CBSE Class 9 Artificial Intelligence Syllabus 2024-25

The use of Artificial Intelligence has nowadays become pretty common in our lives. As such it is important that the students get themselves familiarized with the basic concepts of AI. CBSE Class 9 syllabus for AI comprise of important topics and sub-topics that can provide the students fundamental knowledge about this technology.

Along with this, the students will also get to learn basics of Python coding language. In this article, the candidates can find the complete details of the CBSE Class 9 Artificial Intelligence syllabus 2024-25.

CBSE Artificial Intelligence Syllabus for Class 9 2024-25

The syllabus of AI provides the students adaptation with the important technical and life skills for AI. The main aim is to help learners understand the concepts of Artificial Intelligence and its application in different scenarios. Check below the complete details of the CBSE Class 9 AI syllabus 2024-25.

Unit No.Unit NameSub-unitDuration/ Periods
Unit IIntroduction to AIExcite2 Hours 40 mins/ 4 Periods
Relate2 Hours/ 3 Periods
Purpose2 Hours/ 3 Periods
Possibilities2 Hours/ 3 Periods
AI Ethics3 Hours 20 mins/ 5 Periods
Unit IIAI Project CycleProblem Scoping14 Hours/ 21 Periods
 Data Acquisition2 Hours/ 3 Periods
 Data Exploration4 Hours/ 6 Periods
 Modelling6 Hours/ 9 Periods
Unit IIINeural Network—–4 Hours/ 6 Periods
Unit IVIntroduction To Python—–70 Hours/ 105 Periods
—–Total—–112 Hours/ 168 Periods

Unit I: Introduction to AI


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

Ice Breaker Activity: Dream Smart Home idea

Learners to design a rough layout of floor plan of their dream smart home

Recommended Activity: The AI Game

Learners to participate in three games based on different AI domains

  • Game 1: Rock, Paper and Scissors (based on data)
  • Game 2: Mystery Animal (based on Natural Language Processing – NLP)
  • Game 3: Emoji Scavenger Hunt (based on Computer Vision – CV)

Recommended Activity: AI Quiz (Paper Pen/Online Quiz)

Recommended Activity: To write a letter to one’s future self

Learners will have to write a letter to self-keeping the future in context. They will describe what they have learnt so far or what they would like to learn someday.


Video Session: To watch a video

Introducing the concept of Smart Cities, Smart Schools and Smart Homes

Recommended Activity: Write an Interactive Story

Learners to draw a floor plan of a Home/School/City and write an interactive story around it using Story Speaker extension in Google docs.


Session: Introduction to sustainable development goals

Recommended Activity: Go Goals Board Game

Learners to answer questions on Sustainable Development Goals


Session: Theme-based research and Case Studies

  • Learners will listen to various case studies of inspiring start-ups, companies or communities where AI has been involved in real-life.
  • Learners will be allotted a theme around which they need to search for present AI trends and have to visualize the future of AI in and around their respective theme

Recommended Activity: Job Ad Creating activity

Learners to create a job advertisement for a firm describing the nature of job available and the skill-set required for it 10 years down the line. They need to figure out how AI is going to transform the nature of jobs and create the Ad accordingly

AI 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 for a given scenario

Session: AI Bias and AI Access

  • Discussing about the possible bias in data collection
  • Discussing about the implications of AI technology

Recommended Activity: Balloon Debate

Students divide in teams of 3 and 2 teams are given 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 the society

Unit II: AI Project Cycle

Problem Scoping

Session: Introduction to AI Project Cycle

  • Problem Scoping
  • Data Acquisition
  • Data Exploration
  • Modelling
  • Evaluation

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

  • Discuss various topics within the given theme and select one.
  • List down/ Draw a mindmap of problems related to the selected topic and choose one problem to be the goal for the project.

Activity: To set actions around the goal

  • List down the stakeholders involved in the problem
  • Search on the current actions taken to solve this problem
  • Think around the ethics involved in the goal of your project

Activity: Data and Analysis

  • What are the data features needed?
  • Where can you get the data?
  • How frequent 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?

Presentation: Presenting the goal, actions and data

Data Acquisition

Activity: Introduction to data and its types

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

Data Exploration

Session: Data Visualisation

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

Recommended Activity: Let’s use Graphical Tools

  • To decide what kind of data is required for a given scenario and acquire the same.
  • To select an appropriate graphical format to represent the data acquired.
  • Presenting the graph sketched


Session: Decision Tree

To introduce basic structure of Decision Trees to students

Recommended Activity: Decision Tree

To design a Decision Tree based on the data given

Recommended Activity: Pixel It

  • To create an “AI Model” to classify handwritten letters
  • Students develop a model to classify handwritten letters by diving the alphabets into pixels
  • Pixels are then joined together to analyze a pattern amongst same alphabets and to differentiate the different ones

Unit III: Neural Network

Session: Introduction to neural network

  • Relation between the neural network and nervous system in human body
  • Describing the function of neural network

Recommended Activity: Creating a Human Neural Network

  • Students split in four teams each representing input layer (X students), hidden layer 1 (Y students), hidden layer 2 (Z students) and output layer (1 student) respectively
  • Input layer gets data which is passed on to hidden layers after some processing
  • The output layer finally gets all information and gives meaningful information as output

Unit IV: Introduction to Python

Recommended Activity: Introduction to programming using Online Gaming portals like Code Combat

Session: Introduction to Python language

Introducing python programming and its applications

Practical: Python Basics

  • Students go through lessons on Python Basics (Variables, Arithmetic Operators, Expressions, Data Types – integer, float, strings, using print() and input() functions)
  • Students will try some simple problem solving exercises on Python Compiler

Practical: Python Lists

  • Students go through lessons on Python Lists (Simple operations using list)
  • Students will try some basic problem solving exercises using lists on Python Compiler

CBSE Class 9 Artificial Intelligence Syllabus 2024: Download PDF

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