Artificial Intelligence

Total Marks: 100 → Theory 50 + Practical 50. The course has two halves: Part A – Employability Skills (common to all skill subjects) and Part B – Subject-Specific AI Skills, plus Practical (Part C) and Project (Part D).

Part A — Employability Skills (10 marks)

Part B — Subject-Specific AI Skills (40 marks)

Practical & Project (Parts C & D — 50 marks)

Follow a simple Read → Recall → Test → Revise loop for every unit. The guide is built so you can study fully offline in any browser.

Communication is the act of sharing or exchanging information, ideas or feelings between two or more people so that the message is understood. It comes from the Latin word communicare meaning "to share."

Communication is the most-used skill in the world. We do it from the moment we wake up — a smile, a "good morning", a text message. Studies suggest a person spends around 70% of waking hours communicating in some form. Strong communication is the number-one skill employers look for, which is why it is the first employability unit.

The Communication Cycle

Communication is a two-way process. A complete cycle has the following elements:

Methods / Types of Communication

Verbal Communication — Advantages & Disadvantages

The 7 C's of Effective Communication

Barriers to Communication & How to Overcome

📝 Practice Question Bank — Communication Skills

Self-management, also called self-control, is the ability to understand and control your own emotions, thoughts and actions to reach your goals. It begins with self-awareness.

Self-Awareness & Strengths/Weaknesses

Knowing yourself — your feelings, strengths, weaknesses, likes and dislikes — is the first step. A useful tool is the SWOT analysis.

Self-Motivation

Motivation is the energy that pushes us to act and achieve goals. It is of two types:

Self-Regulation & Goal Setting (SMART)

Self-regulation means managing your emotions and behaviour. A key skill is setting SMART goals:

Time Management & Positive Thinking

📝 Practice Question Bank — Self-Management

ICT means using computers and other digital devices to store, retrieve, send and receive information. ICT skills are essential for study, work and daily life.

Parts of a Computer

Operating System & File Management

An Operating System (OS) (Windows, Linux, macOS, Android) manages all hardware and software and provides the user interface. Good file management means organising files in folders, naming them clearly, and keeping backups.

Basic ICT Etiquette & Care

📝 Practice Question Bank — ICT Skills

An entrepreneur is a person who sets up a business or businesses, taking on financial risks in the hope of profit. Entrepreneurship is the activity of starting and running such a business.

Entrepreneur vs Wage Employment

Qualities & Functions of an Entrepreneur

Role & Importance of Entrepreneurship

• Creates jobs — entrepreneurs employ others, reducing unemployment.
• Solves problems — they identify needs of society and provide solutions.
• Develops the economy — businesses add to national income & growth.
• Encourages innovation — new products, services and ideas.
• Uses local resources — creates value from available materials & skills.

How Entrepreneurs Work

📝 Practice Question Bank — Entrepreneurial Skills

Green skills are the knowledge, abilities and values needed to live and work in a way that protects the environment and supports sustainable development.

Sustainable Development

Problems / Need for Green Skills

• Pollution — air, water, land, noise harming health.
• Global warming & climate change — rising temperatures, melting glaciers.
• Deforestation — loss of trees and wildlife.
• Depletion of resources — overuse of water, fuel, minerals.
• Waste & e-waste — too much garbage and electronic waste.

The 3 R's & Green Practices

📝 Practice Question Bank — Green Skills

Artificial Intelligence (AI) is the ability of a machine to perform tasks that normally require human intelligence — like seeing, understanding language, making decisions and learning from experience.

1.1 The Story of Intelligence

Before we can understand artificial intelligence, we must understand natural intelligence — the kind you use every day. When you wake up and decide what to wear by looking out of the window, when you recognise your friend's face in a crowd, when you understand a joke, or when you learn from a mistake in a maths sum and avoid it next time — you are using intelligence.

Psychologists describe intelligence as a bundle of abilities. The human brain blends many of them together so smoothly that we rarely notice. AI scientists try to give machines these same abilities, one at a time.

1.2 What is Artificial Intelligence?

A machine is said to be artificially intelligent when it can copy human abilities such as learning, reasoning and self-correction. The key idea is that the machine is not just following fixed instructions — it studies data, finds patterns, and then makes its own decision, improving as it sees more data.

