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Artificial Intelligence for Kids (Block Coding) – Curriculum

Artificial Intelligence for Kids (Block Coding) – Curriculum

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Introduction

Artificial Intelligence for Kids is an AI curriculum for activity centers, schools, and self-learners for teaching the basic concepts of Artificial Intelligence like computer vision, face detection & recognition, text detection, speech recognition, and machine learning using graphical programming.

Learning Outcomes

After completing this curriculum, students will have a basic understanding of artificial intelligence and machine learning 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

10+

Activities

20+

Learning Hours

Grades

Curriculum Lesson Plan

Lesson 1: Introduction to Artificial Intelligence

Objective: Human intelligence vs. artificial intelligence? Advantages and applications of artificial intelligence. Usage of AI to tackle climate change & global challenges.
Learning Outcome: Understand what is the difference between human intelligence and artificial intelligence | Appreciate the advantages of using AI | Correlate with the real-world applications of AI to generate an AI mindset.
Mode: Theoretical Understanding

Lesson 2: Introduction to PictoBlox

Lesson 3: Computer Vision

Objective: Learn about human vision and computer vision. Its applications. How self-driving car works? Artificial Intelligence extension in PictoBlox.
Activity 1: Identifying celebrities from images.
Activity 2: Locating Objects - Make a script that creates a bounding box on the identified object.
Learning Outcome: Describe Computer Vision | Explain how it works | Use computer vision & coding concept inside projects.
Mode: Practical (Artificial Intelligence)

Lesson 4: Face Detection

Objective: Learn about face detection in image. Face detection blocks in PictoBlox. How facial recognition works?
Activity 1: Make a script that detects the face using the camera and reports the expression of all the faces detected on the stage.
Learning Outcome: Describe Face Detection | Explain how it works | Use face detection & coding concepts inside projects.
Mode: Practical (Artificial Intelligence)

Lesson 5: Optical Character Recognition

Objective: Learn about optical character recognition. Handwritten text recognition, Printed text recognition. Application of text recognition.
Activity 1: Make an AI project in PictoBlox that detects the receiver’s name of a gift from the image and sends it to the receiver. So as you know, Amazon also uses the same to automate its delivery system.
Learning Outcome: Describe OCR | Explain how it works | Use OCR technique & coding concepts inside projects.
Mode: Practical (Artificial Intelligence)

Lesson 6: Speech Recognition

Objective: Understand how speech recognition works. Use AI blocks in PictoBlox to convert speech into text and use in projects.
Activity 1: Make your own virtual assistant in PictoBlox which recognizes your that can recognize your command play the requested song.
Learning Outcome: Understand how AI delivery bots work | Appreciate the advantages of AI delivery bots.
Mode: Practical (Artificial Intelligence)

Lesson 7: Machine Learning

Objective: Learn what is machine learning. Teachable machines for training & creating ML models in PictoBlox.
Activity 1: Make an ML model that classifies an image as a cat or a dog.
Learning Outcome: Understand how machine learning similar is to human learning | Understand how does machine learning cycle works | Make ML models using images in Teachable Machine | Use ML models in PictoBlox to make ML projects.
Mode: Practical (Machine Learning)

Lesson 8: Rock Paper Scissors with AI – Part 1

Objective: In this project you will train the machine to identify the player move from the camera feed and choose the AI’s move randomly. Then, Tobi will decide who won the round and keep track of the score. Whoever wins three rounds first wins the game.
Activity 1: Training the Model
Activity 2: Rock Paper Scissors Script – Part 1
Learning Outcome: Understand create a game using ML.
Mode: Practical (Machine Learning)

Lesson 9: Rock Paper Scissor with AI – Part 2

Objective: In this project you will train the machine to identify the player move from the camera feed and choose the AI’s move randomly. Then, Tobi will decide who won the round and keep track of the score. Whoever wins three rounds first wins the game.
Activity 1: Rock Paper Scissor with AI
Learning Outcome: Understand create a game using ML.
Mode: Practical (Machine Learning)

Lesson 10: Pose Classifier

Objective: How does the pose detection works? Pose Machine Learning model in PictoBlox.
Activity 1: Create a project in Pictoblox in which Tobi identifies “Hi” and the “Namaste” gesture and greets back accordingly.
Learning Outcome: Detect different poses from an image | Build a pose model in Teachable Machine to classify different poses | Use the pose blocks in PictoBlox to make ML projects.
Mode: Practical (Machine Learning)

Lesson 11: Ethics

Lesson 12: Capstone Project

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Artificial Intelligence for Kids (Block Coding) – Curriculum

Introduction

Learning Outcomes

Curriculum Lesson Plan

Lesson 1: Introduction to Artificial Intelligence

Lesson 2: Introduction to PictoBlox

Lesson 3: Computer Vision

Lesson 4: Face Detection

Lesson 5: Optical Character Recognition

Lesson 6: Speech Recognition

Lesson 7: Machine Learning

Lesson 8: Rock Paper Scissors with AI – Part 1

Lesson 9: Rock Paper Scissor with AI – Part 2

Lesson 10: Pose Classifier

Lesson 11: Ethics

Lesson 12: Capstone Project

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