Artificial Intelligence

Total Marks: 100 → Theory 50 + Practical 50. Part A (Employability, 10 marks) + Part B (Subject-Specific AI, 40 marks) form the 50-mark theory; Practical, Project & Viva form the other 50.

Part A — Employability Skills (10 marks)

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

Part C — Practical & Project (50 marks)

Theory Paper Pattern (50 marks · 2 hours)

Detailed Topic List (Part B)

Follow a Read → Visualise → Test → Solve Papers loop. Everything works offline in any browser.

In Class X we revisit the AI Project Cycle and the three domains, then go deeper into the ethics of AI through formal ethical frameworks — the rules and values that keep AI fair and safe.

1.1 The AI Project Cycle (recap)

Every AI project follows six stages. You learnt these in Class IX; here is the quick map you must remember.

Let us understand each stage and its key terms in detail, using one running example — an AI project to predict whether it will rain tomorrow so a farmer can plan irrigation.

① Problem Scoping

The main tool here is the 4Ws Problem Canvas — four questions that frame the problem:

Stakeholders = all the people who are affected by or interested in the problem. Problem statement = one sentence summarising the 4Ws.

② Data Acquisition

③ Data Exploration

④ Modeling

⑤ Evaluation

(Detailed metrics like Accuracy, Precision, Recall & F1 are covered fully in Unit 3 – Evaluating Models.)

⑥ Deployment

1.2 The Three Domains of AI (recap)

2.1 What is an Ethical Framework?

AI now makes or influences important decisions — who gets a loan, which patient is treated first, what news you see. Because these affect real lives, we need ethical frameworks so that AI choices are responsible and just.

2.2 Types of Ethical Frameworks

Value-based sub-types

• Rights-based: values human rights/life above other considerations.
• Virtue-based: asks whether developers uphold good moral character/values.
• Utility-based: aims to maximise the overall good for the most people.

2.3 Bioethics — a Sector-Based Framework

📘 Terminology Explained (Unit 1)

📝 Practice Question Bank — Unit 1

A model is the "brain" of an AI project — the part that learns from data and makes predictions. This unit explores the different kinds of AI models and the neural networks behind them.

2.1 Differentiating AI, ML and DL

2.2 Common Data Terminology

• Dataset: a collection of related data used to train/test a model.
• Features: the input columns/attributes (e.g. height, weight).
• Label / Target: the output we want to predict (e.g. "species").
• Training data: data used to teach the model; Testing data: unseen data used to check it.

2.3 Rule-Based vs Learning-Based

2.4 Categories of Machine Learning

Supervised sub-types

Unsupervised sub-types

2.5 Artificial Neural Networks (ANN)

2.6 Convolutional Neural Network (CNN)

📘 Terminology Explained (Unit 2)

📝 Practice Question Bank — Unit 2

Building a model is not enough — we must check how good it is. Evaluation tells us whether the model can be trusted on new, unseen data. This is one of the most important (and most-tested) units.

3.1 Why evaluate a model?

Evaluation checks whether the model has truly learned patterns or merely memorised the training data. A model that memorises does great on seen data but fails on new data — this is called overfitting.

3.2 Train-Test Split

3.3 Confusion Matrix

A confusion matrix compares the model's prediction with the actual reality in a 2×2 table.

3.4 The four key metrics

3.5 Ethical concerns in evaluation

• Bias: if the test data is unbalanced, the metrics can be misleading and unfair to some groups.
• Transparency: we should be able to explain how the model reached its decision.
• Accuracy paradox: high accuracy can hide poor performance when classes are imbalanced — so we also use precision & recall.

📘 Terminology Explained (Unit 3)

📝 Practice Question Bank — Unit 3

The Statistical Data domain works with numbers and tables. This unit shows how to build AI models without writing code, using No-Code tools like Orange Data Mining.

4.1 Data Science & Statistical Data

4.2 Code vs No-Code vs Low-Code AI

4.3 AI Project Cycle in Orange (Palmer Penguins)

The famous Palmer Penguins case study predicts a penguin's species from features like bill length, flipper length and body mass — a classification problem. In Orange you connect widgets:

4.4 Important Statistics Concepts

To work with statistical data you need a few basic measures:

4.5 Why No-Code AI matters

📘 Terminology Explained (Unit 4)

📝 Practice Question Bank — Unit 4

Computer Vision (CV) is the AI domain that gives machines the ability to "see" and understand images and videos the way humans do.

5.1 Applications of Computer Vision

5.2 Computer Vision Tasks

5.3 Basics of Images — Pixels, Resolution & Pixel Value

• Pixel: the smallest unit (a tiny dot) of an image.
• Resolution: the number of pixels in an image (e.g. 1920×1080). More pixels = higher quality.
• Pixel value: a number giving the brightness/colour of a pixel. In a byte image it ranges from 0 to 255 (0 = black, 255 = white).

