The syllabus, Classes 1 to 12

Six strands, every year

Each strand returns every year at a deeper level, so each class builds on the one before.

• S1

  Thinking like a computer

  How do I break a problem into steps?

  Early: Steps for making noon chai

  Class 12: Designing an ML pipeline

• S2

  How AI senses and understands

  How does a machine see, hear, read?

  Early: Our senses vs. a camera's "eye"

  Class 12: How computer vision and LLMs work inside

• S3

  Data and learning

  How does a machine learn from examples?

  Early: Sorting apples into baskets

  Class 12: Training and evaluating a real model

• S4

  Using and creating with AI

  How do I use AI well, and build with it?

  Early: Watching the teacher ask a voice assistant

  Class 12: Building and presenting an AI project for the community

• S5

  AI, me and society

  Is it fair, safe, true, and good?

  Early: Being kind and safe with gadgets

  Class 12: Law, bias, jobs, and AI governance

• S0

  Typing & digital skills

  Can I use a laptop with confidence?

  Early: (starts in Class 6)

  Class 12: Fast, accurate typing; managing files and tools without help

How the strands grow, year by year

Every strand runs through every class. Pick a square to see the lessons that teach it.

Level: Understand. Unplugged. Story-led with a recurring character. The teacher reads, and the children play and draw.

• Class1Smart Helpers Around Me20 periodsPlanned

  1. 1. Living or machine?

     What is alive? · What is a machine? · Machines that help at home · Machines that help in school

  2. 2. Patterns everywhere

     Colour patterns · Patterns in our clothes and shawls · Sound and clap patterns · Make your own pattern

  3. 3. Step by step

     Steps to wash hands · Steps to make noon chai · What if a step is missing? · Put the pictures in order

  4. 4. Sorting

     Sort by colour (apples) · Sort by shape · Sort by size · Which one doesn't belong?

  5. 5. Kind and safe

     Gadgets are tools, not toys · Asking an adult first · Kind words, on screen and off

• Class2How Machines Notice Things20 periodsPlanned

  1. 1. Senses

     Our five senses · A camera is like an eye · A microphone is like an ear · What machines cannot feel

  2. 2. Clear instructions

     Robot game on a floor grid · Forward, turn, stop · When instructions are unclear · Fix the instructions

  3. 3. Learning from examples

     How did you learn what a cat is? · Showing examples · Too few examples · Tricky examples

  4. 4. Machines that talk

     Voice helpers (teacher demo) · Asking good questions · When the machine doesn't understand

  5. 5. Me and screens

     Screen time · Private things stay private · Telling an adult if something feels wrong

• Class3Thinking Like a Computer30 periodsPlanned

  1. 1. Patterns

     Growing patterns · Number patterns · Shape rules · Spot the rule · The weaver's talim: a pattern written as code

  2. 2. Breaking problems down

     Big jobs, small jobs · Planning a school trip · Clue puzzles (2–3 clues) · Which step first? · Build a plan

  3. 3. Algorithms

     Grid paths · Loops: "repeat 3 times" · Before, after, in between · Debugging: find the mistake · Write an algorithm for a friend

  4. 4. Is it AI?

     Machines that follow rules · Machines that learn · Sort the cards: AI or not? · Quick, Draw! (level B) · Where have I seen AI?

  5. 5. Safe and smart online

     What is the internet? · Strong passwords (a game) · Strangers online · Not everything online is true · My safety promise

  Project: A pattern book: design a shawl border, write it as a talim, and have a friend follow it

Level: Understand → Apply. Mostly unplugged, with teacher demos and short turns on shared devices.

• Class4Machines That Learn From Examples30 periodsPlanned

  1. 1. What is data?

     Data is information · Collecting data about our class · Tally marks and tables · Pictures as data · Sounds as data

  2. 2. Labels and sorting

     What is a label? · Sort leaves (chinar, willow, poplar) · Rules vs. examples · The card-sorting "machine" · When the machine is wrong

  3. 3. How machines see

     Pixels: a picture made of dots · Drawing with a pixel grid · Teachable Machine: teach a camera (level B/C) · Why lighting matters · What the camera cannot know

  4. 4. Fair for everyone?

