CBSE Class 9 Artificial Intelligence Syllabus 2026-27 - Updated Curriculum

Part 

Units 

No. Of Hours for Theory and Practical

Max. Marks for Theory and Practical

Part A: Employability Skills

Unit 1: Communication Skills-I 

10

2

Unit 2: Self-Management Skills-I

10

2

Unit 3: ICT Skills-I 

10

2

Unit 4: Entrepreneurial Skills-I

15

2

Unit 5: Green Skills-I 

05

2

Total

50

10

Part B

Subject Specific Skills 

Theory 

Practical 

Unit 1: AI Reflection, Project Cycle, and Ethics 

30

25

10

Unit 2: Data Literacy 

22

28

10

Unit 3: Maths for AI (Statistics & Probability) 

12

13

07

Unit 4: Introduction to Generative AI 

08

12

05

Unit 5: Introduction to Python

01

09

08

Total 

160

40

Part C: Practical Work

Unit 5: Introduction to Python Practical File (minimum 15 programs) 

15

Practical Examination 

• Simple programs using input and output function 

• Variables, Arithmetic Operators, Expressions, Data Types 

• Flow of control and conditions 

• Lists 

*Any 3 programs based on the above topics

15

Viva Voce

5

Total 

35

Part D

Project Work / Field Visit / Student Portfolio 

* relate it to Sustainable Development Goals 

15

Total

15

Grand Total

210

100

Unit 1: 

Communication Skills

Session 1: Introduction to Communication

Session 2: Verbal Communication 

Session 3: Non-Verbal Communication

Session 4: Writing Skills: Parts of Speech

Session 5: Writing Skills: Sentences

Session 6: Pronunciation Basics

Session 7: Greetings and Introduction

Session 8: Talking about Self

Session 9: Asking Questions I

Session 10: Asking Questions II

Unit 2: 

Self-Management Skills

Session 1: Introduction to Self-management

Session 2: Strength and Weakness Analysis

Session 3: Self-confidence

Session 4: Positive Thinking

Session 5: Personal Hygiene

Session 6: Grooming

Unit 3: 

Information and Communication Technology Skills

Session 1: Introduction to ICT

Session 2: ICT Tools: Smartphones and Tablets — I

Session 3: ICT Tools: Smartphones and Tablets — II

Session 4: Parts of Computer and Peripherals 

Session 5: Basic Computer Operations 

Session 6: Performing Basic File Operations

Session 7: Communication and Networking — Basics of Internet

Session 8: Communication and Networking — Internet Browsing

Session 9: Communication and Networking — Introduction to e-mail 

Session 10: Communication and Networking — Creating an Email Account

Session 11: Communication and Networking — Writing an email

Session 12: Communication and Networking — Receiving and Replying to emails

Unit 4: 

Entrepreneurship Skills 

Session 1: What is Entrepreneurship?

Session 2: Role of Entrepreneurship 

Session 3: Qualities of a Successful Entrepreneur 

Session 4: Distinguishing Characteristics of Entrepreneurship and Wage Employment

Session 5: Types of Business Activities

Session 6: Product, Service, and Hybrid Businesses 

Session 7: Entrepreneurship Development Process 

Unit 5

Green Skills 

Session 1: Society and Environment

Session 2: Conserving Natural Resources

Session 3: Sustainable Development and Green Economy

Unit

Name

Unit 1

AI Reflection, Project Cycle, and Ethics

Unit 2

Data Literacy

Unit 3

Maths for AI (Statistics & Probability) 

Unit 4

Introduction to Generative AI

Unit 5

Introduction to Python

Sub-Unit 

Learning Outcomes

Session / Activity / Practical

AI Reflection

To identify and appreciate Artificial Intelligence and describe its applications in daily life.

Session: Introduction to AI and setting up the context of the curriculum

Recommended Activity: Make a statement about lighting and LUIS will interpret and adjust the house accordingly 

To recognize, engage, and relate with the three realms of AI: Computer Vision, Data Statistics, and Natural Language Processing.

Recommended Activity: The AI Game 

Learners are to participate in three games based on different AI domains.

• Game 1: Rock, Paper, and Scissors (based on data) 

• Game 2: Semantris (based on Natural Language Processing - NLP) 

• Game 3: Quick Draw (based on Computer Vision - CV)

AI Project Cycle 

Identify the AI Project Cycle framework. 

Session: Introduction to AI Project Cycle 

• Problem Scoping  

• Data Acquisition 

• Data Exploration 

• Modelling 

• Evaluation 

• Deployment

Learn problem scoping and ways to set goals for an AI project. 

Session: Problem Scoping

Activity: Brainstorm around the theme provided and set a goal for the AI project.

• Discuss various topics within the given theme and select one.

• Fill in the 4Ws problem canvas and a problem statement to learn more about the problem identified in the community/ society.

• List down/ Draw a mind map of problems related to the selected topic and choose one problem to be the goal for the project. 

Identify stakeholders involved in the problem scope. Brainstorm on the ethical issues involved around the problem selected. 

• Activity: To set actions around the goal. 

• List down the stakeholders involved in the problem.

• Search for the current actions taken to solve this problem.

• Think about the ethics involved in the goal of your project.

Understand the iterative nature of problem scoping in the AI project cycle. Foresee the kind of data required and the kind of analysis to be done. 

Activity: Data and Analysis

• What are the data features needed? 

• How will the features collected affect the problem? 

• Where can you get the data? 

• How frequently do you have to collect the data? 

• What happens if you don’t have enough data? 

• What kind of analysis needs to be done? 

• How will it be validated? 

• How does the analysis inform the action?

Share what the students have discussed so far. 

Presentation: Presenting the goal, actions, and data.

Teamwork Activity: 

• Brainstorming solutions for the problem statement.

Identify data requirements and find reliable sources to obtain relevant data. 

Session: Data Acquisition 

Activity: Introduction to data and its types. 

• Students work around the scenarios given to them and think of ways to acquire data.

Activity: Data Features 

• Identifying the possible data features affecting the problem.

Activity: System Maps 

• Creating system maps considering data features identified.

To understand the purpose of Data Visualisation 

Session: Data Exploration/ Data Visualisation

• Need of visualising data 

• Ways to visualise data using various types of graphical tools.

Quiz Time

Use various types of graphs to visualise acquired data. 

Recommended Activities: Let’s use Graphical Tools

• Selecting an appropriate graphical format and presenting the graph sketched.

• Understanding graphs using Data viz catalogue.

• Listing of newly learnt data visualisation techniques. 

• Top 10 Song Prediction: Identify the data features, collect the data, and convert it into a graphical representation. 

• Collect and store data in a spreadsheet and create some graphical representations to understand the data effectively.

Understand modelling (Rule Based & Learning-based) 

Session: Modelling 

• Introduction to modelling and types of models (Rule-based & Learning-based)

Understand various evaluation techniques. 

Session: Evaluation

Learners will understand about new terms

• True Positive 

• False Positive 

• True Negative 

• False Negative 

Challenge students to think about how they can apply their knowledge of deployment in future AI projects and encourage them to continue exploring different deployment methods.

Session: Deployment

Recommended Case Study: Preventable Blindness.

Activity: Implementation of AI project cycle to develop an AI Model for Personalized Education.

To understand and reflect on the ethical issues around AI. 

Session: Ethics 

Video Session: Discussing about AI Ethics 

Recommended Activity: Ethics Awareness 

• Students play the role of major stakeholders, and they have to decide what is ethical and what is not in a given scenario.

• Students explore the Moral Machine to understand more about the impact of ethics.

To gain awareness around AI bias and AI access. 

Session: AI Bias and AI Access 

• Discussing the possible bias in data collection

• Discussing the implications of AI technology

To let the students analyse the advantages and disadvantages of Artificial Intelligence. 

Recommended Activity: Balloon Debate

• Students divided into teams of 3 and 2 teams are given the same theme. One team goes in affirmation to AI for their section while the other one goes against it. 

• They have to come up with their points as to why AI is beneficial/ harmful for society.

Sub-Unit 

Learning Outcomes

Session / Activity / Practical 

Basics of data literacy 

• Define data literacy and recognize its importance Understand how data literacy enables informed decision-making and critical thinking.

• Apply the Data Literacy Process Framework to analyse and interpret data effectively.

• Differentiate between Data Privacy and Security

• Identify potential risks associated with data breaches and unauthorised access. 

• Learn measures to protect data privacy and enhance data security 

• Session: Basics of data literacy Introduction to Data Literacy 

• Impact of Data Literacy

• How to Become Data Literate? 

• What are data security and privacy? How are they related to AI? 

• Best Practices for Cyber Security

Recommended Activity: Impact of News Articles

Acquiring Data, Processing, and Interpreting Data 

• Determine the best methods to acquire data. 

• Classify different types of data and enlist different methodologies to acquire it. 

• Define and describe data interpretation. 

• Enlist and explain the different methods of data interpretation. 

• Recognize the types of data interpretation. 

• Realise the importance of data interpretation.

Session: Acquiring Data, Processing, and Interpreting Data 

• Types of data 

• Data Acquisition/Acquiring Data 

• Best Practices for Acquiring Data 

• Features of data and Data Preprocessing 

• Data Processing and Data Interpretation 

• Types of Data Interpretation

• Importance of Data Interpretation 

Recommended Activities: 

• Trend analysis 

• Visualise and Interpret Data

Project Interactive Data Dashboard & Presentation 

• Recognize the importance of data visualisation 

• Discover different methods of data visualisation 

Session: Project Interactive Data Dashboard & Presentation 

• Data visualisation Using Tableau

Reference Links 

• Public Tableau

• Data wrapper

Sub-Unit 

Learning Outcomes 

Session / Activity / Practical

Importance of Math for AI 

Analysing the data in the form of numbers/images and finding the relation/pattern between them. 

Use of Math in AI. 

Session: Importance of Math for AI 

• Finding Patterns in Numbers and Images. 

• Uses of Math -

• Statistics

• Linear Algebra 

• Probability

• Calculus 

Number Patterns Picture Analogy

Activity: 

• Observe the number pattern and find the missing number. 

• To find connections between sets of images and use them to solve problems

Statistics 

Understand the concept of Statistics in real life. 

Session: 

• Definition of Statistics

• Applications 

• Disaster Management

• Sports 

• Diseases Prediction

• Weather Forecast

Application in various real-life scenarios 

Activity: Uses of Statistics in daily life 

• Students will explore the applications of statistics in real life. They collect data and can apply various statistical measures to analyse the data.

Activity: Car Spotting and Tabulating 

Purpose: To implement the concept of data collection, analysis, and interpretation. Activity Introduction:

• In this activity, Students will be engaged in data collection and tabulation.

• Data collection plays a key role in Artificial Intelligence as it forms the basis of statistics and interpretation by AI. 

• This activity will also require students to answer a set of questions based on the recorded data.

Probability 

Understand the concept of Probability in real life and explore various types of events.

Session: Introduction to Probability 

• How to calculate the probability of an event

• Types of events

• understand the concept of Probability using a relatable example

Exercise: Identify the type of event.

Application in various real-life scenarios

Session: Applications of Probability 

• Sports 

• Weather Forecast 

• Traffic Estimation

Exercise: Revision time 

Learning Outcomes

Session / Activity / Practical

Students will be able to define Generative AI & classify different kinds.

Recommended Activity: 

• Activity: Guess the Real Image vs. the AI-generated image

• Students will be able to explain how Generative AI works and recognize how it learns. 

• Applying Generative AI tools to create content. 

• Understanding the ethical considerations of using Generative AI.