5.4 Convolution Operator

The convolution operator slides a small grid of numbers called a kernel (filter) over the image, multiplying and adding values to produce a new image that highlights features like edges, blur or sharpening.

5.5 Convolutional Neural Network (CNN)

A CNN stacks several layers to recognise images:

📘 Terminology Explained (Unit 5)

📝 Practice Question Bank — Unit 5

Natural Language Processing (NLP) is the AI domain that helps machines understand, interpret and generate human language — text and speech.

6.1 Why is natural language hard for machines?

Human language is full of ambiguity. The same word can mean different things depending on context, and feelings change meaning.

• Multiple meanings (a word means different things in different contexts).
• Synonyms (many words for the same idea).
• Syntax & grammar differences and errors.
• Sarcasm & emotion that change literal meaning.

6.2 Applications of NLP

Other applications: text classification, keyword extraction, spam detection and chatbots.

6.3 Stages of NLP

6.4 Script Bot vs Smart Bot

6.5 Text Normalisation

Before a machine can analyse text, it must be cleaned and standardised. Steps include:

6.6 Bag-of-Words (BoW)

The Bag-of-Words model converts text into numbers by counting how often each word appears — ignoring grammar and order.

📘 Terminology Explained (Unit 6)

📝 Practice Question Bank — Unit 6

Class X Python moves from basics to the real tools data scientists use — Jupyter Notebook and powerful libraries like NumPy, Pandas, Matplotlib and OpenCV.

7.1 Jupyter Notebook & Environments

• Jupyter Notebook: a browser tool to write & run Python in "cells", with text, code and charts together.
• Virtual environment: an isolated space so each project keeps its own package versions.
• pip install: command to install packages, e.g. pip install numpy pandas matplotlib.

7.2 Key Libraries

7.3 Python Recap — the essentials

m1=int(input("Marks 1: ")); m2=int(input("Marks 2: ")); m3=int(input("Marks 3: "))
avg=(m1+m2+m3)/3
print("Average:", avg)
if avg>=33:
    print("Pass")
else:
    print("Fail")

7.4 Sample Programs (Libraries)

# Mean, median, mode with NumPy
import numpy as np
from scipy import stats
data = [10, 20, 20, 30, 40]
print("Mean:", np.mean(data))      # 24.0
print("Median:", np.median(data))  # 20.0
print("Mode:", stats.mode(data))   # 20

# Line chart with Matplotlib
import matplotlib.pyplot as plt
plt.plot([2,9], [5,10])     # line from (2,5) to (9,10)
plt.show()

# Read a CSV and show 10 rows with Pandas
import pandas as pd
df = pd.read_csv("data.csv")
print(df.head(10))
print(df.info())

# Read & display an image with OpenCV
import cv2
img = cv2.imread("photo.jpg")
print("Shape:", img.shape)   # (height, width, channels)
cv2.imshow("Image", img); cv2.waitKey(0)

📘 Terminology Explained (Unit 7)

📝 Practice Question Bank — Unit 7

Building on Class IX, this unit deepens communication — methods, the 7 C's, feedback, and sentence types used in writing.

1.1 What is Communication?

Good communication is the number-one employability skill that employers look for. Whether you are giving a presentation, writing an email, attending an interview or working in a team, the quality of your communication decides how well you succeed.

1.2 The Communication Cycle

2.1 Methods / Types of Communication

2.2 The 7 C's of Effective Communication

2.3 Barriers to Communication

3.1 Sentences & Their Types

A sentence is a group of words that gives a complete meaning. It has a subject (who/what) and a predicate (what is said). Sentences are of four types:

3.2 Parts of Speech & Punctuation

The 8 parts of speech are: noun, pronoun, verb, adjective, adverb, preposition, conjunction and interjection. Good writing also uses correct punctuation — full stop (.), comma (,), question mark (?), exclamation mark (!), apostrophe (') and quotation marks (" ") — and starts every sentence with a capital letter.

📘 Terminology Explained (Communication-II)

📝 Practice Question Bank — Communication-II

Class X focuses on stress management and the ability to understand emotions — Emotional Intelligence.

Emotional Intelligence (EI)

2.1 What is Stress?

2.2 The ABC of Stress Management

2.3 Stress-Management Techniques

3.1 Self-Awareness & SWOT

Self-management starts with self-awareness — knowing your feelings, strengths and weaknesses. A useful tool is the SWOT analysis.