     Does it work for all faces? · Missing examples · A fair dataset · Our class fairness check · Making it better

  5. 5. My data, my choice

     What is personal data? · Photos are data · Asking permission · Digital footprints (footprint trail game) · My data rules

  Project: Teach a machine: the class trains a paper classifier, tests it, finds where it fails, and fixes it

• Class5AI That Listens and Talks30 periodsPlanned

  1. 1. Language is data

     Words and sentences · How a machine counts words · Predict the next word (game) · Word clouds · Semantris (level B)

  2. 2. Translation

     How do we translate? · Machine translation demo, Urdu ↔ English (level B) · Funny translation mistakes · Why Kashmiri is hard for machines · Checking with a person

  3. 3. Chatbots

     What is a chatbot? · A rule-based chatbot on paper (flowchart) · Build a chatbot in Scratch Desktop (level C, offline) · What chatbots get wrong · Talking to a chatbot safely (teacher demo)

  4. 4. Is it true?

     Facts and opinions · Checking a source · Verify before you share · Made-up answers from AI · Our checking checklist

  5. 5. Block coding

     Sequences in Scratch · Loops · If-then · Events · Make a quiz game

  Project: Class helper bot: a paper or Scratch chatbot that answers questions about our school

• Class6Understanding AI42 periods · aligned with CBSE Class 6 AI Book written

  1. 0. Getting to know the laptop (S0, 6 periods)

     Parts of a laptop and caring for it · Mouse, trackpad and windows · Home row: first typing.com lessons · Files and folders: saving your work · Typing practice and good posture · The browser, and safe links

  2. 1. AI and everyday life

     What AI is · AI vs. automation · Human vs. machine intelligence · AI in Kashmir today · Three ways machines learn: supervised, unsupervised, reinforcement (unplugged games)

  3. 2. Data

     Types of data: numbers, text, images, sound · Organising data in tables · Bar charts and pictographs · Collecting a class dataset · What makes data good?

  4. 3. Patterns and decisions

     Patterns in daily routines · Patterns in data · Making predictions · Decision trees on paper · When patterns mislead

  5. 4. Ethics and digital responsibility

     Digital footprints · Privacy · Passwords and 2-step login · Respect online · Human-centred design: who is it for?

  6. 5. Computational thinking

     Decomposition puzzles · Pattern puzzles · Abstraction: what to ignore · Algorithms with conditions · Debugging

  Project: Our school in data: collect, chart, and explain one pattern about our school

  Read the Class 6 book

Level: Apply. Shared-device activities using no-code tools. Generative AI is taught from Class 8 as a teacher-led topic, with direct student use from Class 9.

• Class7How AI Makes Predictions35 periods · aligned with CBSE Class 7 AI Book written

  1. 1. Three techniques

     Classification (sort apples by grade) · Regression (predict tomorrow's temperature) · Clustering (group similar items) · Training and testing: splitting a dataset · Which technique fits?

  2. 2. Domains of AI

     Computer vision · Natural language processing · Data science · Chatbots, image recognition, translation · Map the domain game

  3. 3. AI at work

     Healthcare · Education · Transport · Agriculture: orchard disease and saffron · Communication

  4. 4. Data visualisation

     Structured vs. unstructured data and formats · Collecting data honestly · Preparing (cleaning) data · Bar, line, and pie charts · Accuracy, precision, and charts that lie

  5. 5. Bias and citizenship

     What is bias? · Three kinds of bias (missing-group, historical, measurement) · Human in the loop · Consent, privacy, transparency · Good digital citizens

  Project: Predict it: choose a local question (e.g. rainfall and apple yield), collect data, and make and test a prediction

  Read the Class 7 book
• Class8Building With AI35 periods · aligned with CBSE Class 8 AI Book written

  1. 1. The AI project cycle

     Define the problem · Collect data · Test the tool · Reflect and improve · How AI learns from patterns

  2. 2. No-code AI

     Image classifier (Teachable Machine) · Sound classifier · Pose classifier · Testing with new examples · How good is it?

