Class 12 – Coding, AI and Robotics Curriculum

Warning: Undefined array key "pp_wrapper_link" in /var/www/html/wp-content/plugins/powerpack-elements/extensions/wrapper-link.php on line 194

Warning: Undefined array key "pp_wrapper_link_enable" in /var/www/html/wp-content/plugins/powerpack-elements/extensions/wrapper-link.php on line 196

Warning: Undefined array key "pp_custom_cursor_icon" in /var/www/html/wp-content/plugins/powerpack-elements/extensions/custom-cursor.php on line 350

Warning: Undefined array key "pp_custom_cursor_text" in /var/www/html/wp-content/plugins/powerpack-elements/extensions/custom-cursor.php on line 351

Warning: Undefined array key "pp_custom_cursor_target" in /var/www/html/wp-content/plugins/powerpack-elements/extensions/custom-cursor.php on line 352

Warning: Undefined array key "pp_custom_cursor_css_selector" in /var/www/html/wp-content/plugins/powerpack-elements/extensions/custom-cursor.php on line 353

Warning: Undefined array key "pp_custom_cursor_enable" in /var/www/html/wp-content/plugins/powerpack-elements/extensions/custom-cursor.php on line 355

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 12 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 in python.

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 Artificial Intelligence

Lesson 2: Introduction to Python

Lesson 3: Data Identification

Lesson 4: Data Acquisition

Lesson 5: Modelling Approach

Objective: Learn about the steps of the AI Modelling. Understanding the use of Rule Based Learning and Neural Networks.
Activity 1: This activity teaches 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: Model Validation -Part 1

Lesson 7: Model Validation -Part 2

Lesson 8: Model Validation -Part 3

Lesson 9: Model Evaluation- Part 1

Objective: Learn about Evaluation Metrics. Be able to Use Evaluation Metrics in Machine Learning.
Activity 1: This activity teaches about model evaluation techniques and understand important terms like Precision, Recall, Accuracy, and F1-score along with implementing each one of them.
Mode: Practical (Coding Basics)

Lesson 10: Model Evaluation- Part 2

Lesson 11: Introduction to Machine Learning Algorithm -Part 1

Lesson 12: Introduction to Machine Learning Algorithm -Part 2

Lesson 13: Introduction to Machine Learning Algorithm -Part 3

Objective: Learn about understanding of KNN and the steps involved and Know how to use KNN in Machine Learning.
Activity 1: This activity teaches about K Nearest Neighbor Classification Model. They will understand how the model works and will also implement it in the Python Interface of the Pictoblox Software.
Mode: Practical (Coding Basics)

Lesson 14: Hyperparameter Manipulation

Objective: Learn about how hyperparameters affect ML Models. Be able to tweak hyperparameters in the ML Environment.
Activity 1: This activity teaches about Hyperparameters and what role they play in the model accuracy and predictions. The same will also be applied in the ML Environment of the Pictoblox software.
Mode: Practical (Coding Basics)

Lesson 15: Machine Learning in Jupyter Notebooks

Lesson 16: Life Cycle of AI Model

Lesson 17: Life Cycle of AI Model- Regression

Lesson 18: Life Cycle of AI Model- Regression Evaluation

Module 2: Robotics

Lesson 19: Wirelessly Controlled Robot

Lesson 20: Robotic Motion

Objective: Learn about Quarky’s various movement patterns. Demonstrate robot movement in a square. Demonstrate robot movement in a circle.
Activity 1: Thia activity teaches how to make a square and a circle with the robot (Quarky) by adjusting the time and speed of the motors.
Mode: Practical (Coding Basics)

Lesson 21: Gesture Controlled Robot Model Creation

Lesson 22: Gesture Controlled Robot

Lesson 23: Gripper Robot Assembly and Control

Lesson 24: Gripper Robot Programming

Lesson 25: Decision Tree vs Random Forest

Objective: Learn about basic understanding of the Decision Tree and Random Forest algorithms. Understand the basic differences between the two.
Activity 1: This activity teaches about the structural differences between Decision Trees and Random Forests, as well as when to use each model.
Mode: Practical (Coding Basics)

Lesson 26: Model Comparison – Part 1

Lesson 27: Model Comparison – Part 2

Lesson 28: Model Comparison – Part 3

Objective: Learn about the fundamental differences between Decision Trees and Random Forest. Gain a working understanding of ensemble models and how Random Forest deals with data.
Activity 1: Thia activity teaches how the specifics about Decision Tree and Random Forest and understand the use cases in which they both thrive.
Mode: Practical (Coding Basics)

Lesson 29: Relevant Data Representation -Part 1

Lesson 30: Relevant Data Representation -Part 2

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.

AI & Robotics Lab and Classroom Bundles

Equip your school with a Next-Gen AI laboratory along with the best AI-learning kits Quarky, DIY kits like Mars Rover and Humanoid, consumables, and hands-on projects and activities making your students AI learning journey superfun!

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!

Contact Us

Implement this curriculum at your School / Activity Center!

Name

Phone Number

Country

School / Organization Name

Message

Class 12 – Coding, AI and Robotics Curriculum

Introduction

Learning Outcomes

Curriculum Lesson Plan

Module 1: Artificial Intelligence

Lesson 1: Introduction to Artificial Intelligence

Lesson 2: Introduction to Python

Lesson 3: Data Identification

Lesson 4: Data Acquisition

Lesson 5: Modelling Approach

Lesson 6: Model Validation -Part 1

Lesson 7: Model Validation -Part 2

Lesson 8: Model Validation -Part 3

Lesson 9: Model Evaluation- Part 1

Lesson 10: Model Evaluation- Part 2

Lesson 11: Introduction to Machine Learning Algorithm -Part 1

Lesson 12: Introduction to Machine Learning Algorithm -Part 2

Lesson 13: Introduction to Machine Learning Algorithm -Part 3

Lesson 14: Hyperparameter Manipulation

Lesson 15: Machine Learning in Jupyter Notebooks

Lesson 16: Life Cycle of AI Model

Lesson 17: Life Cycle of AI Model- Regression

Lesson 18: Life Cycle of AI Model- Regression Evaluation

Module 2: Robotics

Lesson 19: Wirelessly Controlled Robot

Lesson 20: Robotic Motion

Lesson 21: Gesture Controlled Robot Model Creation

Lesson 22: Gesture Controlled Robot

Lesson 23: Gripper Robot Assembly and Control

Lesson 24: Gripper Robot Programming

Lesson 25: Decision Tree vs Random Forest

Lesson 26: Model Comparison – Part 1

Lesson 27: Model Comparison – Part 2

Lesson 28: Model Comparison – Part 3

Lesson 29: Relevant Data Representation -Part 1

Lesson 30: Relevant Data Representation -Part 2

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

Explore Other AI & Robotics Class-wise Curriculum

Class 3 – Coding, AI and Robotics Curriculum

Class 4 – Coding, AI and Robotics Curriculum

Class 5 – Coding, AI and Robotics Curriculum

Class 6 – Coding, AI and Robotics Curriculum

Class 7 – Coding, AI and Robotics Curriculum

Class 8 – Coding, AI and Robotics Curriculum

Class 9 – Coding, AI and Robotics Curriculum

Class 10 – Coding, AI and Robotics Curriculum

Class 11 – Coding, AI and Robotics Curriculum

Company

Community

Products

School Programs

Impact Programs

Learning Resources

Product Documentation

Get in Touch

Follow Us

PictoBlox - Block & Python Coding for Kids

Dabble App - One App. Infinite Control.