Curriculum alignment · India

CISCE AI and robotics syllabuses and Indian state AI programmes

We checked the LittleAIMaster Grade 1–12 AI course against all 63 rated requirements of this framework: 50 fully covered, 5 mostly covered, 8 partly covered. Open a requirement to see what learners do and the exact lessons.

87%
Fully covered 50Mostly covered 5Partly covered 8of 63 requirements36 taught earlier than the framework asks

Source documents (4)

Robotics and Artificial Intelligence (Subject Code 66) — ICSE
Council for the Indian School Certificate Examinations (CISCE)
Official syllabus PDF dated 2024 (revised copy hosted March 2025), for use through the 2025-26 exam cycles; subject introduced from the 2023 academic year with first ICSE exams in 2025
Artificial Intelligence (883) — ISC
Council for the Indian School Certificate Examinations (CISCE)
Official syllabus for ISC Examination Year 2027 (dated February 2025); may be taken with Computer Science but not with Robotics
AI-integrated ICT curriculum (KITE / Samagra, Classes 7-10)
Kerala Infrastructure and Technology for Education (KITE), General Education Department, Government of Kerala
Class 7 AI chapter introduced 2024 (first Indian state to do so); extended into Class 8-10 ICT textbooks progressively through 2025-26; Class 10 robotics chapter made mandatory statewide from June 2025
Launched academic year 2025-26 as a pilot in ~5,000 government schools, Classes 6-9 (SCERT prepared bilingual textbooks for Classes 6-8); planned expansion to all government schools

3 of 63 requirements are worded from secondary summaries or could not be checked against the primary document; they are marked.

Alignment chart

Every CISCE + states requirement, rated

5Taught at this grade5Some material5Grades the framework asks for
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Showing 63 of 63 requirements

Robotics and Artificial Intelligence (66) — ICSE

Introduction to Robotics

Explain what a robot is, its characteristics, and the benefits of robots over humans (quality, productivity, efficiency, working span, hazardous environments)

We start Grade 3 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Covered earlier · Grade 7
What learners do

In Grade 7 'How Robots Sense, Decide and Move', learners apply a four-part robot test, separate robots from fixed machines and body-less AI, match what AI adds, and sort who controls each robot. Grade 3 sorts robots by land, air, water and space, and cobots.

State a brief history of robotics evolution since 1900 and recite Isaac Asimov's Three Laws of Robotics

We start Grade 3 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Covered earlier · Grade 7
What learners do

In Grade 7 'How Robots Sense, Decide and Move', learners apply a four-part robot test, separate robots from fixed machines and body-less AI, match what AI adds, and sort who controls each robot. Grade 3 sorts robots by land, air, water and space, and cobots.

Classify robots by terrain/field (aerial, ground, underwater), by control (manual, automatic), and identify bio-inspired robots (humanoid, bird, snake, insect)

We start Grade 3 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Covered earlier · Grade 7
What learners do

In Grade 7 'How Robots Sense, Decide and Move', learners apply a four-part robot test, separate robots from fixed machines and body-less AI, match what AI adds, and sort who controls each robot. Grade 3 sorts robots by land, air, water and space, and cobots.

Give at least one real-world application example of robots in domestic, industry, medical, defence, entertainment and agriculture domains

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

In Grade 7 'How Robots Sense, Decide and Move', learners compare robots in home, factory, hospital, farm, defence and transport settings, sort points into advantage or drawback, and judge a case where a rover bumps a student.

Where it is taught

Robot as a System

Describe the general block diagram and building blocks of a robot (mechanical, electronic, computational) and identify robots by demonstration/video (industrial arm, humanoid, mobile)

We start Grade 3 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Covered earlier · Grade 7
What learners do

In Grade 7 'How Robots Sense, Decide and Move', learners build the block diagram of a rover's loop, placing each step under sensor, controller, actuator or feedback, and match sensors to jobs.

Concepts in Robotics

Distinguish types of motion (linear, angular, circular) and types of joints/links (prismatic, revolute, spherical; rigid, soft) and use links and joints to create specific motion

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

In Grade 7 'How Robots Sense, Decide and Move', learners read about turning and sliding joints, links and degrees of freedom, and bend a one-joint elbow to see how the hand's reach changes.

