Computer Science & AI

Klassenstufen: Grade 2

Bildungssystem: 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)

#AI_for kids#Machine_Learning for beginners#Grade_2 STEM#Fun_AI activities#Child-friendly_artificial intelligence#Block-based_coding#Beginner_AI course#Logic_and problem solving for kids#eaching_AI concepts with PictoBlox

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

Lernziele:

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.

Module

1. Discovering AI Fundamentals

1. 1. Introduction to Artificial Intelligence

Lernergebnisse:

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

Lernergebnisse:

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

Lernergebnisse:

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

Klassenstufen: Grade 2

Bildungssystem: STEM

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

Lernziele:

Module

1. Discovering AI Fundamentals

1. 1. Introduction to Artificial Intelligence

Lernergebnisse:

1. 2. History and Evolution of AI

Lernergebnisse:

1. 3. Exploring Machine Learning Basics

Lernergebnisse:

Computer Science & AI

Klassenstufen: Grade 2

Bildungssystem: STEM

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

Lernziele:

Module

1. Discovering AI Fundamentals

1. 1. Introduction to Artificial Intelligence

Lernergebnisse:

1. 2. History and Evolution of AI

Lernergebnisse:

1. 3. Exploring Machine Learning Basics

Lernergebnisse:

1. 4. Key Terms and Concepts in AI

Lernergebnisse:

1. 5. Fundamentals of Data Science and Data Handling

Lernergebnisse:

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

Lernziele:

Module

1. Machine Learning in Action with PictoBlox

1. 1. Understanding the ML Process

Lernergebnisse:

1. 2. Building Simple ML Models using PictoBlox

Lernergebnisse:

1. 3. Evaluating ML Model Performance

Lernergebnisse:

1. 4. Hyperparameter Tuning and Model Validation

Lernergebnisse:

1. 5. Data Visualization and Interpretation in ML

Lernergebnisse:

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

Lernziele:

Module

1. Creative AI Project Design with PictoBlox

1. 1. Project Planning and Design

Lernergebnisse:

1. 2. Developing Interactive Games and Stories

Lernergebnisse:

1. 3. Incorporating Input Devices in Projects

Lernergebnisse:

1. 4. Iterative Prototyping and Feedback Integration

Lernergebnisse:

1. 5. Advanced Gamification and Storytelling Techniques

Lernergebnisse:

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

Lernziele:

Module

1. Integrating and Testing AI Systems

1. 1. Sensor Integration Techniques

Lernergebnisse:

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

Lernergebnisse:

1. 3. Debugging and Optimizing AI Systems

Lernergebnisse:

1. 4. System Integration and Deployment Testing

Lernergebnisse:

1. 5. Stress Testing and Scalability Assessment

Lernergebnisse:

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

Lernziele:

Module

1. Optimizing AI Algorithms for Enhanced Problem Solving

1. 1. Assessing AI Algorithm Performance

Lernergebnisse:

1. 2. Refining and Debugging AI Code

Lernergebnisse: