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Advanced AI and Robotics Teaching Certification for Class 11-12 Teachers
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Course Overview
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Advanced AI and Robotics Teaching Certification for Class 11-12 Teachers
Professional Certification Program
🎥
FormatLive + Recorded
📅
Duration8 Weeks
📜
CertificationVerified
Program Syllabus
Module 1
About This Course
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.
Module 2
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.
Module 3
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.
Module 4
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.
Module 5
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.
Module 6
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.
Module 7
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.
Module 8
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.
Module 9
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.
Module 10
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.
Module 11
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.
Module 12
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.
Module 13
10. Preparing Students for AI Competitions
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- 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.
Module 14
Who Should Enrol?
Basic experience with STEM subjects and programming. Familiarity with basic Python is essential. Teaching experience in senior secondary classes is recommended.
Module 15
Program Outcomes
- Confidence in teaching AI and robotics to high school students.
- Skills in developing and delivering AI-driven lesson plans.
- Enhanced knowledge of responsible AI practices in education.
- Hands-on project experience to engage students in practical learning.
Module 17
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
Module 18
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Module 19
Publication Opportunity
Get published in a prestigious open-access journal.
Module 20
Centre of Excellence
Become part of an elite research community.
Module 21
Networking & Learning
Connect with global researchers and mentors.
Module 22
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
Module 23
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Lead Instructor
Dr. Sarah Chen
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
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