Still to add

Linear, angular and circular motion are not named, and learners never count the degrees of freedom of a given mechanism beyond a definition question.

Define degrees of freedom of a robot and identify them through illustration

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

In Grade 7 'How Robots Sense, Decide and Move', learners read about turning and sliding joints, links and degrees of freedom, and bend a one-joint elbow to see how the hand's reach changes.

Still to add

Linear, angular and circular motion are not named, and learners never count the degrees of freedom of a given mechanism beyond a definition question.

Introduction to AI

Define artificial intelligence, give a brief history since John McCarthy coined the term in 1956, and explain the Turing Test and its importance

We start Grade 6 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

Grade 6 'What is AI?' covers the Turing test, Dartmouth 1956, AI winters, Moore's law, deep learning and large language models. In Grade 10 'The First Learning Machine!', learners order milestones chronologically.

Identify applications and benefits of AI across commercial, industry, medical/healthcare, defence, banking, entertainment, transport, security and agriculture domains

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

In Grade 9 'Welcome, AI Engineers!' and Grade 10 'Real-World CV Applications', learners name AI tasks in healthcare, finance, entertainment, agriculture, education and transport, and match tasks to sectors.

Still to add

Defence and military AI is not taught as a sector in Grades 9-10; it appears only in Grade 12.

Ethics in AI

Describe ethical considerations in AI including bias, prejudice, fairness, accountability, transparency, interpretability and explainability

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

In Grade 10 'Why AI Ethics Matters' and 'Building Your AI Ethics Framework', learners apply four ethics pillars to eight scenarios and use PAUSE to approve or reject use cases. 'Explainable AI (XAI)' judges explainability separately.

Data and Computing

Classify types of data (audio, visual, numeric, text) and explain how data becomes information within the AI Project Cycle

We start Grade 6 · framework Grade 9Framework grades 9–10
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 9 'Numbers in AI' and Python Lab Module 45, learners sort data types, work with feature vectors, use sampled sound arrays and see how a generated spectrogram changes.

Still to add

The tasks use digital samples; the physical analogue signal and the sampling-plus-quantisation conversion are not taught together.

Where it is taught

Explain the evolution of computing from deterministic (binary logic, conditional gates) to probabilistic approaches and why AI is needed for probabilistic, real-life problems

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

In Grade 9 'Probability Basics', learners see that a classifier gives a probability for every label, sort predictions by expected confidence, and explain why AI gives probabilities rather than certainty.

Still to add

The contrast is spread over three chapters; no single task runs a rule program and a probabilistic model on the same inputs and asks which suits noisy cases.

Python Programming

Write Python programs using variables, data types, operators, conditional statements (if/elif/else), loops (for/while, nested, break/continue/pass) and user-defined functions

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.

AI Concepts and Project Framework

Differentiate broad vs narrow AI and strong vs weak AI, and describe expert systems (e.g. Eliza), computer vision (CV), natural language processing (NLP) and neural networks (NN)

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

In Grade 5 'One Job, Any Job, Smarter Than Us?', learners sort eight real and story systems into narrow AI, general AI or superintelligence, and judge that today's chatbot is narrow. In Grade 6 they write why general AI is harder.

Where it is taught

Apply the AI Project Framework/Cycle stages — problem scoping, data acquisition, data exploration, modelling and evaluation — to a use case (in brief)

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.

New Age Robotic Systems (NARS)

Explain New Age Robotic Systems (warehouse, assistant, smart-home/school/mobility robots, autonomous cars/drones, medical/healthcare robotics) and why NARS are becoming increasingly relevant and possible

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

In Grade 7 'How Robots Sense, Decide and Move', learners compare robots in home, factory, hospital, farm, defence and transport settings, sort points into advantage or drawback, and judge a case where a rover bumps a student.

