Artificial Intelligence (AI) is a branch of computer science that enables machines and computers to think, learn, and make decisions like humans.
In simple words: “AI is the ability of a machine to behave intelligently.“
An AI system can:
Learn from data
Identify patterns
Make decisions
Improve its performance over time
💡 Important Point: AI does not mean robots only. Many AI systems work silently inside computers, mobiles, and apps.
🔹 AI as a Problem-Solving Technology
AI is mainly used to solve real-world problems that are:
Too complex for humans
Require analysis of large data
Need quick and accurate decisions
AI helps by:
Studying the problem
Collecting and analyzing data
Finding the best possible solution
📌 Example: Humans cannot analyze millions of online searches in seconds, but AI can.
🔹 Examples of AI in Daily Life
AI is already part of our everyday life:
Search Engines
Google suggests results based on our searches
Auto-complete suggestions
Voice Assistants
Google Assistant, Alexa, Siri
Understand voice commands and respond
Recommendation Systems
YouTube video suggestions
Netflix movie recommendations
Online shopping product suggestions
Smartphones
Face unlock
Camera filters
Spam call detection
📌 These systems learn from user data and behavior, which makes them AI-based.
2️⃣ AI PROJECT CYCLE – OVERVIEW
🔹 What is an AI Project Cycle?
An AI Project Cycle is a step-by-step process used to build an AI solution for a real-world problem.
Just like:
A science experiment has steps
A cooking recipe follows steps
👉 AI projects also follow fixed steps to ensure accuracy and success.
🔹 Why Do AI Projects Follow a Step-by-Step Cycle?
AI projects follow a cycle because:
It helps understand the problem clearly
It avoids confusion and mistakes
It ensures proper use of data
It improves the final result
📌 Skipping any step may lead to:
Wrong results
Biased AI
Failed AI solutions
🔹 Relationship Between Real-World Problems and AI Solutions
AI projects always start with a real-life problem, such as:
Crop failure
Disease detection
Pollution monitoring
Traffic management
AI uses:
Data
Technology
Logic
to create a solution for the problem.
👉 No problem = No AI project
3️⃣ STAGES OF AI PROJECT CYCLE (INTRODUCTORY LEVEL)
The AI Project Cycle has five main stages:
🔸 Stage 1: Problem Scoping
This is the most important stage.
Here we:
Identify the problem
Understand who is affected
Decide what needs to be solved
📌 If the problem is not clear, the AI solution will fail.
🔸 Stage 2: Data Acquisition
Data is the fuel of AI.
In this stage:
Data is collected from different sources
The data may be:
Text
Numbers
Images
Audio
📌 Without data, AI cannot learn.
🔸 Stage 3: Data Exploration
After collecting data, we:
Study the data
Find patterns
Remove errors or irrelevant data
This helps in understanding:
What the data tells us
What information is useful
🔸 Stage 4: Modelling
In this stage:
AI models are created
The machine is trained using data
The AI learns patterns
📌 This is where actual intelligence is developed.
🔸 Stage 5: Evaluation
In the final stage:
The AI solution is tested
Accuracy is checked
Performance is evaluated
If results are not good:
The process is repeated
Improvements are made
4️⃣ 4Ws FRAMEWORK FOR PROBLEM SCOPING
To clearly define a problem, we use the 4Ws Framework.
🔹 Who is Affected?
Who faces the problem?
Farmers, students, doctors, common people?
📌 Identifying affected people helps focus the solution.
🔹 What is the Problem?
What exactly is going wrong?
What needs to be solved?
📌 The problem should be clear and specific.
🔹 Where Does It Occur?
In a village, city, school, hospital, or country?
At a local or global level?
📌 Location helps decide the scale of the solution.
🔹 Why Does It Matter?
Why should this problem be solved?
What happens if it is ignored?
📌 This shows the importance of the problem.
5️⃣ AI FOR SUSTAINABLE DEVELOPMENT GOALS (SDGs
🔹 What Are SDGs?
Sustainable Development Goals (SDGs) are global goals set by the United Nations to:
Improve quality of life
Protect the environment
Ensure equality and development
There are 17 SDGs, such as:
No Poverty
Zero Hunger
Good Health
Quality Education
Climate Action
🔹 Understanding SDGs in Simple Terms
SDGs aim to:
Solve major world problems
Create a better future for everyone
Balance development and environment
🔹 Role of AI in Solving Global Problems
AI helps in achieving SDGs by:
Predicting crop diseases
Monitoring climate change
Improving healthcare diagnosis
Managing resources efficiently
📌 AI supports smart and sustainable solutions.
