(PDF) CBSE Class 9 Artificial Intelligence Syllabus PDF | Class 9 Artificial Intelligence Syllabus PDF 2022-23

CBSE Class 9 Artificial Intelligence Syllabus PDF – In our daily lives, artificial intelligence plays a vital role in transforming human life in a wide range of daily services. From the academic year 2022-23, CBSE Board has introduced Artificial Intelligence subjects for 9th standard students. It is considered a skill subject and compulsory for all students of CBSE class 9th. The CBSE Board has developed a Class 9th Artificial Intelligence syllabus that covers all the important subjects and sub-subjects required for teaching 9th-standard students. It also provides detailed information on topics and sub-topics, including unit-wise marking plans and durations.

Overview – CBSE Class 9 Artificial Intelligence Syllabus PDF

Unit No.	Unit Name	Sub-unit	Duration/ Periods
Unit I	Introduction to AI	Excite	2 Hours 40 mins/ 4 Periods
		Relate	2 Hours/ 3 Periods
		Purpose	2 Hours/ 3 Periods
		Possibilities	2 Hours/ 3 Periods
		AI Ethics	3 Hours 20 mins/ 5 Periods
Unit II	AI Project Cycle	Problem Scoping	14 Hours/ 21 Periods
		Data Acquisition	2 Hours/ 3 Periods
		Data Exploration	4 Hours/ 6 Periods
		Modelling	6 Hours/ 9 Periods
Unit III	Neural Network		4 Hours/ 6 Periods
Unit IV	Introduction To Python		70 Hours/ 105 Periods
	Total		112 Hours/ 168 Periods

CBSE Class 9 Artificial Intelligence Syllabus PDF

Unit 1: Introduction To A.I

1.	Excite
	Session: Introduction to Al 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 Al Game Learners to participate in three games based on different Al 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: Al 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.
2.	Relate
	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.
3.	Purpose
	Session: Introduction to sustainable development goals
	Recommended Activity: Go Goals Board Game Learners to answer questions on Sustainable Development Goals
4.	Possibilities
	Session: Theme-based research and Case Studies Learners will listen to various case studies of inspiring start-ups, companies or communities where Al has been involved in real-life. Learners will be allotted a theme around which they need to search for present Al trends and have to visualize the future of Al 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 At is going to transform the nature of jobs and create the Ad accordingly.
5.	Al Ethics
	Video Session: Discussing about Al 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: Al Bias and Al Access Discussing about the possible bias in data collection Discussing about the implications of Al technology
	Recommended Activity: Balloon Debate Students divide in teams of 3 and 2 teams are given same theme. One team goes in affirmation to Al for their section while the other one goes against it. They have to come up with their points as to why Al is beneficial/ harmful for the society.

Unit 2: A. I Project Cycle

1.	Problem Scoping
	Session: Introduction to Al Project Cycle Problem Scoping Data Acquisition Data Exploration Modelling Evaluation
	Activity: Brainstorm around the theme provided and set a goal for the Al Project Discuss various topics within the given theme and select one. List down/ Draw a mind-map 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
3.	Data Acquisition
	Activity: Introduction to data and its types Students work around the scenarios given to them and think of ways to acquire data
4.	Data Exploration
	Session: Data Visualization Need of visualizing data Ways to visualize 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
5.	Modelling
	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 “Al 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 among same alphabets and to differentiate the different ones

Unit 3: 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 receives all information and gives meaningful information as output

Unit 4: 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