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CBSE Class 9 Artificial Intelligence Syllabus

Artificial Intelligence is an optional subject offered to students that focuses on the technology that creates artificial intelligence or interfaces that perform functions to emulate human intelligence and cranial capacity.

Syllabus

The syllabus for Artificial Intelligence for the year of 2022-2023 is divided into 4 parts. The curriculum as well as the marking scheme us given below.
  1. Part A: Employability Skills
    a. Term 1
    • Unit 1: Communication Skills-I
    • Unit 2: Self-Management Skills-I
    • Unit 3: ICT Skills-I
    b. Term 2
    • Unit 4: Entrepreneurial Skills-I
    • Unit 5: Green Skills-I
  2. Part B: Subject Specific Skills
    a. Term 1
    • Unit 1: Introduction to Artificial Intelligence (AI)
    • Unit 2: AI Project Cycle
    b. Term 2
    • Unit 3: Neural Network
    • Unit 4: Introduction to Python
  3. Part C: Practical Work
    • Unit 4: Introduction to Python
    • Practical Examination
    • Viva Voce
  4. Part D:
    • Project Work / Field Visit / Practical File/ Student Portfolio
    • Viva Voce
Marking Scheme
UNITS MAX. MARKS for Theory and Practical
Employability Skills
Unit 1: Communication Skills-I 5
Unit 2: Self-Management Skills-I
Unit 3: ICT Skills-I
Unit 4: Entrepreneurial Skills-I 5
Unit 5: Green Skills-I
Total 10
Subject Specific Skills
Unit 1: Introduction to Artificial Intelligence (AI) 10
Unit 2: AI Project Cycle 10
Unit 3: Neural Network 5
Unit 4: Introduction to Python 15
Total 40
PART C Practical Work Unit 4: Introduction to Python 20
Practical Examination 10
Viva Voce 5
Total 35
PART D Project Work / Field Visit / Practical File/ Student Portfolio 10
Viva Voce 5
Total 15
GRAND TOTAL 100

Detailed Curriculum

PART-A: EMPLOYABILITY SKILLS

Units Duration in Hours
Unit 1: Communication Skills-I 10
Unit 2: Self-management Skills-I 10
Unit 3: Information and Communication Technology Skills-I 10
Unit 4: Entrepreneurial Skills-I 15
Unit 5: Green Skills-I 05
Total 50

PART-B – SUBJECT SPECIFIC SKILLS

  • Unit 1: Introduction to Artificial Intelligence (AI)
  • Unit 2: AI Project Cycle
  • Unit 3: Neural Network
  • Unit 4: Introduction To Python

UNIT 1: INTRODUCTION TO ARTIFICIAL INTELLIGENCE (AI)

SUB-UNIT LEARNING OUTCOMES SESSION / ACTIVITY / PRACTICAL
Excite 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
Ice Breaker Activity: Dream Smart Home idea· Learners to design a rough layout of floor plan of their dream smart home.
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: 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 undergo an assessment for analysing progress towards acquired AI-Readiness skills. Recommended Activity: AI Quiz (Paper Pen/Online Quiz)
To imagine, examine and reflect on the skills required for futuristic job opportunities. 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
SUB-UNIT LEARNING OUTCOMES SESSION / ACTIVITY / PRACTICAL
Relate Learners to relate to application of Artificial Intelligence in their daily lives. Video Session: To watch a video· Introducing the concept of Smart Cities, Smart Schools and Smart Homes
To unleash their imagination towards smart homes and build an interactive story around it. To relate, apply and reflect on the Human-Machine Interactions. 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.
Purpose To understand the impact of Artificial Intelligence on Sustainable Development Goals to develop responsible citizenship.
Session: Introduction to UN Sustainable Development Goals
Recommended Activity: Go Goals Board Game· Learners to answer questions on Sustainable Development Goals
Possibilities 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. 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 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 To understand and reflect on the ethical issues around AI. To gain awareness around AI bias and AI access. To let the students analyse the advantages and disadvantages of Artificial Intelligence. 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 2: AI PROJECT CYCLE:

SUB-UNIT LEARNING OUTCOMES SESSION / ACTIVITY / PRACTICAL
Problem Scoping Identify the AI Project Cycle framework. Learn problem scoping and ways to set goals for an AI project. Identify stakeholders involved in the problem scoped. Brainstorm on the ethical issues involved around the problem selected. 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. 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 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?
Share what the students have discussed so far. Presentation: Presenting the goal, actions and data.
Data Acquisition Identify data requirements and find reliable sources to obtain relevant data. Activity: Introduction to data and its types. Students work around the scenarios given to them and think of ways to acquire data.
Data Exploration To understand the purpose of Data Visualisation Use various types of graphs to visualise acquired data. 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.
SUB-UNIT LEARNING OUTCOMES SESSION / ACTIVITY / PRACTICAL
Modelling Understand, create and implement the concept of Decision Trees. Understand and visualise computer’s ability to identify alphabets and handwritings.
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 analyse a pattern amongst same alphabets and to differentiate the different ones.

UNIT 4: INTRODUCTION TO PYTHON:

LEARNING OUTCOMES SESSION / ACTIVITY / PRACTICAL
Learn basic programming skills through gamified platforms. Recommended Activity:
  • Introduction to programming using Online Gaming portals like Code Combat.
Acquire introductory Python programming skills in a very user-friendly format.
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.
Objectives
  1. Helping learners understand the world of Artificial Intelligence and its applications through games, activities and multi-sensorial learning to become AI-Ready.
  2. Introducing the learners to three domains of AI in an age-appropriate manner.
  3. Allowing the learners to construct meaning of AI through interactive participation and engaging hands-on activities.
  4. Introducing the learners to AI Project Cycle.
  5. Introducing the learners to programming skills - Basic python coding language.
Learning Outcomes Learners will be able to
  1. Identify and appreciate Artificial Intelligence and describe its applications in daily life.
  2. 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 and Undergo assessment for analysing their progress towards acquired AI-Readiness skills.
  3. Imagine, examine and reflect on the skills required for futuristic job opportunities.
  4. Unleash their imagination towards smart homes and build an interactive story around it.
  5. Understand the impact of Artificial Intelligence on Sustainable Development Goals to develop responsible citizenship.
  6. Research and develop awareness of skills required for jobs of the future.
  7. Gain awareness about AI bias and AI access and describe the potential ethical considerations of AI.
  8. Develop effective communication and collaborative work skills.
  9. Get familiar and motivated towards Artificial Intelligence and Identify the AI Project Cycle framework.
  10. Learn problem scoping and ways to set goals for an AI project and understand the iterative nature of problem scoping in the AI project cycle.
  11. Brainstorm on the ethical issues involved around the problem selected.
  12. Foresee the kind of data required and the kind of analysis to be done, identify data requirements and find reliable sources to obtain relevant data.
  13. Use various types of graphs to visualize acquired data.
  14. Understand, create and implement the concept of Decision Trees.
  15. Understand and visualize computer’s ability to identify alphabets and handwritings.
  16. Understand and appreciate the concept of Neural Network through gamification and learn basic programming skills through gamified platforms.
  17. Acquire introductory Python programming skills in a very user-friendly format.