Class 11 – Coding, AI and Robotics Curriculum

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Introduction

Certified AI curriculum is aligned with international boards like CBSE / IB / IGCSE / ICSE / CSTA / CIE and specifically designed grade-wise AI syllabus for students of grade 5 to introduce them to technological activities in Artificial Intelligence, Robotics, Coding, and much more.

Learning Outcomes

After completing this curriculum, students will have a basic understanding of robotics, coding, and artificial intelligence with the help of a wide variety of projects.

Apart from the aforementioned learning outcomes, this curriculum will also help them develop important skills such as problem-solving and debugging techniques, critical thinking, logical reasoning, and creativity.

Lessons

30+

Activities

50+

Learning Hours

Grade

Curriculum Lesson Plan

Module 1: Artificial Intelligence

Lesson 1: Introduction to Python

Objective: Learn about the main concept of programming, and it’s applications in the real world. Describe, about Python programming language, and how Python codes can be written in Pictoblox Python interface.
Activity 1: This activity teaches to Identify different sections of the PictoBlox Python interface as well as the palettes in it, and start their journey in coding.
Mode: Practical (Coding Basics)

Lesson 2: Lists and Strings in Python

Lesson 3: Introduction to Artificial Intelligence

Objective: Learn about the concept of Artificial Intelligence. Learn about different concepts of AI. Learn where AI is used in real world.
Activity 1: This activity teaches the basic overview about the differences between human intelligence and artificial intelligence, exploring the advantages and applications of artificial intelligence.
Mode: Practical (Coding Basics)

Lesson 4: Types of Data

Lesson 5: Types of Learning Models

Objective: Learn about Different types of Learning Models. Understanding the use of Rule Based Learning and Neural Networks.
Activity 1: This activity teaches about about Rule-Based and Learning Based AI Approaches, Supervised, Unsupervised, and Reinforcement Learning Models, and Neural Networks in the class.
Mode: Practical (Coding Basics)

Lesson 6: AI Application and Methodology

Lesson 7: Introduction to Matrices and Set Theory

Lesson 8: Math for AI Part – 1

Lesson 9: Math for AI Part – 2

Lesson 10: AI Values: Ethical Decision Making

Objective: Learn about what are the values that need to be considered before implementing a model or a project and grasp what are the boundaries that should be evaluated before making a model.
Activity 1: Thia activity teaches Boundaries in AI, Bias in AI, and Proper techniques in AI.
Mode: Practical (Coding Basics)

Lesson 11: Story Telling

Objective: Learn about he importance of representing data and how data representation is a crucial and important step to ensure thorough understanding of the model to everyone.
Activity 1: This activity teaches how to AI storytelling AI in courts Use case of AI storytelling.
Mode: Practical (Coding Basics)

Lesson 12: Introduction to Numpy and Pandas

Lesson 13: Critical and Creative Thinking

Objective: Learn about how Critical and Creative thinking is important to develop models in Machine Learning and Artificial Intelligence.
Activity 1: This activity teaches common approaches for creative thinking and design, such as brainstorming, mind mapping, prototype testing, reverse engineering, and creative constraints.
Mode: Practical (Coding Basics)

Module 2: Robotics

Lesson 14: AI Based Self Driving Car Part – 1

Objective: Learn about Importance and potential benefits of self-driving cars, including improved safety, increased efficiency, improved accessibility, and reduced environmental impact.
Activity 1: Thia activity teaches how self-driving cars work and what they can be used for.
Mode: Practical (Coding Basics)

Lesson 15: AI Based Self Driving Car Part – 2

Objective: Learn about the concept, potential benefits, and applications of self-driving cars, as well as to provide an example of an activity related to sign detection with AI.
Activity 1: This activity teaches how self-driving cars work and we will implement a small code to detect the signs at first that can be used for autonomous driving.
Mode: Practical (Coding Basics)

Lesson 16: LED Looping Patterns with Quarky – 2

Lesson 17: AI Based Waste Collector Part – 1

Objective: Learn about waste-collecting bots and how they are helpful to society, along with creating a model that will identify and classify objects from the camera.
Activity 1: This activity teaches waste and differentiate types of waste i.e. Biodegradable & Non-Biodegradable Waste.
Mode: Practical (Coding Basics)