1.3 Three Types of AI (by capability)

What is Intelligence? What is AI?

Intelligence is the ability to learn, reason, understand and make decisions. A machine is called "artificially intelligent" when it can mimic these human abilities. AI machines work on data — they learn patterns from data and use them to take decisions.

What is NOT AI?

A machine that only follows fixed instructions is automation, not AI. A washing machine, a calculator, or a remote-controlled car is automatic but not intelligent, because it cannot learn from data or improve itself.

The Three Domains of AI

Most real AI products combine two or three domains. A self-driving car, for example, uses Computer Vision to see the road, Data Sciences to predict the safest speed, and even NLP to take your voice commands.

Applications of AI in Daily Life

The AI Project Cycle — 6 Stages

The AI Project Cycle is a step-by-step framework to build any AI project. Remember the order: Problem Scoping → Data Acquisition → Data Exploration → Modeling → Evaluation → Deployment.

Think of it like cooking a new dish. First you decide what to cook (problem scoping), then gather ingredients (data acquisition), wash and chop them (data exploration), cook using a recipe (modeling), taste and adjust (evaluation), and finally serve it to guests (deployment). If the guests don't like it, you go back and change the recipe — that is why the cycle is iterative (it repeats).

Stage 1 · Problem Scoping & the 4Ws Canvas

Problem scoping means clearly identifying the problem. The 4Ws Problem Canvas helps:

Stage 2 · Data Acquisition

Data = facts and figures. Data features are the pieces of information needed to solve the problem. Data can be collected from surveys, sensors, interviews, web/APIs, cameras, or existing databases. Good data must be reliable, relevant and authentic.

A System Map shows the relationship between different data features using arrows (positive "+" and negative "–" relationships).

Stage 3 · Data Exploration / Visualisation

We make data easy to understand using graphs. Choosing the right graph is a key skill:

Stage 4 · Modeling (Rule-Based vs Learning-Based)

Stage 5 · Evaluation (Confusion Matrix Terms)

To check how good a model is, we compare its prediction with the reality:

Stage 6 · Deployment

Deployment means putting the tested model into real-world use so people can benefit from it — e.g. a trained model placed in an app, a hospital, or a website. (Case study: Preventable Blindness detection.)

AI Ethics, Bias & Access

📝 Practice Question Bank — AI, Project Cycle & Ethics

This bank now has 7 question types: MCQ, True/False, Fill-in-the-blanks, Assertion–Reason, Match the following, Competency-based and Theory. Use the tabs to switch.

Data Literacy is the ability to read, understand, create and communicate data as information. A data-literate person can collect data, analyse it, draw meaning and make informed decisions.

2.1 What exactly is "Data"?

Data are raw, unorganised facts and figures — numbers, words, measurements, images — that on their own may not mean much. When data is organised and given context, it becomes information. This journey is called the DIKW Pyramid.

Why Data Literacy Matters

How to Become Data Literate — 4 Steps

Data Security & Data Privacy

Types of Data

Acquiring, Processing & Interpreting Data

Methods / Types of Data Interpretation

• Quantitative interpretation — analysing numerical data (totals, averages, percentages).
• Qualitative interpretation — analysing descriptive data (themes, opinions, categories).
• Trend analysis — studying how data changes over time to predict the future.

Data Visualisation (Project: Interactive Dashboard)

Data visualisation = showing data using charts, graphs and dashboards so patterns are easy to see. Tools include Tableau, Datawrapper and spreadsheets (Excel / Google Sheets). A dashboard combines several charts on one screen to monitor information at a glance.

📝 Practice Question Bank — Data Literacy

AI is built on mathematics. Machines analyse data in the form of numbers and images and find patterns and relationships to make decisions.

Whenever an AI recognises your face, recommends a song, or forecasts rain, behind the scenes it is doing mathematics — comparing numbers, calculating averages and chances, and spotting patterns. You do not need to be a maths genius; you only need the core ideas of statistics (summarising data) and probability (measuring chance).

Branches of Math Used in AI

Number Patterns & Picture Analogy

Finding the rule in a sequence (e.g. 2, 4, 8, 16, __ → 32, rule ×2) and connecting sets of images (picture analogy) are core "pattern-finding" skills that AI uses on a large scale.

