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Artificial Intelligence

Artificial Intelligence

Welcome

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Artificial Intelligence in Class 9 introduces students to AI concepts, such as machine learning, robotics, and natural language processing. It focuses on understanding AI's applications in daily life, problem-solving, and ethical considerations in technology. The exam is 100 marks, with 50 marks for theory and 50 marks for practicals. The paper has four sections: 1) Employability Skills, 2) Subject-Specific Skills, 3) Practical Work, and 4) Project Work

 

Prerequisites

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The prerequisites for learning Artificial Intelligence in Class 9 include basic computer skills, logical thinking, and an interest in technology. Students should be familiar with simple coding concepts, math basics, and have curiosity about AI and its real-life applications.

 

Learning Objective

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The learning objective of Artificial Intelligence is to introduce students to AI concepts, enhance problem-solving and critical thinking skills, and help them understand how AI technologies work in real-life applications, preparing them for future technological advancements.

Course Overview

1 Communication Skills-I

  • Introduction to Communication
  • Effective Listening:
  • Speaking Skills
  • Non-verbal Communication
  • Barriers to Effective Communication


2.Self-Management Skills-I

  • Introduction to Self-management
  • Stress Management
  • Self-Motivation
  • Personal Strengths and Weaknesses


3: Basic Information and Communication Technology Skills-I

  • Introduction to ICT
  • Basic Computer Operations
  • Using the Internet
  • Cyber Safety


4 Entrepreneurial Skills-I

  • Introduction to Entrepreneurship
  • Entrepreneurial Qualities
  • Business Idea Identification


5. Green Skills-I

  • Introduction to Green Economy
  • Conservation of Resources
  • Environment-Friendly Practices


1: AI Reflection, Project Cycle, and Ethics

  • Introduction to AI
  • AI Project Cycle
  • Ethics in AI
  • Reflection


2: Data Literacy

  • Understanding Data
  • Data Collection and Cleaning
  • Data Representation
  • Data Bias and Privacy


3: Math for AI (Statistics & Probability)

  • Basics of Statistics
  • Probability Fundamentals
  • AI Models


4: Introduction to Generative AI

  • Generative AI Basics
  • Applications of Generative AI
  • Ethical Considerations.


5: Introduction to Python

  • Introduction to Python Programming: Overview of Python and its real-world applications.
  • Understanding IO in Python: Input and output operations.
  • Writing First Program: Basic Python program execution.
  • Python Tokens: Keywords, identifiers, literals, and operators.
  • Comments: Writing single-line and multi-line comments in Python.
  • Data Types: Introduction to basic data types (int, float, string, etc.)

  • .
  • Operators: Arithmetic, logical, and comparison operators.
  • Conditional Statements: if, elif, and else statements.
  • Loops: for and while loops for iteration.
  • List: Creating, accessing, and manipulating lists.


  • Practical Examination of Python : 30 Marks
  • Viva Voce : 05 Marks


  • Practical File/ Student Portfolio 15 Marks


Frequently Asked Questions (FAQs)

The syllabus includes AI fundamentals, data literacy, machine learning concepts, project cycle, ethics in AI, and Python programming

Assessment typically consists of theory exams, practical assessments, and project evaluations, contributing to overall marks.

Basic computer skills and an interest in technology are recommended, but no prior knowledge of AI is required.

Practical work involves hands-on coding in Python, data analysis, and applying AI concepts through projects and exercises.

Yes, students are required to complete a project that applies AI concepts, encouraging practical application and understanding.

Students will develop skills in programming, critical thinking, data analysis, ethical reasoning in technology, and teamwork through projects.

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