Table of Contents
Chapter 1: Artificial Intelligence – Basics and Project Cycle
1. Multiple Choice Questions (MCQs)
1. What is Artificial Intelligence (AI)?
A. Ability of humans to use computers
B. Ability of machines to behave intelligently
C. Programming languages used in computers
D. Internet-based technology
✅ Correct Answer: B
2. Which of the following best defines AI in simple words?
A. Machines that look like humans
B. Robots that can walk and talk
C. Ability of a machine to think and learn like humans
D. Computers that store large data
✅ Correct Answer: C
3. Which of the following is NOT a capability of an AI system?
A. Learning from data
B. Identifying patterns
C. Making decisions
D. Feeling emotions
✅ Correct Answer: D
4. AI does NOT always mean robots because:
A. Robots do not use AI
B. AI systems work silently in apps and devices
C. Robots are outdated
D. AI is only theoretical
✅ Correct Answer: B
5. Why is AI considered a problem-solving technology?
A. It replaces humans
B. It solves only mathematical problems
C. It analyzes complex data to find solutions
D. It works without data
✅ Correct Answer: C
6. Which task is difficult for humans but easy for AI?
A. Writing a story
B. Reading a book
C. Analyzing millions of searches in seconds
D. Drawing pictures
✅ Correct Answer: C
7. Which of the following is an example of AI in daily life?
A. Calculator
B. Electric bulb
C. YouTube video recommendations
D. Fan
✅ Correct Answer: C
8. Face unlock in smartphones is an example of:
A. Manual programming
B. Hardware technology
C. Artificial Intelligence
D. Internet service
✅ Correct Answer: C
9. What is an AI Project Cycle?
A. A circular computer program
B. A step-by-step process to build AI solutions
C. A type of machine
D. A software update
✅ Correct Answer: B
10. Why is following the AI Project Cycle important?
A. To reduce computer usage
B. To avoid confusion and improve results
C. To make robots
D. To reduce electricity
✅ Correct Answer: B
11. Skipping steps in the AI Project Cycle may lead to:
A. Faster results
B. Perfect AI
C. Biased or wrong AI solutions
D. Better accuracy
✅ Correct Answer: C
12. How many main stages are there in the AI Project Cycle?
A. Three
B. Four
C. Five
D. Six
✅ Correct Answer: C
13. Which is the first stage of the AI Project Cycle?
A. Data Acquisition
B. Data Exploration
C. Problem Scoping
D. Evaluation
✅ Correct Answer: C
14. Which stage focuses on collecting data?
A. Problem Scoping
B. Data Acquisition
C. Modelling
D. Evaluation
✅ Correct Answer: B
15. Data is called the “fuel of AI” because:
A. AI works without computers
B. AI learns from data
C. Data replaces machines
D. Data is always free
✅ Correct Answer: B
16. In which stage does the machine actually learn patterns?
A. Problem Scoping
B. Data Exploration
C. Modelling
D. Evaluation
✅ Correct Answer: C
17. What is done during the Evaluation stage?
A. Data collection
B. Training the model
C. Testing accuracy and performance
D. Defining the problem
✅ Correct Answer: C
18. The 4Ws Framework is used in which stage?
A. Data Acquisition
B. Modelling
C. Problem Scoping
D. Evaluation
✅ Correct Answer: C
19. Which of the following is NOT part of the 4Ws Framework?
A. Who
B. What
C. When
D. Why
✅ Correct Answer: C
20. Sustainable Development Goals (SDGs) are set by:
A. Government of India
B. World Health Organization
C. United Nations
D. UNESCO
✅ Correct Answer: C
2. Assertion–Reason Questions
Directions:
Each question has two statements: Assertion (A) and Reason (R).
Choose the correct option:
A. Both A and R are true, and R is the correct explanation of A
B. Both A and R are true, but R is NOT the correct explanation of A
C. A is true, but R is false
D. A is false, but R is true
1.
Assertion (A): Artificial Intelligence enables machines to learn from data and improve their performance over time.
Reason (R): AI systems can identify patterns and make decisions using data.
✅ Correct Answer: A
2.
Assertion (A): Artificial Intelligence always refers to robots that look and act like humans.
Reason (R): Many AI systems work silently inside mobile phones, computers, and applications.
✅ Correct Answer: D
3.
Assertion (A): AI is considered a problem-solving technology.
Reason (R): AI helps in studying problems, analyzing large amounts of data, and finding solutions.
✅ Correct Answer: A
4.
Assertion (A): AI Project Cycle follows a step-by-step process.
Reason (R): Skipping stages in the AI Project Cycle may lead to biased or incorrect AI solutions.
✅ Correct Answer: B
5.
Assertion (A): Problem Scoping is the most important stage of the AI Project Cycle.
Reason (R): A clearly defined problem ensures that the AI solution is accurate and effective.
✅ Correct Answer: A
6.
Assertion (A): Data Acquisition is essential in an AI project.