Statistics — Measures of Central Tendency

Applications of Statistics

Probability

Types of Events

Applications of Probability

Used in sports (chance of winning), weather forecasting (70% chance of rain), and traffic estimation (likelihood of congestion). AI uses probability to make predictions under uncertainty.

📝 Practice Question Bank — Math for AI

Generative AI is a type of artificial intelligence that can create new, original content — text, images, music, audio, video or code — that resembles human-made content, by learning patterns from huge amounts of training data.

For most of its history, AI was a judge — it looked at something and gave a verdict: "spam or not", "cat or dog". Generative AI flips this: it is an artist. Give it a few words (a prompt) and it paints a picture, writes a poem, or composes a tune that never existed before. The leap happened because of huge datasets and powerful models called transformers and GANs.

Generative AI vs Conventional (Traditional) AI

How Does Generative AI Learn?

Types & Examples of Generative AI

Benefits & Limitations

Ethical Considerations

• Misinformation & deepfakes — fake images/videos can mislead people.
• Copyright & plagiarism — who owns AI-generated content?
• Bias — output reflects biases in training data.
• Job impact — automation may affect some jobs.
• Privacy — personal data may be used without consent.

📝 Practice Question Bank — Generative AI

Python is a popular, high-level, easy-to-read programming language created by Guido van Rossum (1991). It is the most-used language for AI & Machine Learning because of its simple syntax and powerful libraries.

A computer does exactly what it is told — no more, no less. A program is a set of step-by-step instructions written in a language the computer understands. Python is loved by beginners because its instructions read almost like plain English. A program flows in three simple stages: Input → Process → Output.

Why Python for AI?

print() and input()

print("Hello, World!")          # output function — shows text on screen
name = input("Enter your name: ")  # input function — takes data from user
print("Welcome", name)

Variables & Data Types

A variable is a named container that stores a value. Python decides the type automatically.

Operators

Flow of Control — Conditions & Loops

# Conditional statement (if-elif-else)
num = int(input("Enter a number: "))
if num > 0:
    print("Positive")
elif num < 0:
    print("Negative")
else:
    print("Zero")

# for loop — first 5 natural numbers
for i in range(1, 6):
    print(i)

# while loop — count down
n = 5
while n > 0:
    print(n)
    n -= 1

Lists

A list is an ordered, changeable collection written in square brackets [ ]. Items have an index starting from 0.

fruits = ["apple", "banana", "mango"]
print(fruits[0])        # apple  (positive index)
print(fruits[-1])       # mango  (negative index)
fruits.append("orange") # add at end
fruits.remove("banana") # delete an item
print(len(fruits))      # length of list

📝 Practice Question Bank — Python

The practical file needs a minimum of 15 programs. Below are the complete, ready-to-run programs from the official syllabus list, grouped by topic. Type them in any Python compiler (IDLE, Thonny, online compiler) and note the output.

A · PRINT Programs

1. Print personal information

print("Name: Naren Kumar")
print("Father's Name: Mr. XYZ")
print("Class: IX")
print("School Name: Kendriya Vidyalaya")

2. Square of number 7  |  3. Sum of 15 and 20

print("Square of 7 =", 7 * 7)        # 49
print("Sum =", 15 + 20)              # 35

4. Convert kilometres to metres  |  5. Table of 5 (five terms)

km = 5
print(km, "km =", km * 1000, "metres")
for i in range(1, 6):
    print("5 x", i, "=", 5 * i)

6. Simple Interest

principle_amount = 2000
rate_of_interest = 4.5
time = 10
SI = (principle_amount * rate_of_interest * time) / 100
print("Simple Interest =", SI)       # 900.0

B · INPUT Programs

7. Area & Perimeter of a rectangle

l = int(input("Enter length: "))
b = int(input("Enter breadth: "))
print("Area =", l * b)
print("Perimeter =", 2 * (l + b))

8. Area of a triangle  |  9. Average of 3 subjects

base = float(input("Base: "))
height = float(input("Height: "))
print("Area of triangle =", 0.5 * base * height)

m1 = int(input("Marks 1: ")); m2 = int(input("Marks 2: ")); m3 = int(input("Marks 3: "))
print("Average =", (m1 + m2 + m3) / 3)

10. Discounted amount  |  11. Surface Area & Volume of a Cuboid

price = float(input("Price: "))
disc = float(input("Discount %: "))
print("Final price =", price - (price * disc / 100))

l = float(input("Length: ")); b = float(input("Breadth: ")); h = float(input("Height: "))
print("Surface Area =", 2 * (l*b + b*h + h*l))
print("Volume =", l * b * h)

C · LIST Programs

12. Quiz students list operations

students = ["Arjun", "Sonakshi", "Vikram", "Sandhya", "Sonal", "Isha", "Kartik"]
print(students)              # whole list
students.remove("Vikram")    # delete Vikram
students.append("Jay")       # add Jay at end
students.pop(1)              # remove item at 2nd position (index 1)
print(students)

13. Indexing on num list

num = [23, 12, 5, 9, 65, 44]
print("Length:", len(num))
print(num[1:4])     # 2nd to 4th position (positive index) -> [12, 5, 9]
print(num[-4:-1])   # 3rd to 5th using negative index -> [5, 9, 65]

14. First 10 even numbers, add 1 to each

evens = []
for i in range(2, 21, 2):
    evens.append(i)
new_list = [x + 1 for x in evens]
print(new_list)     # [3, 5, 7, ... 21]

15. extend() and sort()

List_1 = [10, 20, 30, 40]
List_1.extend([14, 15, 12])
List_1.sort()
print(List_1)       # [10, 12, 14, 15, 20, 30, 40]

D · IF / FOR / WHILE Programs

16. Can the person vote?  |  17. Positive/Negative/Zero

age = int(input("Enter age: "))
if age >= 18:
    print("You can vote")
else:
    print("You cannot vote")

n = int(input("Enter a number: "))
if n > 0:
    print("Positive")
elif n < 0:
    print("Negative")
else:
    print("Zero")

18. Grade of a student

marks = int(input("Enter marks: "))
if marks >= 90:    print("Grade A")
elif marks >= 75:  print("Grade B")
elif marks >= 60:  print("Grade C")
elif marks >= 33:  print("Grade D")
else:              print("Fail")

19. First 10 natural / even / odd numbers & their sum

for i in range(1, 11):     # first 10 natural numbers
    print(i, end=" ")

for i in range(2, 21, 2):  # first 10 even numbers
    print(i, end=" ")

n = int(input("\nEnter n: "))
for i in range(1, n+1):    # odd numbers from 1 to n
    if i % 2 != 0:
        print(i, end=" ")

total = 0
for i in range(1, 11):     # sum of first 10 natural numbers
    total += i
print("\nSum =", total)    # 55

20. Sum of all numbers stored in a list

numbers = [4, 8, 15, 16, 23, 42]
total = 0
for x in numbers:
    total += x
print("Sum of list =", total)   # 108  (or use sum(numbers))

You must complete any one of the following, related to the Sustainable Development Goals (SDGs).

Option 1 · Build an AI Model

Option 2 · An SDG Problem-Solving Project

Pick an SDG issue and follow the AI Project Cycle:

Option 3 · Field Visit

Visit (physically or virtually) an industry, IT company or place that creates or uses AI. Observe how AI is applied and submit a report covering: place visited, AI applications seen, learnings, and your reflections.

Option 4 · Student Portfolio (min. 5 activities)

• Letter to Future Self — what AI skills you want to gain.
• Smart Home Floor Plan — design an AI-enabled home.
• Future Job Advertisement — imagine an AI job of 2040.
• Research Work — AI for SDGs / AI in different sectors.
• 4Ws Canvas & System Map — for a chosen problem.

This is the official KVS Delhi Region Blueprint for the Class IX AI (417) Annual Examination 2025–26. The paper is 50 marks (Theory) for 2 hours, split into Part A (Employability, 10 marks) and Part B (Subject-Specific, 40 marks). All three official sets (Set-1, Set-2, Set-3) follow the same blueprint structure.