6️⃣ SUGGESTED ACTIVITIES (EXPLAINED)
🔹 SDG-Based AI Problem Identification
Students identify a real problem related to an SDG
Apply AI Project Cycle steps
🔹 Case Study 1: Coffee Production System
Problem: Low coffee yield
AI use: Predict soil health and weather
SDG: Zero Hunger, Decent Work
🔹 Case Study 2: Earth-Like Exoplanet Discovery
Problem: Finding habitable planets
AI use: Analyze space data
SDG: Innovation and Scientific Progress
🔹 Group Discussion Using 4Ws Framework
Students work in groups
Apply Who, What, Where, Why
Present AI-based solutions
CHAPTER 2- Data and Problem Scoping in Artificial Intelligence
1️⃣ DETAILED UNDERSTANDING OF EACH AI PROJECT STAGE
In CHAPTER 1, we learned the names of the stages of the AI Project Cycle. In CHAPTER 2, we understand why each stage is important and what happens if it is ignored.
The five stages are:
Problem Scoping
Data Acquisition
Data Exploration
Modelling
Evaluation
Each stage plays a critical role in building a successful AI system.
2️⃣ WHY PROBLEM SCOPING IS IMPORTANT
🔹 Meaning of Problem Scoping
Problem Scoping means:
Clearly defining the problem
Understanding the needs of the people affected
Setting clear goals for the AI solution
🔹 Importance of Problem Scoping
Problem scoping is important because:
It gives direction to the AI project
It avoids solving the wrong problem
It saves time, money, and effort
📌 If the problem is not clear:
The AI system may give useless results
The project may fail completely
🔹 Example
Problem: “Farmers are facing crop loss.”
If not scoped properly:
AI may predict weather only But the real problem might be:
Soil health
Pest attacks
Lack of irrigation
👉 Correct problem scoping ensures the right AI solution.
3️⃣ IMPORTANCE OF CORRECT AND SUFFICIENT DATA
🔹 Why Data Is Important in AI
Data is the foundation of AI.
AI systems:
Learn from data
Make decisions using data
Improve with more data
📌 No data = No AI
🔹 Correct Data
Correct data means:
Accurate data
Relevant to the problem
Up-to-date information
Wrong data leads to:
Wrong predictions
Biased decisions
Unsafe AI systems
🔹 Sufficient Data
Sufficient data means:
Enough quantity of data
Covers different situations
📌 Too little data:
AI cannot learn properly
📌 Too much but irrelevant data:
Confuses the AI system
4️⃣ ROLE OF DATA EXPLORATION BEFORE BUILDING AI MODELS
🔹 What Is Data Exploration?
Data exploration means:
Studying collected data
Understanding its structure
Finding patterns and trends
🔹 Why Data Exploration Is Necessary
Before building AI models, data must be explored because:
It helps detect missing values
It removes unwanted data
It improves accuracy of AI models
📌 Skipping data exploration can cause:
Poor performance
Biased AI
Incorrect outputs
5️⃣ DATA ACQUISITION
🔹 What Is Data?
Data is a collection of facts, figures, or information used by AI systems to learn and make decisions.
Examples:
Words in a message
Photos
Sounds
Numbers
🔹 Types of Data
1️⃣ Text Data
Emails
Messages
Reviews
Articles
📌 Used in:
Chatbots
Translation apps
Sentiment analysis
2️⃣ Image Data
Photographs
Medical images
Satellite images
📌 Used in:
Face recognition
Object detection
Medical diagnosis
3️⃣ Audio Data
Voice recordings
Music
Phone calls
📌 Used in:
Voice assistants
Speech recognition
Call analysis
4️⃣ Numerical Data
Marks
Temperature readings
Sales figures
📌 Used in:
Weather prediction
Financial analysis
Performance tracking
🔹 Sources of Data
1️⃣ Government Portals
Census data
Weather data
Health statistics
📌 Reliable and authentic source
2️⃣ Surveys
Questionnaires
Feedback forms
📌 Used to collect opinions and responses
3️⃣ Sensors
Temperature sensors
Motion sensors
Cameras
📌 Used in smart devices and machines
4️⃣ Online Datasets
Open-source platforms
Educational datasets
📌 Easily accessible for AI projects
6️⃣ DATA EXPLORATION
🔹 Cleaning Data
Data cleaning means:
Removing duplicate data
Filling missing values
Correcting errors
📌 Clean data improves AI accuracy.
