High School Artificial Intelligence

   May 11, 2025

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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    • aaaaaaa

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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    • aaaaaaa

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
    • Aaaaa
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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
    • Aaaa
    • aaaa

Module 7: AI Project Cycle

  • Overview and significance of the AI Project Cycle
  • Six stages of the cycle:
    1. Problem Scoping
    2. Data Acquisition
    3. Data Exploration
    4. Modelling
    5. Evaluation
    6. Deployment
  • Mapping real-world problems to AI stages
  • Application of the AI Cycle in hands-on projects
  • Exercises
    1. Objective Type Questions
    2. Subjective Types Questions
  • Activities
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    2. aaaa

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:

  1. Define and differentiate AI from automation, explain its types, and identify real-life applications across domains like CV, NLP, and Data Science.
  2. Demonstrate proficiency in data handling, from collection to visualization, using Python libraries and no-code tools, while applying best practices for privacy and security.
  3. Apply appropriate AI models and techniques, including supervised, unsupervised, and neural network approaches, and evaluate their performance effectively.
  4. Build basic AI applications in Computer Vision and NLP, including chatbot design, sentiment analysis, image recognition, and real-world case studies.
  5. 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

  1. Teachable Machine
  2. Learn about Artificial Intelligence (AI) | Code.org
  3.  

..Dr.Thyagaraju G S