Reason (R): Without data, an AI system cannot learn or make predictions.
✅ Correct Answer: A
7.
Assertion (A): Data Exploration helps in understanding patterns and cleaning data.
Reason (R): Raw data may contain errors or irrelevant information.
✅ Correct Answer: A
8.
Assertion (A): The Modelling stage is where actual intelligence is developed in an AI system.
Reason (R): In this stage, the AI model is trained using data to learn patterns.
✅ Correct Answer: A
9.
Assertion (A): The 4Ws Framework helps in defining a real-world problem clearly.
Reason (R): It focuses on Who is affected, What the problem is, Where it occurs, and Why it matters.
✅ Correct Answer: A
10.
Assertion (A): Artificial Intelligence can help achieve Sustainable Development Goals (SDGs).
Reason (R): AI can predict crop diseases, improve healthcare diagnosis, and monitor climate change.
✅ Correct Answer: A
3. Case-Based Question
Case-Based Question 1: Smart Crop Disease Prediction
Farmers in a village are facing repeated crop losses due to sudden pest attacks and plant diseases. Manually checking crops is time-consuming and often inaccurate. An AI-based system is proposed that collects images of crops, analyzes weather data, and predicts possible diseases in advance so that farmers can take timely action.
Questions:
1.1 What is the real-world problem identified in this case?
A. Lack of internet facilities
B. Crop disease and low yield
C. High cost of fertilizers
D. Poor transportation
✅ Correct Answer: B
1.2 Which stage of the AI Project Cycle focuses on clearly identifying this problem?
A. Data Acquisition
B. Data Exploration
C. Problem Scoping
D. Evaluation
✅ Correct Answer: C
1.3 What type of data will be MOST useful for this AI system?
A. Only text data
B. Crop images and weather data
C. Audio recordings
D. Handwritten notes
✅ Correct Answer: B
1.4 Which Sustainable Development Goal (SDG) is mainly supported by this AI solution?
A. Quality Education
B. Climate Action
C. Zero Hunger
D. Clean Water
✅ Correct Answer: C
Case-Based Question 2: AI-Based Online Learning Platform
An online learning platform uses AI to recommend videos and study materials based on students’ learning history and performance. The system improves suggestions over time by analyzing student behavior and feedback.
Questions:
2.1 Which AI feature allows the system to improve recommendations over time?
A. Manual programming
B. Learning from data
C. Hardware upgrade
D. Internet speed
✅ Correct Answer: B
2.2 Which stage of the AI Project Cycle involves studying students’ learning patterns?
A. Data Acquisition
B. Data Exploration
C. Problem Scoping
D. Evaluation
✅ Correct Answer: B
2.3 Which daily-life AI example is similar to this system?
A. Calculator
B. Face unlock
C. YouTube recommendations
D. Spam calls
✅ Correct Answer: C
2.4 Which SDG is directly supported by this AI application?
A. Zero Hunger
B. Good Health
C. Quality Education
D. Climate Action
✅ Correct Answer: C
Case-Based Question 3: AI-Based Traffic Management System
A city faces heavy traffic congestion during peak hours. An AI-based traffic management system is planned to collect real-time data from cameras and sensors, analyze traffic patterns, and automatically adjust traffic signals to reduce congestion.
Questions:
3.1 Why is AI suitable for solving this problem?
A. It replaces traffic police
B. It can analyze large data quickly
C. It works without data
D. It avoids decision-making
✅ Correct Answer: B
3.2 Which stage of the AI Project Cycle involves collecting data from cameras and sensors?
A. Problem Scoping
B. Data Acquisition
C. Modelling
D. Evaluation
✅ Correct Answer: B
3.3 In which stage does the AI learn traffic patterns?
A. Data Exploration
B. Modelling
C. Problem Scoping
D. Evaluation
✅ Correct Answer: B
3.4 Which question of the 4Ws Framework is MOST important to decide the location of implementation?
A. Who
B. What
C. Where
D. Why
✅ Correct Answer: C
4. Short Answer Questions
1. What is Artificial Intelligence?
Artificial Intelligence (AI) is a branch of computer science that enables machines to think, learn from data, and make decisions like humans.
2. Why is AI called a problem-solving technology?
AI is called a problem-solving technology because it analyzes large amounts of data, identifies patterns, and provides accurate solutions to complex real-world problems.
3. Mention any two capabilities of an AI system.
Two capabilities of an AI system are:
- Learning from data
- Identifying patterns and making decisions
4. Why do AI projects follow a step-by-step cycle?
AI projects follow a step-by-step cycle to clearly understand the problem, use data properly, avoid mistakes, and improve the accuracy of the final solution.
5. What is meant by the AI Project Cycle?
The AI Project Cycle is a structured process consisting of steps followed to develop an AI-based solution for a real-world problem.
6. Name the five stages of the AI Project Cycle.
The five stages are:
- Problem Scoping
- Data Acquisition
- Data Exploration
- Modelling
- Evaluation
7. Why is data called the “fuel of AI”?
Data is called the fuel of AI because AI systems learn, train, and make predictions only using data.
8. What is the purpose of the 4Ws Framework?
The 4Ws Framework helps in clearly defining a problem by identifying who is affected, what the problem is, where it occurs, and why it is important.
9. How does the Modelling stage contribute to an AI project?
In the Modelling stage, AI models are created and trained using data so that the machine can learn patterns and develop intelligence.
10. How can AI help in achieving Sustainable Development Goals (SDGs)?
AI helps achieve SDGs by predicting crop diseases, improving healthcare diagnosis, monitoring climate change, and managing resources efficiently.
5. Long Answer Questions (5 Marks Each)
1. Explain Artificial Intelligence and its role as a problem-solving technology. Give examples from daily life.
Answer guidelines:
- Meaning of Artificial Intelligence
- AI as the ability of machines to learn, think, and decide
- Why AI is used to solve complex problems
- Examples: search engines, voice assistants, recommendation systems, smartphones
2. What is the AI Project Cycle? Explain why it is important to follow a step-by-step cycle in AI projects.
Answer guidelines:
- Definition of AI Project Cycle
- Comparison with scientific experiments or recipes
- Importance of structured steps
- Problems caused by skipping stages (bias, errors, failure)
3. Describe all the five stages of the AI Project Cycle in detail.
Answer guidelines:
- Problem Scoping – identifying and understanding the problem
- Data Acquisition – collecting data from various sources
- Data Exploration – analyzing and cleaning data
- Modelling – training AI models and learning patterns
- Evaluation – testing accuracy and improving performance
4. Explain the 4Ws Framework used in Problem Scoping with suitable examples.
Answer guidelines:
- Purpose of the 4Ws Framework
- Explanation of Who, What, Where, and Why
- Importance of each W
- Example of a real-life problem
5. What are Sustainable Development Goals (SDGs)? Explain the role of Artificial Intelligence in achieving SDGs.
Answer guidelines:
- Meaning and purpose of SDGs
- Examples of major SDGs
- How AI supports sustainable development
- AI applications in agriculture, healthcare, climate monitoring
Chapter 2: Data and Problem Scoping
1. MCQs
1. Why is problem scoping the first stage of the AI project cycle?
A. It trains the AI model
B. It defines what problem AI should solve
C. It tests the AI system
D. It collects results
Ans: B
2. An AI system is built to help farmers, but it only predicts weather and ignores pest attacks. What stage was poorly done?
A. Modelling
B. Evaluation
C. Data Exploration
D. Problem Scoping
Ans: D
3. Which of the following best explains problem scoping?
A. Writing computer programs
B. Understanding the real needs of people
C. Collecting large data
D. Testing AI results
Ans: B
4. If an AI project gives correct answers but solves the wrong problem, what was missing?
A. Enough data
B. Good hardware
C. Clear problem scoping
D. Fast computer
Ans: C
5. Why is correct data important in AI systems?
A. To increase storage
B. To reduce internet usage
C. To avoid wrong predictions
D. To speed up computers
Ans: C
6. Which situation shows insufficient data?
A. Using many unrelated datasets
B. Using only a few examples to train AI
C. Using accurate data
D. Using government data
Ans: B
7. What is the main purpose of data exploration?
A. To collect new data
B. To study and improve the quality of data
C. To design AI rules
D. To replace missing data automatically
Ans: B
8. What problem can occur if missing values in data are not found?
A. Better results
B. Faster learning
C. Incorrect AI output
D. Less memory usage
Ans: C
9. Why should unwanted or irrelevant data be removed before modelling?
A. It increases data size
B. It confuses the AI system
C. It makes AI slower
D. It changes hardware
Ans: B
10. Which example shows text data used in AI?
A. Weather sensor reading temperature
B. Satellite photograph
C. Customer reviews on a website
D. Voice recording
Ans: C
11. Which type of data is most suitable for speech recognition systems?
A. Image data
B. Text data
C. Audio data
D. Numerical data
Ans: C
12. Student marks stored in a spreadsheet are best described as:
A. Image data
B. Text data
C. Audio data
D. Numerical data
Ans: D
13. Which data source is most reliable for population or weather information?
A. Social media posts
B. Government portals
C. Personal opinions
D. Advertisements
Ans: B
14. Why is data cleaning an important part of data exploration?
A. It adds new data
B. It removes errors and duplicates
C. It trains AI models
D. It increases bias
Ans: B
15. If a dataset mostly represents one group of people, what problem may occur?
A. Better accuracy
B. Data bias
C. Faster results
D. Clean data
Ans: B
16. Which situation is an example of biased data?
A. Data collected from different age groups
B. Data collected from only one city
C. Cleaned data
D. Large dataset
Ans: B
17. Which statement correctly describes rule-based AI?
A. Learns automatically from data
B. Works only using predefined rules
C. Improves with experience
D. Uses patterns
Ans: B
18. Which feature makes learning-based AI different from rule-based AI?
A. Uses fixed rules
B. Cannot improve
C. Learns from data and patterns
D. Works without data
Ans: C
19. Which AI system is best suited for spam email detection?
A. Rule-based AI
B. Learning-based AI
C. Calculator
D. Timer system
Ans: B
20. Which statement best compares rule-based and learning-based AI?
A. Both improve automatically
B. Rule-based AI learns faster
C. Learning-based AI can handle complex problems
D. Rule-based AI uses more data
Ans: C
2. Assertion–Reason Questions
Directions:
Each question has two statements: Assertion (A) and Reason (R).
Choose the correct option:
A. Both A and R are true, and R is the correct explanation of A
B. Both A and R are true, but R is NOT the correct explanation of A
C. A is true, but R is false
D. A is false, but R is true
1️⃣
Assertion (A): Problem scoping helps in giving direction to an AI project.
Reason (R): It clearly defines the problem and goals to be solved by AI.
Ans: A
2️⃣
Assertion (A): An AI project may fail even if good technology is used.
Reason (R): The problem might not be properly understood at the beginning.
Ans: A
3️⃣
Assertion (A): AI systems cannot work properly without data.
Reason (R): AI systems learn and make decisions based on data.
Ans: A
4️⃣
Assertion (A): Using incorrect or outdated data can lead to wrong AI results.
Reason (R): AI treats all input data as correct while learning.
Ans: A
5️⃣
Assertion (A): Data exploration should be done before building AI models.
Reason (R): It helps identify missing values, errors, and unwanted data.
Ans: A
6️⃣
Assertion (A): Data cleaning improves the accuracy of AI systems.
Reason (R): Clean data reduces errors and confusion during learning.
Ans: A
7️⃣
Assertion (A): Collecting large amounts of data always improves AI performance.
Reason (R): Irrelevant data can confuse the AI system.
Ans: D
8️⃣
Assertion (A): Rule-based AI systems can improve themselves over time.
Reason (R): They follow fixed “If–Then” rules given by humans.
Ans: D
9️⃣
Assertion (A): Learning-based AI is suitable for complex tasks like spam detection.
Reason (R): It learns patterns from data and improves with experience.
Ans: A
🔟
Assertion (A): Identifying bias in data is important for building fair AI systems.
Reason (R): Biased data may favor one group over others.
Ans: A
3. Case-Study Based Questions with Answers
Case Study 1: Smart Farming Assistant
A school team designs an AI system to help farmers.
They collect only weather data and build a system that predicts rainfall.
However, farmers say crop loss continues due to pests and poor soil quality.
Questions & Answers:
1. Which stage of the AI Project Cycle was not done properly?
✅ Answer: Problem Scoping
2. Why did the AI system fail to solve the farmers’ real problem?
✅ Answer: Because the real causes of crop loss (pests and soil health) were not identified during problem scoping.
3. Name one type of data that should have been collected to improve the AI solution.
✅ Answer: Pest attack data / Soil health data (any one)
Case Study 2: Student Performance Predictor
An AI system is created to predict exam performance of students.
The data used includes marks of only high-performing students from one school.
When used for other students, the predictions are incorrect.
Questions & Answers:
1. What is the main problem with the data used in this AI system?
✅ Answer: The data is biased and not representative of all students.
2. Which data exploration step could have helped find this problem?
✅ Answer: Identifying bias in the data.
3. What type of data should be added to improve the AI system?
✅ Answer: Marks of average and low-performing students from different schools.
Case Study 3: Voice-Based Attendance System
A school builds an AI system to mark attendance using students’ voices.
The voice samples used for training were recorded in noisy places.
The system fails to recognize many students during actual use.
Questions & Answers:
1. Which type of data is mainly used in this system?
✅ Answer: Audio data
2. What data quality issue caused poor performance?
✅ Answer: Noisy and unclear training data.
3. Mention one data improvement step to increase accuracy.
✅ Answer:
- Record clear voice samples in a quiet environment
OR - Clean the audio data to remove noise
4. Short Answer Questions
1. What is problem scoping in Artificial Intelligence?
✅ Answer: Problem scoping means clearly defining the problem, understanding people’s needs, and setting goals for an AI solution.
2. Why is problem scoping important in an AI project?
✅ Answer: It helps solve the correct problem, gives direction to the project, and prevents failure of the AI system.
3. What can happen if problem scoping is ignored?
✅ Answer: The AI system may solve the wrong problem and give useless or incorrect results.
4. Why is data important for AI systems?
✅ Answer: AI systems learn, make decisions, and improve using data.
5. What is meant by correct data?
✅ Answer: Correct data is accurate, relevant to the problem, and up to date.
6. What is data exploration?
✅ Answer: Data exploration is the process of studying and understanding collected data to find errors, patterns, and missing values.
7. Why is data cleaning necessary before building AI models?
✅ Answer: Data cleaning removes errors and duplicate values, which improves the accuracy of AI models.
8. What is data bias?
✅ Answer: Data bias occurs when data favors one group over others, leading to unfair AI results.
9. Write one difference between rule-based AI and learning-based AI.
✅ Answer: Rule-based AI works on fixed rules, while learning-based AI learns from data and improves over time.
10. Give one example of a learning-based AI system.
✅ Answer: Spam email detection system.
5. Long Answer Questions
1. Explain the importance of Problem Scoping in an AI project.
Answer:
Problem scoping is the first and most important stage of the AI project cycle. It involves clearly defining the problem, understanding the needs of people affected, and setting clear goals for the AI solution. Proper problem scoping gives direction to the entire project and ensures that the AI system solves the correct problem. If problem scoping is ignored, the AI may solve a different or less important problem, which can lead to failure of the project. Therefore, correct problem scoping saves time, effort, and resources.
2. Why are correct and sufficient data important for building AI systems? Explain.
Answer:
Correct data is important because AI systems learn from the data provided to them. If the data is inaccurate, outdated, or irrelevant, the AI will produce wrong predictions and decisions. Sufficient data is also necessary so that the AI system can learn properly and understand different situations. Too little data does not allow AI to learn well, while too much irrelevant data can confuse the system. Hence, AI systems require both correct and sufficient data to work accurately and fairly.
3. What is data exploration? Explain its role before building AI models.
Answer:
Data exploration is the process of studying and understanding the collected data before building AI models. It includes checking the structure of data, finding missing values, identifying errors, and recognizing patterns or trends. Data exploration helps in removing unwanted data and detecting bias. If data exploration is skipped, the AI model may perform poorly or give biased results. Therefore, data exploration improves the quality and accuracy of AI systems.
4. Explain different types of data used in AI with examples.
Answer:
AI systems use different types of data to learn and make decisions.
- Text data includes emails, messages, and reviews and is used in chatbots and translation apps.
- Image data includes photographs and medical images and is used in face recognition and medical diagnosis.
- Audio data includes voice recordings and music and is used in voice assistants and speech recognition.
- Numerical data includes marks, temperature readings, and sales figures and is used in weather prediction and performance analysis.
Each type of data helps AI systems perform specific tasks.
5. Differentiate between Rule-Based AI and Learning-Based AI.
Answer:
Rule-Based AI works on predefined rules created by humans using “If–Then” conditions. It does not learn from data and cannot improve on its own. It is suitable for simple and fixed tasks.
Learning-Based AI learns from data, identifies patterns, and improves its performance over time. It can handle complex problems such as spam detection and recommendation systems.
Thus, learning-based AI is more flexible and intelligent compared to rule-based AI.