🔹 Understanding Patterns
Pattern recognition includes:
Finding trends
Grouping similar data
Identifying relationships
📌 Patterns help AI make predictions.
🔹 Identifying Errors or Bias in Data
Bias means:
Data favors one group over another
Errors include:
Wrong values
Incomplete information
📌 Identifying bias is important to build fair AI systems.
7️⃣ TYPES OF AI SYSTEMS
🔹 Rule-Based AI
Rule-Based AI works on:
Pre-defined rules
“If–Then” conditions
📌 Example:
If temperature > 38°C → Alert
🔹 Characteristics:
Simple
No learning ability
Works only within rules
🔹 Learning-Based AI
Learning-Based AI:
Learns from data
Improves over time
Uses patterns instead of fixed rules
📌 Example:
Spam email detection
Recommendation systems
🔹 Characteristics:
Flexible
Intelligent
Can handle complex problems
🔹 Comparison Example
Rule-Based AI
Learning-Based AI
Uses fixed rules
Learns from data
No improvement
Improves with experience
Limited tasks
Complex tasks
Example: Calculator
Example: Voice assistant
8️⃣ SUGGESTED ACTIVITIES (EXPLAINED)
🔹 Collecting Datasets from Government Websites
Students explore public data portals
Identify useful datasets for AI projects
🔹 Hands-On Practice of Data Collection & Exploration
Collect sample data
Clean and analyze it
🔹 Rule-Based vs Learning-Based AI Demonstration
Compare outputs
Discuss advantages and limitations
🔹 AI Project Cycle Quiz
Questions from all stages
Concept-based assessment
CHAPTER 3- Ethics in Artificial Intelligence
1️⃣ ETHICS – BASIC MEANING
🔹 What Are Ethics?
Ethics are a set of moral values and principles that guide human behavior and help us decide:
What is right
What is wrong
What is fair
What is unfair
Ethics help people:
Take responsible decisions
Treat others with respect
Live peacefully in society
📌 Ethics are not written laws, but moral rules followed by individuals and society.
🔹 Why Are Ethics Important in Human Decisions?
Ethics are important because they:
Guide our actions and choices
Prevent harm to others
Promote honesty, fairness, and justice
📌 Without ethics:
Society would face chaos
People could misuse power and technology
Example: Telling the truth, helping others, respecting privacy—all are ethical actions.
2️⃣ AI ETHICS
🔹 Meaning of AI Ethics
AI Ethics refers to the moral principles and rules that guide:
The design
The development
The use
of Artificial Intelligence systems.
AI ethics ensures that AI:
Is safe
Is fair
Respects human values
Does not harm individuals or society
🔹 Difference Between Human Ethics and AI Ethics
Human Ethics
AI Ethics
Followed by humans
Applied to AI systems
Based on emotions and values
Based on data and programming
Humans take responsibility
Humans are responsible for AI actions
Flexible thinking
Works within given rules
📌 Important Point: AI does not think on its own. Humans decide the ethics for AI.
3️⃣ WHY AI ETHICS MATTER
AI systems influence:
Education
Healthcare
Employment
Security
Social media
Therefore, ethical AI use is extremely important.
🔹 Bias in AI Systems
Bias means:
Favoring one group over another unfairly
AI bias occurs when:
Training data is incomplete
Data represents only one group
Human bias enters the system
📌 Example: If an AI system is trained mostly on data from one region, it may give wrong results for other regions.
🔹 Privacy and Data Security
AI systems collect and use large amounts of personal data.
Ethical concerns include:
Data misuse
Data theft
Unauthorized access
📌 Example: Using someone’s personal data without permission is unethical.
🔹 Fairness and Inclusiveness
Ethical AI should:
Treat all people equally
Work for all genders, communities, and age groups
Avoid discrimination
📌 Fair AI promotes equal opportunities.
