Class 9  Artificial Intelligence and Robotics  Online Learning.

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AI Curriculum For Class 9th (Inspire And Acquire Module)

Objective of AI Curriculum for Class 9th

The objective of Class IX AI curriculum - which combines both Inspire and Acquire modules is to develop a readiness for understanding and appreciating Artificial Intelligence and its application in our lives. The curriculum focuses on:

  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 the 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.

UNIT

NAME OF THE UNIT

SUB-UNIT

DURATION

PERIODS

1

Introduction to AI

Excite

2 Hours and 40 minutes

4 Periods

Relate

2 Hours

3 Periods

Purpose

2 Hours

3 Periods

Possibilities

2 Hours

3 Periods

AI Ethics

3 Hours and 20 minutes

5 Periods

2

AI Project Cycle

Problem-Scoping

14 Hours

21 Periods

Data-Acquisition

2 Hours

3 Periods

Data Exploration

4 Hours

6 Periods

Modeling

6 Hours

9 Periods

3

Neural Network

 

4 Hours

6 Periods

4

Introduction to Python

 

70 Hours

105 Periods

Total

112 Hours

168 Periods

Robotics -

1. Grabber

  • What is Grabber?
  • Grabber Classification

2. One - motion grabber

  • One fixed one moving grabber
  • Catch grabber
  • Simple grabber
  • Powerful grabber

3. Two motion Grabber

  • X - Y Plane
  • What is a two motion grabber?
  • Using bevel and spur gear
  • Using knob gear

4. Industrial application of Grabber

  • Industrial claw gripping machine
  • Claw bucket machine
  • Excavators
  • Loco shed crane
  • Claw game plucks oil rigs
  • Heavy weight lifting
  • Loader
  • Bulldozer
  • Heavy machine with claw grabber

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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.

Robotics

1. Grabber - The Robotic Grabber arm allows individuals with mobility challenges to pick up objects off of the floor.

  • What is Grabber?
  • Grabber Classification

2. One - motion grabber -robotic arm (not robotic hand) is a type of mechanical arm, usually programmable, with similar functions to a human arm; the arm may be the sum total of the mechanism or may be part of a more complex robot. 

  • One fixed one moving grabber
  • Catch grabber
  • Simple grabber
  • Powerful grabber

3. Two motion Grabber - A coordinate plane is a two-dimensional plane formed by the intersection of a vertical line called y-axis and a horizontal line called x-axis. These are perpendicular lines that intersect each other at zero, and this point is called the origin.

  • X - Y Plane
  • What is a two motion grabber?
  • Using bevel and spur gear
  • Using knob gear

4. Industrial application of Grabber - Typical applications of robots include welding, painting, assembly, disassembly, pick and place for printed circuit boards, packaging and labeling, palletizing, product inspection, and testing; all accomplished with high endurance, speed, and precision. They can assist in material handling.

  • Industrial claw gripping machine
  • Claw bucket machine
  • Excavators
  • Loco shed crane
  • Claw game plucks oil rigs
  • Heavy weight lifting
  • Loader
  • Bulldozer
  • Heavy machine with claw grabber

More Information on School Registration Process of Artificial Intelligence and Robotics Olympiad,follow this link

https://www.schoolconnectonline.com/exam/artificial-intelligence

Register your School in Artificial Intelligence and Robotics Olympiad 2020-21

https://forms.gle/o6weYW8kfQpc5ULV9

Syllabus:
Chapter 1. What is Artificial Intelligence
1. What Is Artificial Intelligence
2. Risks Of Artificial Intelligence
3. History Of Artificial Intelligence
4. Key Events In The History Of Artificial Intelligence
5. Artificial Intelligence Ethics
6. Meaning Of Artificial Intelligence
7. Definition of Artificial Intelligence
8. Machine Learning
9. Neural Networks as Part of Artificial Intelligence
10. Data Science to Produce Artificial Intelligence System
11. Robotics
12. Important Phases In Development Of Artificial Intelligence
13. Types Of Artificial Intelligence
14. Weak Versus Strong Artificial Intelligence
15. Discipline Of Artificial Intelligence
16. Real Life Usage Of Artificial Intelligence
17. Limitations Of Weak Artificial Intelligence
18. Three Domains Of Artificial Intelligence
19. Data in Artificial Intelligence
20. The Relation Between Data Type And Its Usage In Artificial Intelligence Systems
21. Computer Vision
22. Uses Of Computer Vision
23. Natural Language Processing
24. Components Of NLP
25. The Relevance Of Artificial Intelligence In Daily Life
26. Six Dimensions Of Artificial Intelligence
27. Smart Living with Smart Homes To Smart Cities
28. How Smart Homes Connect To Smart Cities
29. Advantages Of Smart Buildings
30. Phases In Development Of Smart Cities
31. Data For Smart Cities to make it Artificially Intelligent
32. Smart Citizens Connecting Individuals With Community Smart Technology
33. Smart Cities Will Help Its Citizens To Have Smarter And Better Lives
34. Using AI To Achieve SDGs
35. The 17 Sustainable Development Goals
36. How AI Is Helping To Achieving SDGs
37. Possibilities Of Artificial Intelligence In Various Fields
38. Everyday Influences Of Artificial Intelligence Globally
39. Real Life Examples Of The Use Of Artificial Intelligence Technology
40. The Ever Changing Focus Of Artificial Intelligence Research
41. Types Of Artificial Intelligence Careers
42. Skill Set Required For Some Artificial Intelligence Related Careers
43. Ethical Concerns Related To Artificial Intelligence Access
44. Ethical Concerns Related To Data Management By Artificial Intelligence
45. Artificial Intelligence Bias In Real World Data
46. The Problem Of Artificial Intelligence Inclusion In Our Lives
47. Problem Of Facts And Their Interpretation With Artificial Intelligence
48. Components Of A Good Artificial Intelligence Policy
49. Ethical Concerns Related To Adoption Of Artificial Intelligence Systems
50. Earning Benefits Of The Usage Of Artificial Intelligence Systems

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