3.2 Self-Motivation & SMART Goals

Motivation is the drive to act. It is internal (from interest/satisfaction) or external (rewards/praise). To turn motivation into results, set SMART goals:

📘 Terminology Explained (Self-Management-II)

📝 Practice Question Bank — Self-Management-II

Class X ICT covers the operating system basics, file/folder management and — importantly — computer/online security.

1.1 ICT in Daily Life

ICT skills help us communicate, run a business, stay connected, shop and bank online, work remotely, learn through e-learning and access healthcare via telemedicine.

1.2 Hardware vs Software & the Operating System

An Operating System (OS) — Windows, Linux, macOS, Android — manages all hardware and software and gives the user interface. Common tasks include managing files, running programs and connecting devices.

1.3 File & Folder Management

Computer Security & Strong Passwords

Online Safety Rules

• Never share passwords, OTP or personal details with strangers.
• Don't click unknown links or download files from unknown senders (phishing).
• Use different strong passwords for different accounts.
• Keep the OS and antivirus updated; scan pen drives before use.
• Be careful on social media — once posted, content can spread (digital footprint).
• Take regular backups of important files.

📘 Terminology Explained (ICT-II)

📝 Practice Question Bank — ICT-II

Class X explores the qualities and functions of an entrepreneur and busts common myths.

Qualities of a Successful Entrepreneur

Entrepreneur vs Wage Employment

Functions / Role of an Entrepreneur in Society

How Entrepreneurs Work

📘 Terminology Explained (Entrepreneurial-II)

📝 Practice Question Bank — Entrepreneurial-II

Class X deepens sustainable development, the SDGs and green practices.

Sustainable Development

Green Practices (3 R's)

Environmental Problems (Need for Green Skills)

Green Economy & Green Jobs

📘 Terminology Explained (Green-II)

📝 Practice Question Bank — Green-II

Minimum 15 programs using Python and AI libraries. Below are the official syllabus programs with code & expected output.

1. Add elements of two lists

list1 = [1, 2, 3]
list2 = [4, 5, 6]
result = [list1[i] + list2[i] for i in range(len(list1))]
print(result)        # [5, 7, 9]

2. Mean, median, mode using NumPy

import numpy as np
from scipy import stats
data = [10, 20, 20, 30, 40]
print("Mean:", np.mean(data))       # 24.0
print("Median:", np.median(data))   # 20.0
print("Mode:", stats.mode(data, keepdims=True).mode[0])  # 20

3. Line chart (2,5) to (9,10)

import matplotlib.pyplot as plt
plt.plot([2, 9], [5, 10])
plt.title("Line Chart"); plt.xlabel("X"); plt.ylabel("Y")
plt.show()

4. Scatter chart

import matplotlib.pyplot as plt
x = [2, 9, 8, 5, 6]
y = [5, 10, 3, 7, 18]
plt.scatter(x, y)
plt.title("Scatter Chart"); plt.show()

5 & 6. Read CSV — display 10 rows & info

import pandas as pd
df = pd.read_csv("data.csv")
print(df.head(10))   # first 10 rows
print(df.info())     # column info, datatypes, non-null counts

7 & 8. Read & display an image, find its shape

import cv2
img = cv2.imread("photo.jpg")
cv2.imshow("My Image", img)
cv2.waitKey(0); cv2.destroyAllWindows()
print("Shape (H, W, Channels):", img.shape)

Complete any one AI project (or field visit / portfolio) linked to a Sustainable Development Goal, then face a project viva.

Three Suggested CBSE Projects

Project Steps (AI Project Cycle)

Field Visit & Portfolio

• Field work: AI for Youth Bootcamp, AI fests/exhibitions, AI training sessions, virtual company tours.
• Portfolio (min. 5 activities): records of AI activities, hackathons, competitions (continued from Class IX).

Seven complete papers in the exact CBSE Class X pattern (Theory 50 marks, 2 hours). Each has 21 questions (Section A objective 24 marks + Section B subjective 26 marks); a candidate answers 15 (5 + 10). Click "Show Answer & Marking" under any question for the step-by-step value points and explanation.

Official KVS Delhi Region Blueprint for the Class X AI (417) Pre-Board II 2025–26. The paper is 50 marks (Theory) for 2 hours — Part A (Employability, 10) + Part B (Subject-Specific, 40). All three sets follow this same structure.

Part A — Employability Skills (10 Marks)

Part B — Subject-Specific Skills (40 Marks)

Six official KVS Delhi Region papers for Class X AI (417) — Pre-Board II (Sets 1–3) and Pre-Board I (Sets 1–3) — each with the official marking scheme for every question. Pattern: 21 questions (Section A objective 24 + Section B subjective 26); answer 15 (5 + 10) in 2 hours. Click "Show Answer & Marking" under any question.