CBSE Class 9 Artificial Intelligence Syllabus 2019-20

With its aim to make school students well-versed in technologies, the Central Board of Secondary Education (CBSE) this year introduced artificial intelligence as an elective subject in class 8th and 9th. Here, we are presenting the CBSE syllabus for Class 9 Artificial Intelligence (AI). Students may read the content or download the complete syllabus in PDF.

Artificial intelligence is the ability of a machine to think, learn and perform tasks normally requiring human intelligence, such as visual perception, speech recognition and decision-making skills. CBSE curriculum for Class 9 Artificial Intelligence has been designed to develop an understanding and appreciating Artificial Intelligence and its application in our lives.

CBSE Class 9 Artificial Intelligence: Unit Wise Distribution


CBSE Class 9 Syllabus 2019-20: All Subjects

CBSE Class 9 Artificial Intelligence: Course Outline

UNIT 1: INTRODUCTION TO AI

Sub unit

Session / Activity / Practical

Learning Outcomes

Excite

 

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

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

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)

To relate, apply and reflect on the Human-Machine Interactions.

To identify and interact with the three domains of AI: Data, Computer Vision and Natural Language Processing.

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

To undergo an assessment for analysing progress towards acquired AI-Readiness skills.

Recommended Activity: To write a letter

Writing a Letter to one’s future self

• Learners 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

To imagine, examine and reflect on the skills required for futuristic job opportunities.

Relate

Video Session: To watch a video

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

Learners to relate to application of Artificial Intelligence in their daily lives.

Purpose

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.

To unleash their imagination towards smart homes and build an interactive story around it.

To relate, apply and reflect on the Human-Machine Interactions.

Session: Introduction to sustainable development goals

To understand the impact of Artificial Intelligence on Sustainable Development Goals to develop responsible citizenship.

Recommended Activity: Go Goals Board Game

Learners to answer questions on Sustainable Development Goals

Possibilities

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 visualise the future of AI in and around their respective theme

To research and develop awareness of skills required for jobs of the future.

 

To imagine, examine and reflect on the skills required for the futuristic opportunities.

 

To develop effective communication and collaborative work skills.

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

To understand and reflect on the ethical issues around AI.

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

To gain awareness around AI bias and AI access.

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.

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

UNIT 2: AI PROJECT CYCLE

Sub unit

Session / Activity / Practical

Learning Outcomes

Problem Scoping

Session: Introduction to AI Project Cycle

• Problem Scoping

• Data Acquisition

• Data Exploration

• Modelling

• Evaluation

Identify the AI Project Cycle framework.

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.

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

Identify stakeholders involved in the problem scoped.

Brainstorm on the ethical issues involved around the problem selected.

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?

Understand the iterative nature of problem scoping for in the AI project cycle.

Foresee the kind of data required and the kind of analysis to be done.

Presentation: Presenting the goal, actions and data.

Share what the students have discussed so far.

Data Acquisition

Activity: Introduction to data and its types.

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

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

Data Exploration

Session: Data Visualisation

• Need of visualising data

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

To understand the purpose of Data Visualisation

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.

Use various types of graphs to visualise acquired data.

Modelling

Session: Decision Tree

To introduce basic structure of Decision Trees to students.

Understand, create and implement the concept of Decision Trees.

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 analyse a pattern amongst same alphabets and to differentiate the different ones.

Understand and visualise computer’s ability to identify alphabets and handwritings.

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.

Understand and appreciate the concept of Neural Network through gamification.

 

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 4: INTRODUCTION TO PYTHON

 

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

Learn basic programming skills through gamified platforms.

 

 

 

 

 

 

 

Session: Introduction to Python language

Introducing python programming and its applications

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

 

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.

Multiple Choice Questions for CBSE Class 9 Maths, Science, Social Science and English

CBSE Class 9 Science Chapters-wise Notes

Skills to be Developed:


You may download the complete syllabus from the following link:

CBSE Class 9 Artificial Intelligence Syllabus 2019-20

 

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