High School Artificial Intelligence

Course Objectives:

Introduce the fundamentals of Artificial Intelligence, its types, workflow, real-world applications, and key domains like CV, NLP, and Data Science.
Develop core data literacy skills, including data acquisition, preprocessing, interpretation, and secure handling using both code and no-code tools.
Explore AI modelling techniques, covering rule-based and learning-based models, neural networks, and key evaluation metrics.
Provide hands-on experience in AI applications, especially in Computer Vision and Natural Language Processing, through practical tools and projects.
Foster responsible AI development, by understanding the AI Project Cycle, ethical frameworks, AI for sustainability, and societal impact.

Syllabus

Module 1: Foundations of Artificial Intelligence

What is Artificial Intelligence?
Difference between AI and Automation
Applications of AI in everyday life
How AI works: Basic AI workflow
Types of AI: Narrow AI, General AI, Super AI
Introduction to domains of AI: Computer Vision, Natural Language Processing, Data Science
Importance of Mathematics in AI
- Basics of Statistics and Probability
- Role of Math in AI decision-making
Exercises
- Objective Type Questions
- Subjective Types Questions
Activities
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Module 2: Data Literacy and Understanding Data

What is Data Literacy?
Impact of data literacy in AI
Types and sources of data
Data security, privacy, and cyber safety best practices
Data acquisition and pre-processing
Importance of data interpretation
Interactive Dashboard and Visualization
- No-Code tools for creating visual charts
- Use Case: Orange Data Mining (Palmer Penguins dataset)
Real-life examples: Recommendation systems, AI assistants
Exercises
- Objective Type Questions
- Subjective Types Questions
Activities
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Module 3: Modelling in AI

AI, ML, and DL
- Key differences and relationships
- Common terminologies used in ML
Types of AI models:
- Rule-based and Learning-based AI
- Supervised, Unsupervised, and Reinforcement learning
- Subcategories: Classification, Regression, Clustering, Association
Neural Networks
- Concept and how machines make decisions
Evaluation of AI Models:
- Train-Test Split
- Accuracy, Error
- Confusion Matrix
- Precision, Recall, F1 Score
Tools and Activities:
- Teachable Machine
- Infinite Drum Machine
- Containment Zone Prediction Model
Exercises
- Objective Type Questions
- Subjective Types Questions
Activities
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Module 4: Computer Vision

What is Computer Vision (CV)?
Real-life CV applications: Face recognition, self-driving cars, medical imaging, retail
Sensors and perception in AI
Vision-based AI systems and how they work
CV Tasks:
- Image Classification
- Object Detection
- Image Segmentation
Basics of image representation:
- Pixels, resolution, grayscale and RGB images
Technical Concepts:
- Image features
- Convolution, Kernels
- CNN: Layers and architecture (Convolution, Pooling, ReLU, Fully Connected Layer)
Tools and Libraries:
- No-code tools: Lobe, Teachable Machine
- Python libraries: OpenCV, TensorFlow, Keras
Exercises
- Objective Type Questions
- Subjective Types Questions
Activities
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Module 5: Natural Language Processing (NLP)

What is NLP?
Importance and features of human vs computer language
NLP Applications:
- Chatbots, Translation, Voice Assistants
- Sentiment Analysis, Auto-generated Captions
NLP Pipeline:
- Lexical, Syntactic, Semantic, Discourse, Pragmatic Analysis
Text Processing:
- Text Normalization
- Bag of Words
- TFIDF and its applications
Tools and Use Cases:
- Code and no-code tools (e.g., Orange)
- Sentiment analysis mini-project
- Scripted vs Smart Chatbots
Exercises
- Objective Type Questions
- Subjective Types Questions
Activities
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Module 6: Generative AI and AI for Society

Introduction to Generative AI
- What it is and how it works
- Examples and applications: Text, Image, Music, Code
AI for Social Good:
- Importance of sustainability
- AI’s role in achieving SDGs
Systems Thinking:
- Introduction to system maps
- Use of system maps in AI planning and decision making
Exercises
- Objective Type Questions
- Subjective Types Questions
Activities
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Module 7: AI Project Cycle

Module 8: Ethics in AI

Difference between Ethics and Morality
Understanding fairness, transparency, and accountability
Common ethical dilemmas in AI systems
AI Bias: Sources, identification, and mitigation strategies
Ethical Frameworks:
- Importance and need
- Types of ethical frameworks
- Principles of Bioethics
Case-based Learning:
- Moral Machine Activity
- Evaluating Ethical Scenarios
Exercises
- Objective Type Questions
- Subjective Types Questions
Activities
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Course Outcome:

Students will be able to:

Define and differentiate AI from automation, explain its types, and identify real-life applications across domains like CV, NLP, and Data Science.
Demonstrate proficiency in data handling, from collection to visualization, using Python libraries and no-code tools, while applying best practices for privacy and security.
Apply appropriate AI models and techniques, including supervised, unsupervised, and neural network approaches, and evaluate their performance effectively.
Build basic AI applications in Computer Vision and NLP, including chatbot design, sentiment analysis, image recognition, and real-world case studies.
Design ethically sound AI projects, map real-world problems to the AI Project Cycle, and propose AI solutions aligned with SDGs and ethical principles.

References

..Dr.Thyagaraju G S

Insight