  3. 3. Generative AI (teacher-led)

     What "generative" means · Predicting the next word · AI images · Deepfakes and how to spot them · Where it helps and where it harms

  4. 4. Data and fairness

     How AI uses data · Finding bias in a dataset · Fixing the dataset · Inclusivity: languages, accents, faces · Accountability: who is responsible?

  5. 5. Responsible AI

     Privacy issues · Misinformation · Social impact · Using AI honestly for homework · Our class AI charter

  Project: A no-code AI for our community, e.g. a leaf-disease spotter or a waste sorter, taken through the full project cycle

  Read the Class 8 book
• Class9AI Foundationsaligned with CBSE 417 Class IX Book written

  1. 1. AI reflection, project cycle and ethics

     The three domains of AI (AI games) · The 4Ws problem canvas · Data acquisition and system maps · Modelling: rule-based vs. learning-based · Evaluation: true and false positives, true and false negatives

  2. 2. Data literacy

     What data literacy is · Data privacy vs. security · Acquiring and processing data · Interpreting data · Building a data dashboard

  3. 3. Maths for AI

     Statistics: mean, median, mode · Probability · Probability in AI decisions · Graphs for AI · Why maths matters for AI

  4. 4. Generative AI

     How LLMs work, simply · Prompting: role, task, context, format · Checking AI output · Images, voice, and video generation · Honest use and citing AI

  5. 5. Python basics

     Input and output · Variables and data types · Operators · If-else · Lists

  Project: An SDG-linked AI proposal: problem canvas, data plan, and a prototype

  Read the Class 9 book

Level: Create. Python, real models, and a portfolio. Direct use of AI tools, with guidance on academic honesty.

• Class10AI in Practicealigned with CBSE 417 Class X Book written

  1. 1. The project cycle, and ethical frameworks

     The cycle and the three domains, revisited · Frameworks, and what shapes a decision · Types of ethical frameworks · Bioethics, and a hospital case · Applying a framework to our project

  2. 2. Modelling (with CBSE's Statistical Data unit)

     AI, ML, DL and the words for data · Rule-based and learning-based; three ways to learn · Classification, regression, clustering, association · Neural networks · No-code AI on statistical data

  3. 3. Evaluating models

     Train-test split · Accuracy and error · The confusion matrix, from scratch · Precision, recall, F1 · Bias, transparency, accountability

  4. 4. Computer vision

     CV tasks · Images are numbers · A no-code image classifier · Features and convolution · Inside a CNN

  5. 5. Natural language processing

     Stages of NLP · Script bots and smart bots · Text normalisation · Bag of words and TF-IDF · Sentiment analysis

  Project: Build it, measure it, answer for it: a working classifier, a confusion-matrix evaluation, and an ethics review

  Read the Class 10 book
• Class11Machine Learningaligned with CBSE 843 Class XIPlanned

  1. 1. Python for data

     NumPy basics · pandas DataFrames · Cleaning data · Plotting · Exploratory analysis

  2. 2. Core ML

     Linear regression · Classification with k-nearest neighbours · Decision trees · Clustering (k-means) · Choosing a model

  3. 3. Deep learning intuition

     Neurons and layers · How a network learns · Image models · Language models · Why they need so much data

  4. 4. Large language models

     Tokens and prediction · Training vs. using a model · Hallucinations · Advanced prompting · Using LLMs to learn, not to skip learning

  5. 5. AI, law and society

     India's DPDP Act · Copyright and AI · Bias audits · AI and jobs · Careers in AI

  Project: Data story: a real local dataset analysed end to end in Python

• Class12Capstonealigned with CBSE 843 Class XIIPlanned

  1. 1. Capstone planning

     Choosing a meaningful problem · Stakeholder interviews · Data plan · Ethics review · Project proposal

  2. 2. Building

     Data collection · Model training · Evaluation · Iteration · Documentation

  3. 3. Deploying

     Turning a model into an app · No-code app builders · Using AI APIs responsibly · Testing with real users · Accessibility

  4. 4. AI futures

     AI agents · AI governance · Environmental cost of AI · AI for public good · The human in the loop

  5. 5. Portfolio and pathways

     Building a portfolio · Presenting your work · Higher education paths · Entrepreneurship · Lifelong learning

  Project: Presented to the school and community: AI in the service of people