Chapter 3 – Ethics in Artificial Intelligence
1. Multiple Choice Questions (MCQs)
1. Ethics mainly help human beings to decide what is:
A. Legal and illegal
B. Right and wrong
C. Easy and difficult
D. Profitable and loss-making
Ans: B
2. Ethics are best described as:
A. Government-made rules
B. Court judgments
C. Moral principles guiding behavior
D. Technical instructions
Ans: C
3. Which of the following is NOT a role of ethics in society?
A. Promoting fairness
B. Preventing harm
C. Encouraging misuse of power
D. Guiding responsible decisions
Ans: C
4. Ethics are different from laws because ethics are:
A. Written and compulsory
B. Enforced by police
C. Moral rules followed voluntarily
D. Limited to courts
Ans: C
5. AI Ethics mainly deals with:
A. Speed of AI systems
B. Cost of AI tools
C. Moral use of AI systems
D. Hardware used in AI
Ans: C
6. AI Ethics ensures that AI systems are:
A. Faster and cheaper
B. Safe, fair, and respectful
C. Fully independent
D. Used only by experts
Ans: B
7. Who is responsible for the actions taken by an AI system?
A. The AI itself
B. The computer
C. The data only
D. Humans who design and use it
Ans: D
8. Which one is a key difference between human ethics and AI ethics?
A. Humans follow data
B. AI uses emotions
C. Humans take responsibility
D. AI decides morals itself
Ans: C
9. AI does not think on its own because it works based on:
A. Emotions
B. Laws
C. Programming and data
D. Instinct
Ans: C
10. Bias in AI refers to:
A. High accuracy
B. Neutral decisions
C. Unfair preference for one group
D. Fast processing
Ans: C
11. One major reason for bias in AI systems is:
A. Excessive electricity use
B. Incomplete or unbalanced data
C. Strong algorithms
D. Regular updates
Ans: B
12. If an AI system works well only for one region, it shows:
A. Fairness
B. Inclusiveness
C. Regional bias
D. Accountability
Ans: C
13. Collecting personal data without permission violates:
A. Transparency
B. Privacy
C. Accuracy
D. Speed
Ans: B
14. Which of the following is an ethical concern related to data?
A. Data misuse
B. Data storage size
C. Data color
D. Data speed
Ans: A
15. Fair and inclusive AI should:
A. Work only for adults
B. Favor one community
C. Treat everyone equally
D. Ignore minorities
Ans: C
16. Accountability in AI means:
A. Blaming machines
B. Ignoring mistakes
C. Taking responsibility for AI decisions
D. Letting AI fix itself
Ans: C
17. If an AI system makes a wrong decision, who should fix it?
A. The user only
B. The machine itself
C. Humans responsible for the AI
D. No one
Ans: C
18. Responsible use of AI by students includes:
A. Misusing AI tools
B. Copying answers blindly
C. Respecting privacy and rules
D. Sharing personal data
Ans: C
19. Which is a possible impact of unethical AI on society?
A. Increased fairness
B. Better privacy
C. Spread of false information
D. Equal opportunities
Ans: C
20. Classroom debates and case discussions help students to develop:
A. Memorization skills
B. Ethical thinking and reasoning
C. Coding speed
D. Technical errors
Ans: B
2. Assertion–Reason Questions
(Choose the correct option)
A. Both Assertion (A) and Reason (R) are true and R is the correct explanation of A
B. Both A and R are true but R is NOT the correct explanation of A
C. A is true but R is false
D. A is false but R is true
1.
Assertion (A): Ethics help individuals decide what is right and wrong.
Reason (R): Ethics are moral principles that guide human behavior.
Ans: A
2.
Assertion (A): Ethics are important for maintaining peace in society.
Reason (R): Ethics encourage fairness, respect, and responsible decision-making.
Ans: A
3.
Assertion (A): Artificial Intelligence systems must follow ethical guidelines.
Reason (R): AI systems can impact areas like education, healthcare, and employment.
Ans: A
4.
Assertion (A): AI can take moral responsibility for its decisions.
Reason (R): AI systems work only based on data and programming given by humans.
Ans: D
5.
Assertion (A): Bias in AI systems can lead to unfair treatment of certain groups.
Reason (R): AI systems may be trained on incomplete or unbalanced data.
Ans: A
6.
Assertion (A): Using personal data without permission is considered ethical.
Reason (R): Data privacy is an important concern in AI ethics.
Ans: D
7.
Assertion (A): Fair and inclusive AI should work equally for all sections of society.
Reason (R): Ethical AI avoids discrimination based on gender, age, or community.
Ans: A
8.
Assertion (A): Humans must be accountable for the actions of AI systems.
Reason (R): AI systems do not have emotions or independent thinking abilities.
Ans: A
9.
Assertion (A): Responsible use of AI by students includes respecting privacy.
Reason (R): Misuse of AI tools can harm individuals and society.
Ans: A
10.
Assertion (A): Unethical use of AI can increase inequality in society.
Reason (R): AI systems, when misused, may favor certain groups unfairly.
Ans: A
3. Case Study–Based Questions (with Answers)
Case Study 1: Biased Recruitment AI
A company uses an AI system to shortlist candidates for jobs. The system is trained using data from previous employees, most of whom belong to one region and one gender. After some time, it is noticed that the AI mostly selects candidates from the same background and rejects others even if they are qualified.
Questions:
a) Which ethical issue is shown in this case?
Ans: Bias in AI systems.
b) Why did this problem occur?
Ans: The AI was trained on incomplete and unbalanced data representing only one group.
c) What should be done to make the AI system ethical?
Ans: The training data should include diverse groups and humans should regularly monitor and correct the AI system.
Case Study 2: Student Data Privacy
An online learning platform uses AI to track students’ performance. It collects personal information such as names, exam scores, and learning habits. The platform shares this data with third parties without informing students or parents.
Questions:
a) Which AI ethics principle is being violated?
Ans: Privacy and data security.
b) Why is sharing data without permission unethical?
Ans: It misuses personal data and violates an individual’s right to privacy.
c) What ethical step should the platform take?
Ans: The platform should take consent, protect data, and use it only for educational purposes.
Case Study 3: AI in Social Media
A social media platform uses AI to recommend news and posts. The AI promotes sensational and false content because it gets more clicks. As a result, many users start believing incorrect information.
Questions:
a) What negative impact of unethical AI is shown here?
Ans: Spread of false information.
b) Why is this considered unethical AI use?
Ans: It harms society by misleading people instead of providing accurate and fair information.
c) Who should be held responsible for this problem?
Ans: Humans and organizations that design and control the AI system should be accountable.
4. Short Answer Questions (with Answers)
1. What are ethics?
Ans: Ethics are moral values and principles that guide people to decide what is right and wrong.
2. Why are ethics important in human decisions?
Ans: Ethics help people make responsible decisions, prevent harm, and promote fairness and honesty.
3. What is meant by AI ethics?
Ans: AI ethics refers to the moral rules and principles that guide the design, development, and use of AI systems.
4. Why does AI need ethical guidelines?
Ans: AI needs ethical guidelines because it affects areas like education, healthcare, employment, and society.
