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CBSE Class 9 AI Practical File 2026-27 | 15 Python Programs with Output | Code 417

📗 AI Logic School

Class 9 AI Practical File
2025–26

All Python programs with code & output for your CBSE/KVS Class 9 AI practical file. Prepared as per Code 417 syllabus.

15+

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Units

100%

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📥 Download Complete Class 9 AI Practical File PDF

All 15 programs with aim, code, output and viva questions in one printable PDF. Free for CBSE students.

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ℹ️

Class IX | Subject: Artificial Intelligence | Code: 417 | Academic Year 2025-26
All programs are written in Python 3 and tested as per the latest CBSE Class 9 AI syllabus. Each program includes aim, code, and expected output.

Class 9 AI Practical File Python Programs CBSE Code 417 KVS 2025-26 With Output Free Download

📚 Syllabus Units Covered

Unit 1

Introduction to AI

AI concepts, types of AI, applications in daily life, AI project cycle.

2 Programs

Unit 2

Data Science

Python basics, variables, lists, NumPy arrays, data handling.

4 Programs

Unit 3

Computer Vision

Image basics, pixel manipulation, OpenCV introduction.

3 Programs

Unit 4

Natural Language Processing

Text processing, tokenisation, basic NLP with Python.

3 Programs

Unit 5

Logical Reasoning

Problem solving, algorithms, flowcharts, Python logic programs.

3 Programs

🛠️ How to Use This Practical File

1️⃣
Read the Aim — Understand the purpose of each program before coding.
2️⃣
Copy the Code — Click the Copy button and paste in IDLE or VS Code.
3️⃣
Run & Check — Run the program and match your output with the expected output.
4️⃣
Write in File — Note Aim, Code, and Output neatly in your practical file.
5️⃣
Download PDF — Download the complete practical file PDF using the button below.

💻 Python Programs with Code & Output

📌 Note: Make sure Python 3 is installed on your computer. Install required libraries using pip install numpy matplotlib in command prompt before running programs.

Prog 01

Introduction to Python — Print & Input

Unit 1

🎯 Aim

To write a basic Python program to display information about Artificial Intelligence and accept user input.

🟢 Python Code

# Program 1: Introduction to AI
print("=" * 45)
print("   Welcome to Artificial Intelligence!")
print("=" * 45)
print("AI stands for Artificial Intelligence.")
print("It makes computers think like humans.")
print()
name = input("Enter your name: ")
grade = input("Enter your class: ")
print()
print(f"Hello {name} from Class {grade}!")
print("Let's explore the world of AI together!")

🖥️ Expected Output

=============================================
   Welcome to Artificial Intelligence!
=============================================
AI stands for Artificial Intelligence.
It makes computers think like humans.

Enter your name: Riya
Enter your class: 9

Hello Riya from Class 9!
Let's explore the world of AI together!

Prog 02

AI Application Quiz Program

Unit 1

🎯 Aim

To create a simple quiz program about AI applications using Python if-else conditions.

🟢 Python Code

# Program 2: AI Quiz
score = 0
print("--- AI Applications Quiz ---")
print()
q1 = input("Q1. Which AI app gives movie suggestions? ")
if q1.lower() in ["netflix", "recommendation system"]:
    print("Correct! ✓"); score += 1
else:
    print("Answer: Netflix (Recommendation System)")
q2 = input("Q2. Which AI technology reads your face? ")
if q2.lower() in ["face recognition", "facial recognition"]:
    print("Correct! ✓"); score += 1
else:
    print("Answer: Face Recognition")
q3 = input("Q3. Name one AI voice assistant: ")
if q3.lower() in ["siri", "alexa", "google assistant", "cortana"]:
    print("Correct! ✓"); score += 1
else:
    print("Answer: Siri / Alexa / Google Assistant")
print()
print(f"Your Score: {score}/3")

🖥️ Expected Output

--- AI Applications Quiz ---

Q1. Which AI app gives movie suggestions? Netflix
Correct! ✓
Q2. Which AI technology reads your face? Face Recognition
Correct! ✓
Q3. Name one AI voice assistant: Alexa
Correct! ✓

Your Score: 3/3

Prog 03

Python Variables, List & Basic Operations

Unit 2

🎯 Aim

To demonstrate use of variables, lists, and basic arithmetic operations in Python for data handling.

🟢 Python Code

# Program 3: Variables and Lists
student_name = "Aryan"
marks = [85, 92, 78, 95, 88]
print(f"Student: {student_name}")
print(f"Marks  : {marks}")
print()
print(f"Total  : {sum(marks)}")
print(f"Average: {sum(marks)/len(marks):.1f}")
print(f"Highest: {max(marks)}")
print(f"Lowest : {min(marks)}")
print()
avg = sum(marks) / len(marks)
if avg >= 33:
    print(f"{student_name} has PASSED! 🎉")
else:
    print(f"{student_name} needs to work harder.")

🖥️ Expected Output

Student: Aryan
Marks  : [85, 92, 78, 95, 88]

Total  : 438
Average: 87.6
Highest: 95
Lowest : 78

Aryan has PASSED! 🎉

Prog 04

NumPy Array — Create & Operations

Unit 2

🎯 Aim

To create NumPy arrays and perform basic mathematical operations used in Data Science.

🟢 Python Code

import numpy as np

temps_week = np.array([32, 35, 30, 28, 33, 36, 31])
days = ["Mon","Tue","Wed","Thu","Fri","Sat","Sun"]

print("Weekly Temperature Data:")
for day, temp in zip(days, temps_week):
    print(f"  {day}: {temp}°C")
print()
print(f"Average Temperature: {np.mean(temps_week):.1f}°C")
print(f"Hottest Day       : {days[np.argmax(temps_week)]} ({np.max(temps_week)}°C)")
print(f"Coolest Day       : {days[np.argmin(temps_week)]} ({np.min(temps_week)}°C)")
print(f"Temperature Range : {np.max(temps_week) - np.min(temps_week)}°C")

🖥️ Expected Output

Weekly Temperature Data:
  Mon: 32°C  Tue: 35°C  Wed: 30°C  Thu: 28°C
  Fri: 33°C  Sat: 36°C  Sun: 31°C

Average Temperature: 32.1°C
Hottest Day       : Sat (36°C)
Coolest Day       : Thu (28°C)
Temperature Range : 8°C

Prog 05

Bar Chart using Matplotlib

Unit 2

🎯 Aim

To create a bar chart showing student marks using the Matplotlib library in Python.

🟢 Python Code

import matplotlib.pyplot as plt

students = ['Aarav','Priya','Rohan','Meena','Karan','Sita']
marks    = [85, 92, 78, 96, 88, 74]
colors   = ['#3498db','#2ecc71','#e74c3c','#9b59b6','#f39c12','#1abc9c']

plt.figure(figsize=(9, 5))
bars = plt.bar(students, marks, color=colors, edgecolor='white', linewidth=1.2)
for bar, mark in zip(bars, marks):
    plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1,
             str(mark), ha='center', va='bottom', fontweight='bold')
plt.title('Class 9 Student Marks — Bar Chart', fontsize=14, fontweight='bold')
plt.xlabel('Student Name')
plt.ylabel('Marks (out of 100)')
plt.ylim(0, 110)
plt.axhline(y=33, color='red', linestyle='--', label='Pass Mark (33)')
plt.legend()
plt.tight_layout()
plt.show()
print("Bar chart displayed successfully!")

🖥️ Expected Output

[Colourful bar chart window opens showing marks for each student]
[Red dashed line shows pass mark at 33]
Bar chart displayed successfully!

Prog 06

Line Graph — Temperature over a Week

Unit 2

🎯 Aim

To plot a line graph showing temperature variation over a week using Matplotlib.

🟢 Python Code

import matplotlib.pyplot as plt

days  = ['Mon','Tue','Wed','Thu','Fri','Sat','Sun']
temps = [32, 35, 30, 28, 33, 36, 31]

plt.figure(figsize=(9, 5))
plt.plot(days, temps, marker='o', color='#e74c3c',
         linewidth=2.5, markersize=8, markerfacecolor='white',
         markeredgewidth=2, label='Temperature')
plt.fill_between(days, temps, alpha=0.15, color='#e74c3c')
for i, (day, temp) in enumerate(zip(days, temps)):
    plt.annotate(f'{temp}°C', (day, temp),
                 textcoords="offset points", xytext=(0,10), ha='center')
plt.title('Weekly Temperature — Line Graph', fontsize=13, fontweight='bold')
plt.xlabel('Day of Week')
plt.ylabel('Temperature (°C)')
plt.legend()
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()
print("Line graph displayed successfully!")

🖥️ Expected Output

[Line graph window opens with temperature data points connected]
[Red shaded area shown under the line]
Line graph displayed successfully!

Prog 07

Read & Display an Image using OpenCV

Unit 3

🎯 Aim

To read an image from the computer and display it using the OpenCV library in Python.

🟢 Python Code

import cv2

img = cv2.imread('photo.jpg')
print("Image Details:")
print(f"  Height  : {img.shape[0]} pixels")
print(f"  Width   : {img.shape[1]} pixels")
print(f"  Channels: {img.shape[2]} (BGR)")
print(f"  Size    : {img.size} total pixels")
cv2.imshow('My Image', img)
print("\nPress any key to close the image window...")
cv2.waitKey(0)
cv2.destroyAllWindows()
print("Window closed.")

🖥️ Expected Output

Image Details:
  Height  : 480 pixels
  Width   : 640 pixels
  Channels: 3 (BGR)
  Size    : 921600 total pixels

Press any key to close the image window...
[Image window opens displaying the photo]
Window closed.

Prog 08

Convert Image to Grayscale

Unit 3

🎯 Aim

To convert a colour image into grayscale using OpenCV and compare both images.

🟢 Python Code

import cv2

color_img = cv2.imread('photo.jpg')
gray_img  = cv2.cvtColor(color_img, cv2.COLOR_BGR2GRAY)

print(f"Original image shape : {color_img.shape}")
print(f"Grayscale image shape: {gray_img.shape}")
print()
print("Colour image has 3 channels (Blue, Green, Red)")
print("Grayscale has only 1 channel (intensity)")

cv2.imshow('Original — Colour',    color_img)
cv2.imshow('Converted — Grayscale', gray_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite('gray_photo.jpg', gray_img)
print("Grayscale image saved as gray_photo.jpg")

🖥️ Expected Output

Original image shape : (480, 640, 3)
Grayscale image shape: (480, 640)

Colour image has 3 channels (Blue, Green, Red)
Grayscale has only 1 channel (intensity)
[Two windows open: original colour + grayscale]
Grayscale image saved as gray_photo.jpg

Prog 09

Draw Shapes on Image using OpenCV

Unit 3

🎯 Aim

To draw basic shapes (rectangle, circle, line, text) on an image using OpenCV drawing functions.

🟢 Python Code

import cv2
import numpy as np

canvas = np.ones((400, 600, 3), dtype=np.uint8) * 255
cv2.rectangle(canvas, (50, 50),  (200, 150), (255, 0, 0),   3)
cv2.circle(canvas,   (350, 100),  80,         (0, 255, 0),   3)
cv2.line(canvas,     (50, 200),  (550, 200),  (0, 0, 255),   2)
cv2.ellipse(canvas,  (300, 300),  (120, 60), 0, 0, 360, (128,0,128), 3)
cv2.putText(canvas, 'Class 9 AI — OpenCV Shapes',
            (60, 370), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,0,0), 2)
cv2.imshow('OpenCV Drawing', canvas)
cv2.waitKey(0)
cv2.destroyAllWindows()
print("Shapes drawn successfully!")

🖥️ Expected Output

[White canvas with Blue rectangle, Green circle,
 Red line, Purple ellipse, and text label]
Shapes drawn successfully!

Prog 10

Text Tokenisation using NLTK

Unit 4

🎯 Aim

To split a paragraph into words and sentences using NLTK tokenisation in Python.

🟢 Python Code

import nltk
nltk.download('punkt', quiet=True)
from nltk.tokenize import word_tokenize, sent_tokenize

text = ("AI is changing the world. "
        "Machines can now see, hear and speak. "
        "Python is used to build AI programs.")

print("Original Text:")
print(text)
print()
sentences = sent_tokenize(text)
print(f"Number of Sentences: {len(sentences)}")
for i, s in enumerate(sentences, 1):
    print(f"  {i}. {s}")
words = word_tokenize(text)
print()
print(f"Number of Words: {len(words)}")
print(f"Words: {words}")

🖥️ Expected Output

Number of Sentences: 3
  1. AI is changing the world.
  2. Machines can now see, hear and speak.
  3. Python is used to build AI programs.

Number of Words: 23

Prog 11

Count Word Frequency in a Text

Unit 4

🎯 Aim

To count the frequency of each word in a given sentence using Python dictionary.

🟢 Python Code

# Program 11: Word Frequency Counter
text = "AI is great AI helps humans AI is the future of technology"
words = text.lower().split()
freq = {}
for word in words:
    freq[word] = freq.get(word, 0) + 1
sorted_freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)
print("Word Frequency Analysis:")
print("-" * 30)
print(f"{'Word':<15} {'Count':>5}")
print("-" * 30)
for word, count in sorted_freq:
    bar = "█" * count
    print(f"{word:<15} {count:>5}  {bar}")
print()
print(f"Total unique words: {len(freq)}")
print(f"Most frequent: '{sorted_freq[0][0]}' ({sorted_freq[0][1]} times)")

🖥️ Expected Output

Word Frequency Analysis:
------------------------------
Word            Count
------------------------------
ai                  3  ███
is                  2  ██
great               1  █
Most frequent: 'ai' (3 times)

Prog 12

Simple Chatbot using if-else

Unit 4

🎯 Aim

To create a simple rule-based chatbot for Class 9 students using Python if-else statements.

🟢 Python Code

# Program 12: Simple Chatbot
def chatbot(msg):
    msg = msg.lower()
    if any(w in msg for w in ["hello","hi","hey"]):
        return "Hello! I am Class 9 AI Bot! How can I help?"
    elif "name" in msg:
        return "I am EduBot — your Class 9 AI assistant!"
    elif "ai" in msg:
        return "AI stands for Artificial Intelligence!"
    elif "python" in msg:
        return "Python is a programming language used in AI."
    elif "bye" in msg or "exit" in msg:
        return "Goodbye! Keep learning! 👋"
    else:
        return "I did not understand. Ask me about AI or Python!"

print("EduBot Started! (Type 'bye' to exit)")
print("-" * 40)
while True:
    user = input("You : ")
    resp = chatbot(user)
    print(f"Bot : {resp}")
    if "bye" in user.lower():
        break

🖥️ Expected Output

EduBot Started! (Type 'bye' to exit)
----------------------------------------
You : hello
Bot : Hello! I am Class 9 AI Bot! How can I help?
You : what is ai
Bot : AI stands for Artificial Intelligence!
You : bye
Bot : Goodbye! Keep learning! 👋

Prog 13

Number Pattern using Loops

Unit 5

🎯 Aim

To demonstrate logical reasoning using Python loops to print number patterns.

🟢 Python Code

# Program 13: Number Patterns
print("Pattern 1: Number Triangle")
for i in range(1, 6):
    print(" ".join(str(j) for j in range(1, i+1)))
print()
print("Pattern 2: Star Pyramid")
for i in range(1, 6):
    print(" " * (5-i) + "* " * i)
print()
print("Pattern 3: Multiplication Table of 5")
for i in range(1, 11):
    print(f"  5 x {i:2d} = {5*i:3d}")

🖥️ Expected Output

Pattern 1: Number Triangle
1
1 2
1 2 3
1 2 3 4
1 2 3 4 5

Pattern 2: Star Pyramid
    * 
   * * 
  * * * 
 * * * * 
* * * * *

Prog 14

Fibonacci Sequence — AI Pattern Recognition

Unit 5

🎯 Aim

To generate and display the Fibonacci sequence using Python, demonstrating pattern recognition used in AI.

🟢 Python Code

# Program 14: Fibonacci Sequence
print("Fibonacci Sequence in AI")
print("Used in pattern recognition and nature!")
print()
n = int(input("How many terms? "))
a, b = 0, 1
fib_list = []
for _ in range(n):
    fib_list.append(a)
    a, b = b, a + b
print(f"\nFirst {n} Fibonacci numbers:")
print(fib_list)
print()
print("Visualisation:")
for num in fib_list[:10]:
    print("█" * (num % 20 + 1), num)

🖥️ Expected Output

How many terms? 10

First 10 Fibonacci numbers:
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34]

Prog 15

Simple Calculator — AI Decision Making

Unit 5

🎯 Aim

To create a simple calculator using Python functions, demonstrating AI decision-making using conditions.

🟢 Python Code

# Program 15: Smart Calculator
def calculate(a, op, b):
    if op == '+': return a + b
    elif op == '-': return a - b
    elif op == '*': return a * b
    elif op == '/':
        if b == 0: return "Error: Cannot divide by zero!"
        return a / b
    else: return "Unknown operator!"

print("Smart AI Calculator")
print("Operations: + - * /")
print("-" * 30)
while True:
    try:
        num1 = float(input("Enter first number : "))
        op   = input("Enter operator (+,-,*,/) : ")
        num2 = float(input("Enter second number: "))
        result = calculate(num1, op, num2)
        print(f"Result: {num1} {op} {num2} = {result}")
    except ValueError:
        print("Please enter valid numbers!")
    again = input("Calculate again? (yes/no): ")
    if again.lower() != 'yes':
        print("Goodbye! 👋")
        break

🖥️ Expected Output

Smart AI Calculator
Enter first number : 25
Enter operator     : *
Enter second number: 4
Result: 25.0 * 4.0 = 100.0
Goodbye! 👋

📥 Download Complete Class 9 AI Practical File PDF

All 15 programs with aim, code, output and viva questions in one printable PDF. Perfect for CBSE practical file submission.

⬇️ Download Full PDF — Free

📌 Note: This page shows all 15 programs. Download the PDF above for a printable version ready for your practical file submission.

❓ Important Viva Questions

Q1. What is Artificial Intelligence?

AI is the ability of a machine to think, learn and solve problems like a human being.

Q2. Name 3 applications of AI in daily life.

Google Maps, Face Unlock, Voice Assistants (Alexa/Siri), Netflix recommendations, Spam filters.

Q3. What is Python? Why is it used in AI?

Python is a simple programming language. It is used in AI because it has many ready-made libraries like NumPy, Matplotlib, and OpenCV.

Q4. What is NumPy?

NumPy is a Python library used to create and work with arrays and perform mathematical operations on data.

Q5. What is Matplotlib?

Matplotlib is a Python library used to create charts and graphs like bar charts, pie charts, and line graphs.

Q6. What is Computer Vision?

Computer Vision is a field of AI that enables computers to understand and interpret images and videos.

Q7. What is OpenCV?

OpenCV is a Python library used for image processing, face detection, and video analysis.

Q8. What is NLP?

NLP stands for Natural Language Processing — it enables computers to understand and generate human language.

Q9. What is tokenisation?

Tokenisation is splitting a sentence into individual words or sentences using NLTK library.

Q10. What is a chatbot?

A chatbot is an AI program that simulates human conversation using rules or machine learning.

🔗 More Resources on AI Logic School

📘 Class 12

Class XII AI Lab Manual

Complete Python programs for CBSE Class 12 AI subject 2025-26.

📗 Class 10

Class 10 AI Practical File

15 Python programs for CBSE Class 10 AI Code 417.

📙 Worksheets

CBSE AI Worksheets

Free printable worksheets for Classes 6 to 12 AI subject.

Continue learning

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Chapter 3: AI & Logic Gates

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Unit 6: Introduction to Python

Mastering the World's Most Popular AI Programming Language

1. Why Python for AI?

Python was created by Guido van Rossum in 1991. Today, it is the #1 choice for AI because it is easy to read, just like English!

Simple Syntax

Python code is very short. To print a message, you just need one line, unlike Java or C++.

Massive Libraries

Python has ready-made "toolboxes" (Libraries) like NumPy, Pandas, and Scikit-learn specifically for AI.

2. Your First Python Script

Let's look at the basic "Hello World" program. In Python, it's as simple as this:

print("Hello, AI Logic School Students!")

3. Variables and Data Types

Think of a variable as a labeled container that stores data. Common data types in Python are:

Integer (int): Whole numbers (e.g., age = 15)
Float: Decimal numbers (e.g., price = 99.50)
String (str): Text inside quotes (e.g., name = "AI")
Boolean: True or False values.

    # Example of Variables

    x = 10

    y = 20

    sum = x + y

    print(sum)

4. Python Interpreters

Python is an interpreted language. This means the computer reads the code line-by-line and executes it immediately.

💻 Live Activity: Python in the Browser

You don't need to install anything to start! Use Google Colab, a cloud-based Python environment used by real AI engineers worldwide.

Open Google Colab 🚀

Task: Click '+ Code', type print(100 * 5), and click the Play button!

Continue learning

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Unit 5: Introduction to Generative AI

From Predicting Data to Creating Masterpieces

1. What is Generative AI?

Traditional AI (Discriminative AI) is like a judge—it looks at data and decides what it is (e.g., "This is a cat"). Generative AI is like an artist—it takes what it has learned and creates something entirely new (e.g., "Draw a cat wearing a space suit").

Definition: Generative AI refers to algorithms (like LLMs) that can be used to create new content, including audio, code, images, text, and videos.

2. Discriminative vs. Generative AI

Feature	Discriminative AI	Generative AI
Goal	Categorize or Predict	Create New Content
Input	Data points	Prompts (Instructions)
Example	Spam Filter, Face ID	ChatGPT, Midjourney

3. How does it work? (LLMs)

Generative AI for text uses Large Language Models (LLMs). These models are trained on billions of pages of text so they can predict the "next most likely word" in a sentence. It’s like a very advanced version of the "Autofill" on your phone keyboard!

4. Ethics of Generative AI

Hallucinations: Sometimes AI gives confident but completely wrong answers.
Deepfakes: Creating realistic but fake images/videos of people.
Copyright: Who owns the art created by an AI?

🎨 Live Activity: AutoDraw

Experience a simple form of Generative AI! Google's AutoDraw uses machine learning to turn your rough sketches into professional icons in real-time.

Start Creating with AutoDraw 🚀

Task: Draw a "Computer" and see how the AI suggests beautiful generated versions of your sketch!

Continue learning

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Unit 3: Mathematics for AI

Statistics, Probability, and Algebraic Thinking

Why does AI need Math?

AI doesn't "think" like humans; it calculates. It uses math to find patterns in data and predict future outcomes. The three pillars of AI Math are Statistics, Probability, and Algebra.

📊 Statistics

Used to collect, organize, and summarize data. Concepts like Mean, Median, and Mode help AI find the "average" behavior of data.

Example: AI uses statistics to predict the average marks of a class based on past performance.

