Introduction

Aligned with the National Education Policy (NEP) 2020, this curriculum focuses on experiential learning, critical thinking, creativity, and interdisciplinary education. It is designed to foster innovation and prepare students for future careers in technology.

Course Structure

  • Introduction to Technology and AI
    • Basic understanding of technology around us.
    • Simple explanation of AI with examples (e.g., smart assistants, robots).
  • Fun with Coding
    • Introduction to block-based coding (e.g., Scratch).
    • Simple coding exercises and games.
  • Pattern Recognition
    • Activities to identify patterns in numbers, shapes, and daily activities.
    • Games that involve sorting and categorizing objects.
  • Logical Thinking
    • Puzzles and brain games to enhance logical reasoning.
    • Story-based problem-solving activities.

Middle Level (Grades 6-8)

  • Introduction to AI and Machine Learning
    • Overview of AI, machine learning, and their real-world applications.
    • Discussions on AI in daily life (e.g., recommendation systems, facial recognition).
  • Basic Programming
    • Introduction to text-based programming languages (e.g., Python).
    • Writing simple programs and understanding basic algorithms.
  • Data and Information
    • Understanding data, data collection, and its importance.
    • Activities involving data visualization (e.g., charts, graphs).
  • AI Ethics and Impact
    • Discussions on the ethical implications of AI.
    • Case studies on the impact of AI on society.
  • Hands-on Projects
    • Simple AI projects (e.g., chatbot, basic games).
    • Group activities to promote collaboration and problem-solving.

Secondary Level (Grades 9-10)

  • Advanced AI Concepts
    • In-depth study of machine learning algorithms (e.g., supervised, unsupervised learning).
    • Introduction to neural networks and deep learning.
  • Programming and Algorithms
    • Advanced Python programming.
    • Data structures and algorithms.
  • Data Science and Big Data
    • Data analysis, data cleaning, and preprocessing.
    • Introduction to big data and its significance.
  • AI Applications
    • Case studies on AI applications in various industries (e.g., healthcare, finance).
    • Exploring AI tools and platforms (e.g., TensorFlow, Keras).
  • AI Ethics, Bias, and Fairness
    • Deep dive into ethical issues, bias in AI, and fairness.
    • Strategies to mitigate bias in AI systems.
  • Capstone Projects
    • Developing comprehensive AI projects addressing real-world problems.
    • Presenting projects and peer reviews.

Senior Secondary Level (Grades 11-12)

  • Specialized AI Topics
    • Natural Language Processing (NLP).
    • Computer Vision.
    • Reinforcement Learning.
  • Advanced Data Science
    • Exploratory data analysis.
    • Predictive modeling and advanced statistical methods.
  • AI Research and Development
    • Introduction to AI research methodologies.
    • Reading and analyzing AI research papers.
  • AI in Emerging Technologies
    • Exploring AI in emerging technologies (e.g., IoT, robotics, blockchain).
    • Projects integrating AI with other technologies.
  • Ethical AI Development
    • Creating AI systems with ethical considerations.
    • Understanding the global impact of AI.
  • Capstone Projects and Internships
    • Advanced AI projects with real-world applications.
    • Internships with industry partners for practical experience.