Where it is taught

Robots to Cobots

Differentiate a machine from a robot, and a robot from a cobot (human-in-the-loop, multi-robot systems), tracing the progress from robots to cobots

We start Grade 3 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Covered earlier · Grade 7
What learners do

In Grade 7 'How Robots Sense, Decide and Move', learners apply a four-part robot test, separate robots from fixed machines and body-less AI, match what AI adds, and sort who controls each robot. Grade 3 sorts robots by land, air, water and space, and cobots.

Components of Robots as a System

Explain gears (role, principles, gear ratio) and classify sensors (internal: position/velocity/acceleration/force; external: contact/non-contact) and their functions in a robotic system

We start Grade 3 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Covered earlier · Grade 7
What learners do

In Grade 7 'How Robots Sense, Decide and Move', learners build the block diagram of a rover's loop, placing each step under sensor, controller, actuator or feedback, and match sensors to jobs.

Describe actuators (linear, rotary), control systems (manual vs automatic) and integrate sensors, actuators and controller in a robotic system using a block diagram (input-controller-robot-feedback)

We start Grade 3 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Covered earlier · Grade 7
What learners do

In Grade 7 'How Robots Sense, Decide and Move', learners build the block diagram of a rover's loop, placing each step under sensor, controller, actuator or feedback, and match sensors to jobs.

Visualization, Design and Creation of Components

Use TinkerCAD to visualize, design and create robot components (joints: revolute, prismatic; RR mechanism) and visualize motion of designed components

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

In Python Lab Module 48, learners write react() and detect() functions for phone gestures, and the Motion Trainer and micro:bit simulator are open to them. The 'robot' is an on-screen Pixel.

Still to add

Learners do not design or build a device, model components, wire sensors or actuators, or write the device's own program, and no Grade 9-10 lesson links to this work.

Integrating Robots as a System

Build a simple robotic system in TinkerCAD, for example a wheeled mobile robot up to four wheels, integrating single-board-computer coding

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

In Python Lab Module 48, learners write react() and detect() functions for phone gestures, and the Motion Trainer and micro:bit simulator are open to them. The 'robot' is an on-screen Pixel.

Still to add

Learners do not design or build a device, model components, wire sensors or actuators, or write the device's own program, and no Grade 9-10 lesson links to this work.

Decision making in Machines/Computers

Compare automated vs autonomous systems and deterministic vs probabilistic decision making; contrast human decision making (subjective) with machine decision making (objective) via object-classification examples

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

In Grade 9 'Probability Basics', learners see that a classifier gives a probability for every label, sort predictions by expected confidence, and explain why AI gives probabilities rather than certainty.

Still to add

The contrast is spread over three chapters; no single task runs a rule program and a probabilistic model on the same inputs and asks which suits noisy cases.

Machine Intelligence and Cybersecurity

Explain Machine Learning basics (role of data/information, importance of programming and algorithms) using an example such as fruit sorting; identify cybersecurity/ethical issues (unauthorized hardware/software use, data theft, hacking, viruses/sabotage)

We start Grade 2 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

Grade 9 'ML Workflow Overview' and 'The Learning Moment' deepen training on examples, pattern finding and prediction on new input, building on Grade 2 'Learning From Examples'.

AI Project Framework

Apply the 4Ws problem canvas (Who, What, Where, Why) to scope a problem and write a problem statement; identify data sources/features/datasets including the Kaggle platform for data acquisition

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.

Represent explored data using bar graphs, line graphs, scatter plots and pie charts; apply modelling approaches — linear regression (prediction), rule-based, supervised learning and unsupervised learning (brief)

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

Data and Programming with Python

Use Python packages/libraries (NumPy, Pandas, SciPy, Matplotlib), lists, tuples and string operations/built-in string functions in programs

Framework grades 9–10
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.

Artificial Intelligence (883) — ISC

Basic concepts of AI

Define AI, trace its evolution and applications across fields, and explain common AI applications (decision making, remote patient monitoring, data analysis, complex problem-solving)

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.

Explain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples

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

Grade 11 'The Math Behind RL' teaches deterministic versus stochastic transitions. Grade 9 chapters such as 'Probability Basics' cover probability, confidence scores and rules versus learning.

Where it is taught

Give an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)

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

Learners cover who-what-why scoping and 5-whys in Grade 8 ('The Wrong Solution', 'The AI Challenge'), a run project cycle in Grade 9, and scoping through deployment with stakeholders in Grades 11-12.

Where it is taught

Represent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions

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 'Your First Python Program', 'Loop Magic' and 'The Dynamic List', learners write and run Python using variables, types, operators, if/elif/else, loops, lists and helper functions.

Where it is taught

NLP and state of AI

Explain the brief history and primary elements of AI including Machine Learning (ML) vs Deep Learning (DL)/neural networks, and applications of ML/DL such as image recognition, computer vision, speech recognition and information retrieval

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

Grade 10 'Welcome to Grade 10!' and 'Chatbot with Deep Learning' have learners sort traditional machine learning from deep learning. Grade 11 uses the terms correctly.

Still to add

The nesting of AI, machine learning and deep learning is never stated, and no task places a technique in its innermost layer.

Describe the domain of Natural Language Processing (NLP) — text understanding, text generation, language translation, question answering/chatbots, dialogue systems — and internet search types (navigation, informational, transactional, investigative, voice)

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

Grade 9 chapters such as 'The Spam Invasion' and 'Emotion Detection' have learners build a spam filter and a sentiment classifier in Python. Grade 12 covers speech recognition, translation, summarisation, question answering and entity recognition.

Where it is taught

Explain the potential use of AI in word processing, smartphones, web-based auction sites, scanner machines, e-commerce and social networking, and AI's social benefits in healthcare, transportation, disaster prediction and agriculture

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

Grade 11 gives a chapter each to AI in Healthcare, Finance, Agriculture, Manufacturing, Transportation and Entertainment, with the named AI task in each sector.

Mathematics for AI

Perform matrix operations (addition, subtraction, multiplication, transpose) and vector arithmetic/applications relevant to AI

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.

Apply Set Theory to data-table joins and relational algebra, and calculate simple statistical measures (mean, median, mode, variance, standard deviation)

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.

Data Visualization

Visualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features

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

Grade 9 chapters such as 'The Mysterious Gaps' and 'The Messy Data Monster' have learners handle missing values, outliers, encoding, scaling and leakage-safe train/test splitting with pandas and scikit-learn.

Where it is taught

Choose and construct appropriate statistical graphs (bar graph, histogram, scatter plot, pie graph) and represent multi-dimensional data (introduction to dimensionality)

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

In Grade 11 'The Silent Failures', learners read about monitoring dashboards and judge monitoring practices. Chart choice itself is taught in Grade 9 'A Picture Worth 1000 Rows', and Python Lab Module 32 names the chart type in each task.

Still to add

In Grades 11-12 learners never choose or build a chart or dashboard for an audience; dashboards are only described and judged.

Data Processing

Clean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets

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

Grade 9 chapters such as 'The Mysterious Gaps' and 'The Messy Data Monster' have learners handle missing values, outliers, encoding, scaling and leakage-safe train/test splitting with pandas and scikit-learn.

Where it is taught

Apply data transformation and standardization methods for analysis

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

Grade 9 chapters such as 'The Mysterious Gaps' and 'The Messy Data Monster' have learners handle missing values, outliers, encoding, scaling and leakage-safe train/test splitting with pandas and scikit-learn.

Where it is taught

Data Modelling, Simple Linear Regression

Distinguish types of data models (dimensional, relational, entity-relational) and describe regression analysis (dependent/independent variables, types of regression, in brief)

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

In Grade 9 chapters such as 'Regression Problems' and 'Linear Regression', learners study best-fit lines, slope, intercept, error and independent and dependent variables, and predict with code.

Where it is taught

Derive and apply the linear regression equation (least square regression line, regression coefficient) and solve linear equations in applied contexts

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

In Grade 9 chapters such as 'Regression Problems' and 'Linear Regression', learners study best-fit lines, slope, intercept, error and independent and dependent variables, and predict with code.

Where it is taught

Ethical Practices in AI

Apply an AI code of ethics — avoiding bias, ensuring privacy of users and their data, mitigating environmental impact, and evaluating the effects of designing technology that replicates human life

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.