Lesson 18: AI Based Waste Collector Part – 2

Lesson 19: AI Based Waste Collector Part – 3

Objective: Learn about the two categories: biological and non-biological Demonstrate the sounds and patterns by using Quarky.
Activity 1: This activity teaches how to makes a specific sound and displays a pattern, while if a non-biological object is detected, a different sound and pattern are used.
Mode: Practical (Coding Basics)

Lesson 20: Data Analysis Part – 1

Lesson 21: Data Analysis Part – 2

Objective: Learn about plot the imported data using the given plotting library like Matplotlib along with Panda’s library to plot various types of graphs.
Activity 1: This activity teaches data visualization and Explain the application of data visualization Use Matplotlib and Panda lib in your activity – visualizing data.
Mode: Practical (Coding Basics)

Lesson 22: Data Analysis Part – 3

Objective: Learn about to plot the correlation between two columns of a simple dataset and why it is important to evaluate important features.
Activity 1: This activity teaches The relationship between different columns in a data set can be analyzed to understand how they affect each other. There are several techniques for analyzing the relationship between columns.
Mode: Practical (Coding Basics)

Lesson 23: Regression Part –1

Lesson 24: Regression Part –2

Lesson 25: Classification Part –1

Lesson 26: Classification Part –2

Lesson 27: Classification Part –3

Lesson 28: Clustering

Lesson 29: AI Values – Bias Awareness

Lesson 30: Robot Pet

How to execute this curriculum?

If you are a school, activity center, or institution looking to implement the coding, Artificial Intelligence, and Robotics curriculum, you are then at the right place. STEMpedia provides the right ecosystem to implement it.

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Coding and AI Platform

Get unlimited access to AI-ML projects like face detection, object classification, machine learning models, self-driving car, and speech recognition with PictoBlox – programming software for schools with Block & Python coding environment! Avail of premium features like bulk account creation and assignment submission with AI Lab.

Teacher Development Programs

For delivering Artificial Intelligence and Machine Learning education to students, our master AI trainers will train and upskill the computer science teachers, STEM faculty, and school IT staff. We provide dedicated live training sessions and curated short courses for teachers.

Structured Curriculum & Resources

Certified AI curriculum is aligned with international boards like CBSE/IB/IGCSE/ICSE/CSTA/CIE and specifically designed grade-wise AI syllabus for students of grades 3rd to 12th to introduce them to technological activities in Artificial Intelligence, Robotics, Coding, and much more.

AI and Robotics Lab

Cubicle of knowledge offering AI & Robotics education with a comprehensive curriculum, interactive robots, hands-on projects, and real-life applications!

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Class 11 – Coding, AI and Robotics Curriculum

Introduction

Learning Outcomes

Curriculum Lesson Plan

Module 1: Artificial Intelligence

Lesson 1: Introduction to Python

Lesson 2: Lists and Strings in Python

Lesson 3: Introduction to Artificial Intelligence

Lesson 4: Types of Data

Lesson 5: Types of Learning Models

Lesson 6: AI Application and Methodology

Lesson 7: Introduction to Matrices and Set Theory

Lesson 8: Math for AI Part – 1

Lesson 9: Math for AI Part – 2

Lesson 10: AI Values: Ethical Decision Making

Lesson 11: Story Telling

Lesson 12: Introduction to Numpy and Pandas

Lesson 13: Critical and Creative Thinking

Module 2: Robotics

Lesson 14: AI Based Self Driving Car Part – 1

Lesson 15: AI Based Self Driving Car Part – 2

Lesson 16: LED Looping Patterns with Quarky – 2

Lesson 17: AI Based Waste Collector Part – 1

Lesson 18: AI Based Waste Collector Part – 2

Lesson 19: AI Based Waste Collector Part – 3

Lesson 20: Data Analysis Part – 1

Lesson 21: Data Analysis Part – 2

Lesson 22: Data Analysis Part – 3

Lesson 23: Regression Part –1

Lesson 24: Regression Part –2

Lesson 25: Classification Part –1

Lesson 26: Classification Part –2

Lesson 27: Classification Part –3

Lesson 28: Clustering

Lesson 29: AI Values – Bias Awareness

Lesson 30: Robot Pet

How to execute this curriculum?

AI & Robotics Lab and Classroom Bundles

Coding and AI Platform

Teacher Development Programs

Structured Curriculum & Resources

AI and Robotics Lab

Contact Us

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