Program Objectives:

• Gain confidence in teaching fundamental AI and robotics concepts to young learners.
• Develop skills to design interactive, project-based learning experiences.
• Learn techniques to introduce responsible and ethical AI discussions.
• Enable students to apply AI and robotics to real-world scenarios.
• Design and evaluate student projects for practical and educational outcomes.

What you will learn?

1. Introduction to AI and Robotics

• Overview: Explore foundational AI concepts and applications across industries, along with the core principles of robotics.
• History and Evolution: Key milestones in AI and robotics, from early inventions to modern advancements in technology.
• Hands-On Component: Introductory activity where participants identify common AI applications in everyday life.

2. Programming Basics for AI

• Python Fundamentals: Introduction to Python syntax, basic data types, loops, and conditional statements.
• Coding Exercises: Simple exercises to write programs performing basic tasks, laying the foundation for AI applications.
• Data Handling: Brief introduction to data manipulation in Python, essential for developing AI applications.

3. Fundamentals of AI in Education

• Problem-Solving with AI: Understanding AI’s role in enhancing problem-solving and pattern recognition, with a focus on critical thinking.
• Case Studies: Examples of AI in education, such as adaptive learning platforms, feedback systems, and personalized learning experiences.
• Collaborative Activity: Group activity where participants brainstorm potential applications of AI in educational settings.

4. Integrating AI & Robotics with STEM

• Curriculum Alignment: Approaches for incorporating AI and robotics within the STEM curriculum.
• Workshop: Participants design a lesson plan that integrates basic AI or robotics concepts with STEM learning goals.
• Practical Examples: Review of educational tools and platforms that support AI and robotics learning in STEM.

5. Building a Simple Robot for Classroom Demonstration

• Introduction to Robotics Kits: Overview of popular educational robotics kits, including sensors, motors, and programmable parts.
• Hands-On Assembly: Guided assembly and programming of a basic robot, demonstrating functions like following instructions or responding to touch.
• Classroom Applications: Discussion on ways to engage students with robotics-based activities in a classroom setting.

6. Practical Coding for AI Tools

• Working with AI Models: Introduction to pre-built AI models, such as chatbots, basic image classifiers, and sentiment analysis tools.
• Training and Testing: Basic concepts in using training data, evaluating model accuracy, and interpreting AI outputs.
• Project: Set up a simple chatbot project to illustrate AI in action, allowing participants to customize and interact with AI responses.

7. Classroom Engagement Techniques

• Pedagogical Approaches: Strategies for teaching complex topics like AI and robotics effectively to various age groups.
• Interactive Learning: Techniques for fostering hands-on learning, promoting inquiry-based projects, and encouraging collaborative problem-solving.
• Student Engagement: Tailoring instructional approaches to different learning styles, emphasizing inclusivity and accessibility.

8. Assessment Strategies in AI Education

• Competency-Based Assessment: Approaches for evaluating practical AI and robotics skills, including problem-solving and critical thinking.
• Rubric Design: Crafting clear, objective rubrics for project-based assessments, quizzes, and hands-on activities.
• Feedback Mechanisms: Using AI-enabled tools for continuous formative assessment and constructive feedback.

9. AI Ethics and Responsible Use

• Ethics in AI: Exploration of key ethical considerations in AI, including bias, privacy, transparency, and accountability.
• Classroom Discussion: Facilitated discussion on ethical scenarios in AI, encouraging students to think critically about the social impact of AI technologies.
• Project: Students create a brief presentation on the ethical implications of a chosen AI technology, addressing potential benefits and risks.

10. Project-Based Learning

• • Final Project Design: Structuring a capstone group project where participants develop simple AI or robotics applications.
  • Group Collaboration: Best practices for managing teamwork, assigning roles, and facilitating cooperative learning.
  • Presentation and Review: Group presentations of projects followed by a class discussion, constructive feedback, and a reflection on project experiences.