5. What is bias in AI systems?
Ans: Bias in AI means unfair preference or discrimination against certain groups due to incomplete or unbalanced data.
6. Mention one cause of AI bias.
Ans: AI bias can occur when training data represents only one group or contains human bias.
7. What is data privacy in AI?
Ans: Data privacy means protecting personal information from misuse, theft, or unauthorized access.
8. What does fairness in AI mean?
Ans: Fairness in AI means treating all people equally and avoiding discrimination.
9. What is accountability in AI?
Ans: Accountability in AI means humans taking responsibility for the decisions and actions of AI systems.
10. Give one example of responsible use of AI by students.
Ans: Students should use AI tools ethically and respect others’ privacy while using them for learning.
5. Long Answer Questions (with Answers)
1. Explain the meaning of ethics and discuss why ethics are important in human life.
Answer:
Ethics are moral values and principles that help individuals decide what is right and what is wrong. They guide human behavior and encourage people to act responsibly, honestly, and fairly. Ethics are not written laws but are followed by individuals and society to maintain harmony.
Ethics are important in human life because they guide our actions and decisions, prevent harm to others, and promote justice and respect. Without ethics, society would face disorder, misuse of power, and conflict. Ethical behavior helps people live peacefully and build trust in society.
2. What is AI ethics? Explain how AI ethics is different from human ethics.
Answer:
AI ethics refers to the moral principles and guidelines that govern the design, development, and use of Artificial Intelligence systems. It ensures that AI systems are safe, fair, reliable, and do not harm individuals or society.
AI ethics is different from human ethics in many ways. Human ethics are based on emotions, values, and personal judgment, while AI ethics depends on data, algorithms, and programming. Humans can think flexibly and take responsibility for their actions, whereas AI systems only follow instructions given by humans. Therefore, humans are responsible for ensuring ethical behavior in AI systems.
3. Why do AI ethics matter? Explain any three ethical concerns related to AI.
Answer:
AI ethics matter because AI systems are increasingly used in important areas such as education, healthcare, employment, security, and social media. Ethical AI ensures that technology benefits society and does not cause harm.
Three major ethical concerns related to AI are:
- Bias in AI: AI systems can show bias if trained on incomplete or unbalanced data, leading to unfair treatment of certain groups.
- Privacy and Data Security: AI uses large amounts of personal data, which can be misused or stolen if not protected properly.
- Accountability: When AI makes mistakes, it is important to know who is responsible and who will correct the error.
4. Explain the concept of responsible use of AI. How can students use AI responsibly?
Answer:
Responsible use of AI means using AI tools in a safe, ethical, and positive manner while following rules and guidelines. It involves avoiding misuse, respecting privacy, and ensuring that AI is used for the benefit of society.
Students can use AI responsibly by not copying answers blindly, respecting others’ personal data, and using AI as a learning aid rather than a shortcut. They should also avoid spreading false information generated by AI and follow school rules while using AI tools.
5. Discuss the impact of unethical AI on society.
Answer:
Unethical use of AI can have serious negative effects on society. It can spread false or misleading information, which can influence people’s opinions wrongly. Biased AI systems can increase inequality by favoring certain groups over others.
Unethical AI can also violate privacy by misusing personal data and may cause unfair job losses if automation is not handled responsibly. Therefore, following ethical principles in AI is necessary to protect society and ensure that technology is used for the common good.