Part A — Employability Skills (10 Marks)

Part B — Subject-Specific Skills (40 Marks)

Weightage by Unit (Part B)

These are the three official KVS Delhi Region Annual Examination papers for Class IX AI (417), reproduced exactly as set, each with the official marking scheme. Pattern: 21 questions (Section A objective 24 marks + Section B subjective 26 marks); answer 15 (5 + 10) in 2 hours. Click "Show Answer & Marking" under any question for the official answer.

temp = float(input("Enter current room temperature: "))
if temp < 18:
    print("Heater ON")
elif temp >= 18 and temp <= 25:
    print("Economy Mode: Both OFF")
else:
    print("Cooler ON")

a = int(input("Enter first number: "))
b = int(input("Enter second number: "))
prod = a * b
print(prod)

marks = [45, 88, 72, 91, 56]
for x in marks:            # 1 mark for loop
    print(x)               # 1 mark for printing mark
    if x > 75:             # 1 mark for condition
        print("Distinction")   # 1 mark for printing message

import calendar
yy = int(input("Enter year: "))
mm = int(input("Enter month: "))
print(calendar.month(yy, mm))

Each paper follows the CBSE pattern: Employability Skills (10 marks) + Subject-Specific AI Skills (40 marks). Time: 2 hours. Try each paper in exam conditions, then check answers from the unit question banks.

This chapter walks you through building, documenting and presenting your Class IX AI project from a blank page to a finished, marks-scoring submission. Your project carries 15 marks and must connect to a Sustainable Development Goal (SDG).

How to pick a winning idea

The best Class IX projects are small, local and real. Don't try to "solve world hunger" — instead solve a problem you can actually see around you and link it to an SDG. Use this simple test: Is it real? Is data available? Can AI help? If all three are "yes", it's a good topic.

Step 1 — Problem Scoping (4Ws Canvas)

Fill this canvas for your chosen topic. Example shown for a waste-sorting project.

Step 2 — Data Acquisition & System Map

List the data features you need and draw a system map showing how they connect. For waste-sorting you need photos of dry items (paper, plastic, metal) and wet items (food, leaves). Collect 30–50 photos per category for a decent model.

Step 3 — Build the Model with Teachable Machine

Teachable Machine (teachablemachine.withgoogle.com) lets you train an AI model with no coding. Follow these steps:

Step 4 — Evaluate & Step 5 — Present (Deploy)

Test your model on 10 new items it has never seen. Count how many it gets right (that's your accuracy). Note any items it confuses — this is your evaluation. Finally "deploy" by demonstrating it live in class or embedding the model link in a simple webpage.

Project Report — recommended structure

1. Cover page — title, your name, class, school, session.
2. Acknowledgement & Index.
3. Introduction — the problem & the SDG it addresses.
4. 4Ws canvas and problem statement.
5. Data — features, sources, sample photos, a chart/graph.
6. System map and the AI approach used.
7. Model building — screenshots of Teachable Machine steps.
8. Evaluation — accuracy, what worked, what confused it.
9. Conclusion & future scope, Bibliography.

How marks are typically awarded (15)

Alternative: Field Visit & Portfolio

If you choose a field visit, submit a report with: place visited, AI applications observed, photos, your learnings and reflection. For a portfolio, collect at least 5 activities (Letter to Future Self, Smart Home Floor Plan, Future Job Ad, AI-for-SDGs research, 4Ws canvas & system map) in a neat folder.

The theory paper is 50 marks — Employability Skills (10) + Subject-Specific AI (40). Knowing the pattern and writing smartly can add easy marks. Here is how to approach it.

Marks Blueprint (Theory 50)

Smart writing tips by question type

High-frequency topics (revise these first)

• The 6 stages of the AI Project Cycle and the 4Ws canvas.
• Three domains of AI & AI vs ML vs DL.
• TP, TN, FP, FN (confusion matrix) and rule-based vs learning-based.
• Mean, median, mode and basic probability sums.
• Generative AI vs conventional AI; GAN; ethics & deepfakes.
• Python print/input, data types, if/for/while, and lists.
• Data privacy vs security; 7 C's of communication; SWOT; 3 R's.

7-Day Revision Plan

Every important term in the AI-417 Class IX course, defined simply. Use this for last-minute revision and to answer "define" questions precisely.