🔹 Accountability in AI Decisions
Accountability means:
Taking responsibility for AI decisions
Important questions:
Who is responsible if AI makes a mistake?
Who will fix the problem?
📌 Humans, not machines, must be accountable.
4️⃣ RESPONSIBLE USE OF AI
🔹 Safe and Ethical Use of AI Tools
Responsible AI use means:
Using AI for positive purposes
Avoiding harm
Following rules and guidelines
Students should:
Not misuse AI tools
Respect privacy
Use AI ethically in studies and daily life
🔹 Impact of Unethical AI on Society
Unethical AI can:
Spread false information
Increase inequality
Violate privacy
Cause job loss unfairly
📌 Ethical AI protects society from harm.
5️⃣ SUGGESTED ACTIVITIES (EXPLAINED)
🔹 Classroom Debates on Ethical AI Use
Students discuss benefits and risks of AI
Develop critical thinking skills
🔹 Balloon Debate on AI Ethical Dilemmas
Each student represents an AI issue
Students argue why their issue is important
Encourages ethical reasoning
🔹 Case Discussions on Biased AI Systems
Real-life examples of biased AI
Identify problems and solutions
UNIT–2: Project 0: Presentation
CHAPTER 4- Domains of Artificial Intelligence
1️⃣ DOMAINS OF ARTIFICIAL INTELLIGENCE
Artificial Intelligence is divided into different domains based on the type of data used and the nature of the problem solved.
Understanding AI domains helps us:
Choose the correct AI approach
Build accurate AI solutions
Avoid wrong or ineffective models
There are three main AI domains at this level:
Data Domain
Computer Vision Domain
Natural Language Processing (NLP) Domain
2️⃣ DATA DOMAIN
🔹 Meaning of Data Domain
The Data Domain deals with:
Numerical data
Structured data
Patterns and trends in data
In this domain, AI systems:
Analyze numbers
Find relationships
Make predictions
🔹 Types of Data Used
Marks of students
Weather readings
Sales records
Health statistics
🔹 Applications of Data Domain
Weather prediction
Disease risk analysis
Performance evaluation
Market trend analysis
📌 Key Idea: If the problem mainly involves numbers and statistics, it belongs to the Data Domain.
3️⃣ COMPUTER VISION DOMAIN
🔹 Meaning of Computer Vision
Computer Vision is the domain of AI that enables machines to:
See
Understand
Interpret images and videos
Just like human eyes and brain work together, AI systems use:
Cameras
Image data
Algorithms
to understand visual information.
🔹 Types of Data Used
Images
Videos
Live camera feeds
🔹 Applications of Computer Vision
Face recognition
Traffic monitoring
Object detection
Medical image analysis
📌 Key Idea: If the problem involves images or videos, it belongs to the Computer Vision Domain.
4️⃣ NATURAL LANGUAGE PROCESSING (NLP) DOMAIN
🔹 Meaning of NLP
Natural Language Processing (NLP) is the AI domain that helps machines:
Understand human language
Read text
Listen to speech
Respond meaningfully
NLP allows humans to communicate with machines naturally.
🔹 Types of Data Used
Text messages
Emails
Voice commands
Documents
🔹 Applications of NLP
Chatbots
Voice assistants
Language translation
Sentiment analysis
📌 Key Idea: If the problem involves text or speech, it belongs to the NLP Domain.
5️⃣ MAPPING AI SOLUTIONS TO AI DOMAINS
🔹 What Does Mapping Mean?
Mapping means:
Identifying the problem
Matching it with the correct AI domain
Correct mapping ensures:
Better accuracy
Efficient AI solution
Proper use of resources
🔹 Identifying the Correct AI Domain for a Problem
Ask these questions:
Does the problem involve numbers? → Data Domain
Does it involve images or videos? → Computer Vision
Does it involve text or speech? → NLP
📌 Some problems may use more than one domain.
🔹 How Applications Use Different Domains
Many AI applications combine domains.