Session: 

• Introduction to Generative AI

• Generative AI vs Conventional AI

Session:

• Types of Generative AI 

• Examples of Generative AI 

Session: 

• Benefits of using Generative AI 

• Limitations of using Generative AI 

Recommended Activities:

• Hands-on Activity: GAN Paint

• Generative AI tools 

Session: 

• Ethical considerations for using Generative AI

Learning Outcomes 

Session / Activity / Practical

Learn basic programming skills through gamified platforms.

Recommended Activity: 

• Introduction to programming using Online Gaming portals like CodeCombat. 

Acquire introductory Python programming skills in a very user-friendly format. 

Session: 

• Introduction to Python language

• Introducing Python programming and its applications

Theory + Practical: Python Basics

• Students go through lessons on Python Basics (Variables, Arithmetic Operators, Expressions, Comparison Operators, logical operators, Assignment Operators, Data Types - integer, float, strings, type conversion, using print() and input() functions) 

• Students will try some simple problem-solving exercises on Python Compiler.

Practical: Flow of control and conditions

• Students go through lessons on conditional and iterative statements (if, for, and while)

• Students will try some basic problem-solving exercises using conditional and iterative statements on the Python Compiler.

Practical: Python Lists 

• Students go through lessons on Python Lists (Simple operations using lists) 

• Students will try some basic problem-solving exercises using lists on Python Compiler.

Unit 5: Introduction To Python: Suggested Program List

PRINT 

• To print personal information like Name, Father’s Name, Class, and School Name. 

• To print the following patterns using multiple print commands-

• To find the square of the number 7 

• To find the sum of two numbers 15 and 20. 

• To convert the length given in kilometres into metres. 

• To print the table of 5 up to five terms. 

• To calculate Simple Interest if the principle_amount = 2000 rate_of_interest = 4.5 time = 10 

INPUT 

• To calculate the Area and Perimeter of a rectangle 

• To calculate the Area of a triangle with Base and Height 

• To calculate average marks of 3 subjects

• To calculate discounted amount with discount %

• To calculate the Surface Area and Volume of a Cuboid

LIST

• Create a list in Python of children selected for science quiz with the following namesArjun, Sonakshi, Vikram, Sandhya, Sonal, Isha, and Kartik Perform the following tasks on the list in sequence- 

• Print the whole list 

• Delete the name “Vikram” from the list 

• Add the name “Jay” at the end 

• Remove the item which is in the second position. 

• Create a list num=[23,12,5,9,65,44] 

• print the length of the list 

• print the elements from second to fourth position using positive indexing 

• print the elements from position third to fifth using negative indexing

• Create a list of the first 10 even numbers, add 1 to each list item, and print the final list. 

• Create a list List_1=[10,20,30,40]. Add the elements [14,15,12] using the extend function. Now sort the final list in ascending order and print it.

IF, FOR, WHILE 

• Program to check if a person can vote 

• To check the grade of a student 

• Input a number check if the number is positive, negative, or zero, and display an appropriate message. 

• To print the first 10 natural numbers 

• To print the first 10 even numbers 

• To print odd numbers from 1 to n 

• To print the sum of the first 10 natural numbers 

• Program to find the sum of all numbers stored in a list

Suggested Projects/ Field Visit / Portfolio (Anyone has to be done)

Suggested Projects

1. Create an AI Model using tools like- 

• Teachable Machine 

• Machine Learning For Kids 

2. Choose an issue that pertains to the objectives of sustainable development and carry out the actions listed below. 

• To understand more about the problem identified, create a 4Ws problem canvas. 

• Identify the data features and create a system map to understand the relationship between them 

• Visualise the data collected graphically (Spreadsheet software to be used to store and visualise the data) 

• Suggest an AI-enabled solution to it (Prototype/Research Work)

Suggested Field Visit 

Visit an industry or IT company or any other place that is creating or using AI applications and present the report for the same. A visit can be in physical or virtual mode.

Suggested Student Portfolio 

Maintaining a record of all AI activities and projects (For Example Letter to Futureself, Smart Home Floor Plan, Future Job Advertisement, Research Work on AI for SDGs