Computer Science & AI

Grade Levels: Grade 2

Educational System: STEM

Welcome to "AI Explorers" – a beginner-friendly, exciting journey into the world of Artificial Intelligence (AI) and Machine Learning (ML) designed especially for curious young minds in Grade 2! In this course, students will dive into the amazing world of AI using PictoBlox, a child-friendly, block-based programming platform. Through colorful blocks and interactive projects, kids will explore key AI concepts like image recognition, voice interaction, and smart decision-making — all in a playful, visual, and hands-on way. They’ll learn how computers can see, hear, and even think, just like humans. From training a computer to recognize different emotions to building their own smart assistant, students will develop logical thinking, creativity, and 21st-century tech skills while having tons of fun! No previous experience with programming or AI is required — just imagination and a willingness to explore! What Kids Will Learn: What is AI and how it works (in simple language) Introduction to Machine Learning Using PictoBlox to build AI projects Train computers to recognize faces, objects, and sounds Create smart, interactive games and stories Develop logical thinking, problem-solving, and creativity Tools Used: PictoBlox (block-based coding interface) Laptop/Tablet with internet connection Webcam and microphone (for AI features like image and voice detection)

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1. Define and explain core Artificial Intelligence (AI) and Machine Learning (ML) concepts.

Learning Targets:

1. List and describe key AI terms such as data, algorithm, model, training, and prediction.

2. Differentiate between AI and ML using clear, simple examples.

3. Explain fundamental workings of AI models and decision-making processes in measurable terms.

4. Demonstrate understanding of technical vocabulary with precise definitions and examples.

Modules

1. Discovering AI Fundamentals

1. 1. Introduction to Artificial Intelligence

Learning Outcomes:

1. Define artificial intelligence using precise and measurable terminology.

2. Identify major components of AI systems including data, algorithms, and hardware.

3. Explain the significance of AI in modern technology through observable examples.

4. Differentiate between rule-based and learning-based AI approaches using clear comparisons.

1. 2. History and Evolution of AI

Learning Outcomes:

1. Chronicle key milestones in AI development with specific dates and events.

2. Analyze paradigm shifts in AI history using measurable case studies.

3. Compare early AI models with contemporary applications using observable criteria.

4. Evaluate the impact of historical developments on current AI trends.

1. 3. Exploring Machine Learning Basics

Learning Outcomes:

1. Define machine learning and explain its relationship with AI using clear examples.

2. Illustrate supervised, unsupervised, and reinforcement learning using measurable distinctions.

3. Demonstrate training and testing phases through block-based simulations.

4. Identify differences between machine learning and traditional programming with observable comparisons.

Computer Science & AI

Computer Science & AI

Grade Levels: Grade 2

Educational System: STEM

1. Define and explain core Artificial Intelligence (AI) and Machine Learning (ML) concepts.

Learning Targets:

Modules

1. Discovering AI Fundamentals

1. 1. Introduction to Artificial Intelligence

Learning Outcomes:

1. 2. History and Evolution of AI

Learning Outcomes:

1. 3. Exploring Machine Learning Basics

Learning Outcomes:

Computer Science & AI

Grade Levels: Grade 2

Educational System: STEM

1. Define and explain core Artificial Intelligence (AI) and Machine Learning (ML) concepts.

Learning Targets:

Modules

1. Discovering AI Fundamentals

1. 1. Introduction to Artificial Intelligence

Learning Outcomes:

1. 2. History and Evolution of AI

Learning Outcomes:

1. 3. Exploring Machine Learning Basics

Learning Outcomes:

1. 4. Key Terms and Concepts in AI

Learning Outcomes:

1. 5. Fundamentals of Data Science and Data Handling

Learning Outcomes:

2. Analyze and apply basic Machine Learning principles using a block-based programming approach.

Learning Targets:

Modules

1. Machine Learning in Action with PictoBlox

1. 1. Understanding the ML Process

Learning Outcomes:

1. 2. Building Simple ML Models using PictoBlox

Learning Outcomes:

1. 3. Evaluating ML Model Performance

Learning Outcomes:

1. 4. Hyperparameter Tuning and Model Validation

Learning Outcomes:

1. 5. Data Visualization and Interpretation in ML

Learning Outcomes:

3. Design and develop interactive AI projects using block-based coding platforms.

Learning Targets:

Modules

1. Creative AI Project Design with PictoBlox

1. 1. Project Planning and Design

Learning Outcomes:

1. 2. Developing Interactive Games and Stories

Learning Outcomes:

1. 3. Incorporating Input Devices in Projects

Learning Outcomes:

1. 4. Iterative Prototyping and Feedback Integration

Learning Outcomes:

1. 5. Advanced Gamification and Storytelling Techniques

Learning Outcomes:

4. Execute and evaluate AI systems by integrating sensor inputs and advanced block-based logic.

Learning Targets:

Modules

1. Integrating and Testing AI Systems

1. 1. Sensor Integration Techniques

Learning Outcomes:

1. 2. Advanced Block-Based Logic in AI Projects

Learning Outcomes:

1. 3. Debugging and Optimizing AI Systems

Learning Outcomes:

1. 4. System Integration and Deployment Testing

Learning Outcomes:

1. 5. Stress Testing and Scalability Assessment

Learning Outcomes:

5. Critically evaluate and refine AI algorithms to boost problem-solving and decision-making abilities.

Learning Targets:

Modules

1. Optimizing AI Algorithms for Enhanced Problem Solving

1. 1. Assessing AI Algorithm Performance

Learning Outcomes:

1. 2. Refining and Debugging AI Code

Learning Outcomes: