
  • Complete Unit 5: Green Skills (Level I-IV), covering society and environment, conserving natural resources, and sustainable development per secondary sources describing the Class IX content.
  • NEP 2020 calls for concerted curricular and pedagogical initiatives, including the introduction of contemporary subjects such as Artificial Intelligence and Design Thinking at relevant stages, to develop skills and capacities in students at all levels (under 'Curricular Integration of Essential Subjects, Skills, and Capacities').
  • The National Education Policy aims for India to emerge as a global leader in emerging knowledge domains (AI, machine learning, data analytics, 3-D machining) and suggests teaching Computational Thinking alongside Mathematics as groundwork, with NCF-SE 2023 carrying this recommendation into an implementable curriculum that positions CT as the foundation needed for learning AI.

Bring the course to your school or region

Schools, school groups, resellers and partners: we will walk you through the alignment for your board and the lessons behind it.

For schoolsFor partners and resellersContact us

See how the course maps to the other 13 curricula on the curriculum alignment overview, or browse the course grade by grade.

Our own reading of our own course, ratings reviewed 22 October 2026. It is not an endorsement, certification or accreditation by any authority named here, and it makes no claim about learning results.