Advanced AI and Robotics Teaching Certification for Class 11-12 Teachers

Empower Future Innovators: AI and Robotics Certification for Teachers

MODE
Mode(Online) TYPE
Self Paced LEVEL
Moderate

Skills you will gain:

Designed for educators, this program covers AI basics, robotics principles, and hands-on learning strategies. Teachers will learn methods for integrating AI and robotics into the curriculum, using interactive and project-based approaches. Emphasis is placed on building problem-solving and critical thinking skills in students, aligning with modern STEM education standards.

Aim: This certification equips teachers with advanced AI and robotics skills to engage students in Grades 11-12. The focus is on developing foundational understanding and practical applications of AI and robotics, preparing educators to foster student interest in these emerging fields.

Program Objectives:

Master AI and robotics concepts tailored for high school students.
Develop project-based learning modules for interactive teaching.
Understand ethical issues in AI and promote responsible use.
Equip students with problem-solving skills using AI and robotics.
Learn curriculum design and student assessment strategies.

What you will learn?

1. Advanced AI and Machine Learning Concepts

Deep Learning Fundamentals: Introduction to neural networks, backpropagation, and multi-layer perceptrons.
Supervised vs. Unsupervised Learning: Key differences, practical applications, and use cases.
Advanced Models: Explore convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning.
Hands-On: Build and train deep learning models using real-world datasets.

2. AI Algorithms for Robotics

Integrating AI with Robotics: How machine learning algorithms (e.g., decision trees, k-means clustering) enhance robotics capabilities.
Autonomous Systems: Apply machine learning to robot vision, pathfinding, and object recognition.
Workshop: Implement AI algorithms to control robotic systems in a dynamic environment.

3. Python for AI

Advanced Python Programming: Focus on Python libraries like TensorFlow, Keras, and PyTorch for AI development.
Data Processing and Analysis: Use NumPy, Pandas, and Matplotlib to process, visualize, and interpret data.
Hands-On: Build and fine-tune neural networks using TensorFlow; explore practical AI projects.

4. Teaching AI as a Subject

Curriculum Development: Designing an AI and robotics curriculum that aligns with NEP 2020 and NCERT guidelines.
Standards Integration: Mapping AI concepts to key learning standards in computer science and STEM.
Classroom Application: Develop lesson plans and classroom activities that make AI accessible to students at different levels.

5. Advanced Robotics Systems

Autonomous Robotics: Building robots capable of navigating environments, recognizing objects, and performing tasks without human input.
Microcontrollers and Sensors: Programming microcontrollers like Arduino and Raspberry Pi to control robotics systems.
Hands-On Projects: Build an autonomous robot using sensors (ultrasonic, IR, etc.) and motors; program it to complete tasks.

6. AI in Real-World Applications

AI in Healthcare: Explore how AI supports diagnostics, patient care, and medical research.
AI in Finance: Learn how AI is used in fraud detection, risk management, and algorithmic trading.
AI for Sustainability: Examine AI’s role in climate modeling, resource management, and smart cities.
Case Studies: Analyze real-world applications where AI is making an impact across various sectors.

7. Ethics in AI and Robotics

Ethical Frameworks: Discuss the ethical considerations around AI and robotics, including bias, accountability, and transparency.
Societal Implications: Understand the potential consequences of widespread AI and robotics adoption, including job displacement, privacy concerns, and governance.
Classroom Discussion: Facilitate discussions on ethical dilemmas in AI, encouraging critical thinking and responsible use.

8. Assessment and Project-Based Learning

Competency-Based Assessment: Strategies for designing assessments that measure practical AI and robotics skills.
Project-Based Learning: Incorporate real-world AI projects into the classroom, fostering problem-solving and innovation.
Student-Led Projects: Guide students in designing and implementing their AI or robotics projects, from concept to prototype.

9. AI for Innovation and Research

Encouraging AI Research: Inspire students to engage in research and contribute to the growing field of AI.
Promoting Innovation: Foster a culture of innovation by encouraging creative problem-solving and interdisciplinary thinking.
Classroom Activities: Organize research-based AI challenges to spark interest in innovation and academic inquiry.

10. Preparing Students for AI Competitions

- Competition Overview: Introduction to national and international AI and robotics competitions (e.g., World Robot Olympiad, AI4ALL).
- Project Development: Provide guidance on selecting, designing, and refining AI and robotics projects for competitions.
- Skills Development: Focus on the technical, analytical, and teamwork skills necessary to succeed in competitive environments.

Intended For :

Basic experience with STEM subjects and programming. Familiarity with basic Python is essential. Teaching experience in senior secondary classes is recommended.