Applications of AI (Class XII)

Apply Named Entity Recognition (NER) to classify names of people/organizations/locations in text, perform text summarization by extraction, and build a spam-email classifier using Machine Learning

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

Grade 9 chapters such as 'The Spam Invasion' and 'Emotion Detection' have learners build a spam filter and a sentiment classifier in Python. Grade 12 covers speech recognition, translation, summarisation, question answering and entity recognition.

Where it is taught

Statistics and Probability (Class XII)

Analyse data trends using mean, median, mode, range, variance and standard deviation; apply probability basics including uniform, normal and binomial distributions

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.

Formulate null and alternative hypotheses, identify Type I and Type II errors, and apply simple linear regression and correlation coefficient for prediction and relationship measurement

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.

Neural Networks / ML / DL paradigms (Class XII)

Explain Artificial Neural Networks (ANN) — neurons, input/hidden/output layers, forward and backward propagation, multi-layer perceptron (MLP) — using an example such as handwritten-digit recognition

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

Explain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree

We start Grade 3 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

Grade 8 chapters such as 'What Makes AI Intelligent?' teach rule-based versus learning-based AI with code. Grade 12 sorts product ideas into needs-AI versus needs-a-rule.

Where it is taught

Explain Deep Learning and demonstrate image/pattern recognition using a Convolutional Neural Network (CNN) example (e.g. classifying images of cats, dogs, birds)

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 'Convolution Operations', 'Building a CNN' and 'Object Detection Basics', learners build CNNs in Keras for image tasks and LSTMs for text; GANs, diffusion and transformers are described in Grade 11.

Where it is taught

Practical Implications of ANN (Class XII)

Prepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting

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

Grade 9 chapters such as 'The Mysterious Gaps' and 'The Messy Data Monster' have learners handle missing values, outliers, encoding, scaling and leakage-safe train/test splitting with pandas and scikit-learn.

Where it is taught

Build, train and evaluate a neural network using Keras/TensorFlow (fit(), epochs, batch size) and evaluate model performance using accuracy, precision and recall, visualized with Matplotlib

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 'Backpropagation Basics' and 'Your First Neural Network', learners study loss, gradient descent, the chain rule and backpropagation, then compile and fit multilayer models in Keras.

Where it is taught

Tune model hyperparameters (learning rate, batch size, number of hidden layers/neurons) using techniques such as grid search or random search

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

In Python Lab Module 50 and Grade 11 'Training Your Model', learners loop over learning rates and label each as too slow, converged or diverged. Grade 12 chapters name grid search and Optuna and match hyperparameters to effects.

Note

The varied settings are learning rate and width rather than every hyperparameter, and the sweep uses a toy loss without a validation set.

Practical Implications of ML (Class XII)

Apply ML to real-world case studies: predictive maintenance from historical machine data, and credit-card/identity-theft fraud detection

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

In Grade 11 chapters such as 'AI in Healthcare', 'AI in Finance' and 'AI in Manufacturing', learners work out alert rates, fraud base rates and maintenance savings by hand and interpret model output in each case. Grade 12 'The AI Radiologist' traces a screening round.

Where it is taught

Apply ML to medical diagnosis by analysing medical imaging, genetic information and patient records

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

In Grade 11 chapters such as 'AI in Healthcare', 'AI in Finance' and 'AI in Manufacturing', learners work out alert rates, fraud base rates and maintenance savings by hand and interpret model output in each case. Grade 12 'The AI Radiologist' traces a screening round.

Where it is taught

Introduction to Computer Vision (Class XII)

Introduce Computer Vision and OpenCV applications in real-world scenarios: image classification, object detection, facial recognition, colour detection and object tracking (brief explanation with examples)

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

Implement a simple image classifier distinguishing categories of objects (e.g. cats vs dogs, fruits vs vegetables) and build a facial-recognition application that can associate identified faces with known individuals

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 'Lily's Big Moment' and 'The 70% Problem', learners train image classifiers and test accuracy on held-out data. Grade 10 'Introduction to CNNs' builds and evaluates a CNN in Keras.