Example: Voice Assistant:
NLP → Understand voice command
Data Domain → Process information
6️⃣ AI-BASED APPLICATIONS (REAL-WORLD EXAMPLES)
🔹 Example 1: Face Unlock in Mobile Phones
Domain: Computer Vision
Data: Images of faces
🔹 Example 2: Online Exam Result Analysis
Domain: Data Domain
Data: Marks and scores
🔹 Example 3: Chatbots on Websites
Domain: NLP
Data: Text and speech
🔹 Example 4: Traffic Signal Monitoring
Domain: Computer Vision + Data Domain
Data: Video feeds and traffic data
7️⃣ MATCHING PROBLEMS WITH AI DOMAINS
Problem
AI Domain
Predicting rainfall
Data Domain
Detecting faces
Computer Vision
Translating languages
NLP
Reading handwritten text
Computer Vision + NLP
📌 Correct matching is a key learning outcome of this unit.
8️⃣ SUGGESTED ACTIVITIES (EXPLAINED)
🔹 Student Presentations
Students explain AI domains
Use real-life examples
Improve communication skills
🔹 AI Application Matching Exercises
Match problems with correct AI domains
Identify single or multiple domain usage
🔹 Group-Based AI Solution Discussions
Students work in groups
Identify problem → select domain → explain solution
CHAPTER 5- AI Use Cases and Project Cycle Applications
1️⃣ AI USE CASE ANALYSIS
🔹 What Is an AI Use Case?
An AI use case is a real-life situation or problem where Artificial Intelligence can be applied to:
Improve efficiency
Save time
Increase accuracy
Support decision-making
📌 In simple words:
An AI use case explains how AI is used to solve a specific real-world problem.
🔹 Understanding a Real-World AI Use Case
To understand an AI use case, we must know:
What problem exists
Who is affected
How AI can help
What data is required
What outcome is expected
📌 Example: Predicting crop disease using AI is an AI use case in agriculture.
2️⃣ BREAKING THE USE CASE INTO AI PROJECT STAGES
Every AI use case follows the AI Project Cycle. Let us understand how a real-world problem is divided into stages.
3️⃣ MAPPING AI USE CASE TO AI PROJECT CYCLE
🔸 Stage 1: Problem Identification
In this stage:
The real problem is clearly identified
The goal of the AI solution is defined
📌 Questions asked:
What is the problem?
Who faces it?
Why is it important to solve?
Example: Farmers suffer losses due to crop disease.
🔸 Stage 2: Data Collection
In this stage:
Relevant data is collected
Data may come from:
Sensors
Records
Surveys
Images
📌 Data must be:
Accurate
Sufficient
Relevant
Example: Crop images, weather data, soil data.
🔸 Stage 3: Data Exploration
Here:
Data is cleaned
Errors are removed
Patterns are studied
📌 This helps in understanding:
Trends
Relationships
Useful information
Example: Identifying patterns between weather conditions and crop disease.
🔸 Stage 4: Model Idea (Modelling)
In this stage:
An AI model is planned
The system learns from data
Predictions or decisions are made
📌 This is where AI learns.
Example: AI model predicts whether crops are healthy or diseased.
🔸 Stage 5: Evaluation
In the final stage:
AI solution is tested
Accuracy is checked
Improvements are suggested
📌 If results are unsatisfactory:
The process is repeated
Model is improved
4️⃣ SECTOR-BASED AI PROJECTS
AI is applied in many sectors. In this unit, we focus on four important sectors.
🌾 AGRICULTURE
🔹 AI Use Case in Agriculture
Crop disease prediction
Soil quality analysis
Weather forecasting
🔹 Mapping with AI Project Cycle
Problem: Crop loss
Data: Crop images, weather data
Exploration: Disease patterns
Model: Disease prediction
Evaluation: Accuracy of prediction
📌 Benefit: Higher crop yield and reduced losses.
🍞 FOOD PROCESSING
🔹 AI Use Case in Food Processing
Quality checking of food
Sorting defective items
Monitoring food safety
🔹 Mapping with AI Project Cycle
Problem: Poor food quality
Data: Images of food items
Exploration: Defect identification
Model: Quality classification
Evaluation: Error rate
📌 Benefit: Improved food quality and safety.
🥛 DAIRY FARMING
🔹 AI Use Case in Dairy Farming
Milk quality monitoring
Health tracking of animals
Automated feeding systems
🔹 Mapping with AI Project Cycle
Problem: Low milk production
Data: Animal health records
Exploration: Health patterns
Model: Production prediction
Evaluation: Output improvement
📌 Benefit: Better dairy management and productivity.