🎲 Probability

Helps AI deal with uncertainty. It calculates the "chance" of an event happening.

Example: When a weather AI says there is a 90% chance of rain, it is using probability.

📈 Algebra

AI uses linear equations to represent relationships between variables. The most famous one is:

Y = mx + c

This helps AI draw a "best-fit line" through data points.

Understanding Data Visualization

Math allows us to turn numbers into pictures. Common ways AI represents data include:

Histograms: To show the frequency of data.
Scatter Plots: To see the relationship between two variables.
Box Plots: To see the spread and outliers in data.

🎲 Live Activity: Emoji Scavenger Hunt

Google's Emoji Scavenger Hunt uses Probability and Computer Vision. The AI calculates the probability that the object it "sees" through your camera is the emoji it asked for!

Start Scavenger Hunt 🔍

Task: Note down how the "Probability Percentage" changes as you move your camera closer to an object.

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Unit 2: Data Literacy

Empowering Students to Understand, Use, and Analyze Data

1. What is Data Literacy?

Data Literacy is the ability to derive meaningful information from data. Just as reading and writing are essential skills, understanding data is the new "literacy" in the age of AI.

The Core Concept: Data is the "raw material." Data Literacy is the "skill" used to process that material into useful "insights."

2. Types of Data: Structured vs Unstructured

Machines process different types of data in different ways:

Structured Data: Highly organized and easy to search (e.g., Excel sheets, SQL databases).
Unstructured Data: Has no pre-defined format (e.g., Photos, Audio files, Social media posts). This is where AI (Computer Vision and NLP) is most useful!

3. The Data Lifecycle

Data goes through several stages before an AI can use it effectively:

1. Data
Acquisition

➔

2. Data
Cleaning

➔

3. Data
Analysis

➔

4. Data
Visualization

4. Data Ethics & Bias

If the data used to train an AI is biased (incorrect or unfair), the AI's decisions will also be biased. This is often called GIGO: Garbage In, Garbage Out.

🕵️ Live Activity: The Data Detective

How much data does the world generate in a single minute? Explore "Data Never Sleeps" to see the massive scale of modern data acquisition.

View Live Data Scale 📊

Task: Identify three sources of "Unstructured Data" mentioned in the infographic.

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Class 9 AI | Unit 1: Introduction to AI (Notes & MCQ

CBSE Subject Code 417

Unit 1: Introduction to Artificial Intelligence

Foundations of Data Science, Computer Vision, and NLP

1. Understanding the 3 Domains of AI

Artificial Intelligence is not a single technology. In the CBSE Class 9 syllabus, it is divided into three core domains based on the type of data the machine processes:

📊 Data Science (DS)

Processing and analyzing massive amounts of numeric data to find patterns.

Daily Life: Price comparisons on Amazon or Weather forecasts on your phone.

👁️ Computer Vision (CV)

Enabling machines to identify and process images and videos like human eyes.

Daily Life: Face Unlock on smartphones or QR code scanning.

🗣️ Natural Language Processing (NLP)

The ability of a computer to understand, interpret, and generate human language.

Daily Life: Auto-correct in WhatsApp or voice commands to Alexa/Siri.

2. AI vs Rule-Based Systems

A "Rule-Based" system follows a fixed set of instructions (If-Then). AI, however, uses Machine Learning to improve its performance as it gets more data.

🎮 Live Activity: AI Ethics & Domains

Can an AI be "fair"? Explore Survival of the Best Fit, an educational game about how AI hiring tools can develop bias if the data is not correct.

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Task: Play the game and note down how "Data Bias" affected the hiring process.

3. AI & Sustainable Development Goals (SDGs)

AI is a powerful tool to achieve the 17 Global Goals set by the UN. For example:

Goal 4 (Quality Education): AI-powered personalized learning apps.
Goal 13 (Climate Action): Using Computer Vision to track melting glaciers.

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Chapter 3: AI & Logic Gates

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