Where it is taught

Track the movement of objects across a video sequence over time (e.g. a ball in sports footage, vehicles in traffic surveillance)

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 'Lily's Big Moment' and 'The 70% Problem', learners train image classifiers and test accuracy on held-out data. Grade 10 'Introduction to CNNs' builds and evaluates a CNN in Keras.

Where it is taught

AI-integrated ICT curriculum (KITE / Samagra, Classes 7-10)

Computer Vision

Build a program (using block-based tools such as PictoBlox) that recognizes facial expressions using a pre-trained model, and explore gesture-based software applications for accessibility

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

In Grade 7 'Lily's Big Moment', 'Your Building Week!' and 'Refinement Week!', learners build spice and waste classifiers in Teachable Machine, measure baseline accuracy, fix a weak class and report results honestly.

Machine Learning

Distinguish supervised, unsupervised and reinforcement learning, and build a machine-learning model (dataset preparation, training, testing) such as one recognizing Indian Sign Language (ISL) gestures

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

In Grade 7 chapters such as 'Lily's Big Moment' and 'Refinement Week!', learners plan data, shoot balanced photos, train in Teachable Machine, hold out a test set, and report accuracy per category and failures.

NLP and Generative AI

Interact with Large Language Models (LLMs) and apply prompt engineering to perform text generation and image creation, and understand how responsive AI agents function in educational contexts

We start Grade 2 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

Learners practise prompt clarity and refinement from Grade 2 'When AI Says No', prompt engineering in Grade 8 'Maya's Discovery', and deepen it in Grade 11 'Welcome, AI Specialists!'.

Robotics integration

Build an AI-powered smart-home/robotics capstone (e.g. a smart door that opens via face recognition) integrating pre-trained ML models with Arduino hardware, a webcam and a servo motor

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

In Python Lab Module 48, learners write react() and detect() functions for phone gestures, and the Motion Trainer and micro:bit simulator are open to them. The 'robot' is an on-screen Pixel.

Still to add

Learners do not design or build a device, model components, wire sensors or actuators, or write the device's own program, and no Grade 9-10 lesson links to this work.

Robotics fundamentals

Construct circuits using sensors and actuators (e.g. Arduino, IR sensor, servo motor) and control electronic devices via computer programming, e.g. building an automated hand-triggered sanitiser dispenser

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

In Python Lab Module 48, learners write Python that reads a phone's live motion sensors. The Motion Trainer records real sensor data, and the micro:bit simulator exports rules that run on a real board.

Still to add

No learner-written code makes an actuator act on the environment, the micro:bit output is only an LED picture from generated code, and no Grade 9-10 lesson links to this work.

TN SPARK — Tamil Nadu Schools Programme for AI, Robotics and Knowledge of Online Tools

Computational thinking and coding

Develop computational-thinking and coding skills using block-based tools such as Scratch/Blockly through hands-on, project-based learning applied to real-world problems

We start Grade 2 · framework Grade 6Framework grades 6–8· secondary source
Fully covered
Our Grades 6–8 Covered earlier · Grade 3
What learners do

In Grade 3 'Byte on the Grid', learners drag blocks into a program with a sequence, an event start and a repeat, run it on a grid, watch where it goes wrong and fix one block. Grade 4 adds nested repeats and counters.

Where it is taught

AI and robotics awareness

Gain foundational awareness of AI concepts, robotics and digital tools beyond conventional computer education, using SCERT-prepared bilingual textbooks

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

In Grade 6 'AI in Your Home', 'AI in Your Phone' and 'AI Recommends For You', learners hunt for AI at home, on the phone and in games, travel, shopping, health and school, naming what each does for the user.

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.

  • Complete a minimum of 15 (Class IX) or 20 (Class X) internal-assessment assignments spanning Robotics, AI and Python programming, evaluated on class design, coding/documentation and execution/output
  • Complete a minimum of twenty (Class XI) or twenty-five (Class XII) practical assignments plus one project, algorithm design and hands-on programming solutions evaluated by a Visiting Examiner (Class XII)

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.