Chapter 4 – Domains of Artificial Intelligence
1. Multiple Choice Questions (MCQs)
1. Artificial Intelligence is divided into domains mainly based on:
A. Speed of machines
B. Type of data and problem solved
C. Cost of technology
D. Size of computer
Ans: B
2. Understanding AI domains helps in:
A. Writing computer programs faster
B. Choosing the correct AI approach
C. Increasing electricity usage
D. Replacing humans completely
Ans: B
3. Which of the following is NOT a main AI domain at this level?
A. Data Domain
B. Computer Vision
C. Natural Language Processing
D. Robotics Domain
Ans: D
4. The Data Domain mainly deals with:
A. Images and videos
B. Text and speech
C. Numbers and structured data
D. Sounds only
Ans: C
5. Which type of data belongs to the Data Domain?
A. Photographs
B. Voice recordings
C. Student marks
D. Videos
Ans: C
6. Predicting market trends using sales records belongs to which domain?
A. NLP
B. Computer Vision
C. Data Domain
D. Robotics
Ans: C
7. If a problem mainly involves statistics and numerical analysis, it belongs to:
A. NLP Domain
B. Data Domain
C. Computer Vision Domain
D. Mixed Domain
Ans: B
8. Computer Vision enables machines to:
A. Understand numbers
B. Translate languages
C. See and interpret images
D. Store data
Ans: C
9. Which type of data is used in Computer Vision?
A. Text documents
B. Numerical tables
C. Images and videos
D. Speech commands
Ans: C
10. Face recognition technology is an application of:
A. Data Domain
B. NLP Domain
C. Computer Vision Domain
D. Mixed Domain
Ans: C
11. Traffic monitoring using CCTV cameras mainly uses:
A. NLP
B. Data Domain only
C. Computer Vision
D. Robotics
Ans: C
12. Natural Language Processing (NLP) helps machines to:
A. See objects
B. Understand human language
C. Predict numbers
D. Control traffic lights
Ans: B
13. Which of the following data types is used in NLP?
A. Images
B. Videos
C. Text and speech
D. Graphs
Ans: C
14. Chatbots on websites mainly use which AI domain?
A. Data Domain
B. Computer Vision
C. NLP
D. Robotics
Ans: C
15. Language translation applications are examples of:
A. Computer Vision
B. Data Domain
C. NLP Domain
D. Hardware systems
Ans: C
16. Mapping AI solutions to domains means:
A. Writing code
B. Identifying problems and matching domains
C. Buying AI tools
D. Storing data
Ans: B
17. Which question helps identify the Data Domain?
A. Does the problem involve images?
B. Does the problem involve speech?
C. Does the problem involve numbers?
D. Does the problem involve cameras?
Ans: C
18. A voice assistant uses NLP mainly to:
A. Store images
B. Understand voice commands
C. Detect faces
D. Analyze charts
Ans: B
19. Reading handwritten text belongs to which domain?
A. Data Domain only
B. NLP only
C. Computer Vision + NLP
D. Robotics
Ans: C
20. Traffic signal monitoring uses which combination of domains?
A. NLP only
B. Data Domain only
C. Computer Vision + Data Domain
D. NLP + Robotics
Ans: C
2. Assertion–Reason Questions
Choose the correct option:
A. Both Assertion (A) and Reason (R) are true and R is the correct explanation of A
B. Both A and R are true but R is NOT the correct explanation of A
C. A is true but R is false
D. A is false but R is true
1.
Assertion (A): Artificial Intelligence is divided into domains to solve different types of problems effectively.
Reason (R): Different AI domains work on different types of data and problem requirements.
Ans: A
2.
Assertion (A): The Data Domain is used when a problem mainly involves numerical data.
Reason (R): The Data Domain focuses on analyzing numbers, patterns, and trends.
Ans: A
3.
Assertion (A): Weather prediction applications belong to the Data Domain.
Reason (R): Weather prediction is based on analyzing numerical and statistical data.
Ans: A
4.
Assertion (A): Computer Vision allows machines to understand images and videos.
Reason (R): Computer Vision uses cameras, image data, and algorithms to interpret visual information.
Ans: A
5.
Assertion (A): Face recognition in mobile phones is an example of the NLP domain.
Reason (R): Face recognition works on images of human faces.
Ans: D
6.
Assertion (A): Natural Language Processing helps machines interact with humans naturally.
Reason (R): NLP enables machines to understand and respond to text and speech.
Ans: A
7.
Assertion (A): Chatbots mainly work using the Data Domain.
Reason (R): Chatbots analyze text messages and voice commands.
Ans: D
8.
Assertion (A): Mapping AI problems to the correct domain improves accuracy and efficiency.
Reason (R): Correct domain selection ensures proper data usage and suitable AI models.
Ans: A
9.
Assertion (A): Some AI applications may use more than one AI domain.
Reason (R): Real-world problems often involve different types of data like text, images, and numbers.
Ans: A
10.
Assertion (A): Reading handwritten text uses only the Data Domain.
Reason (R): It requires identifying text from images and understanding the language.
Ans: D
3. Case Study–Based Questions (with Answers)
Case Study 1: Smart Weather Forecast System
A meteorological department uses an AI system to predict rainfall and temperature. The system analyzes years of weather records such as temperature readings, humidity levels, and rainfall amounts to make future predictions.
Questions:
a) Which AI domain is mainly used in this system?
Ans: Data Domain.
b) Why is this domain suitable for the given problem?
Ans: Because the system works with numerical and structured data to find patterns and make predictions.
c) Name one benefit of using AI in weather forecasting.
Ans: It improves accuracy and helps in better planning and disaster management.
Case Study 2: Face Detection at Traffic Signals
A city installs AI-enabled cameras at traffic signals to detect vehicles and identify traffic violations automatically. The cameras continuously capture images and videos to monitor traffic flow.
Questions:
a) Which AI domain is mainly involved here?
Ans: Computer Vision Domain.
b) What type of data is used by the AI system in this case?
Ans: Images and video feeds.
c) Mention one advantage of using this AI system.
Ans: It helps in efficient traffic monitoring and improves road safety.
Case Study 3: School Website Chatbot
A school introduces a chatbot on its website to answer students’ and parents’ queries related to admissions, exam dates, and fees. The chatbot understands questions typed by users and replies instantly.
Questions:
a) Which AI domain is used by the chatbot?
Ans: Natural Language Processing (NLP) Domain.
b) Why is NLP required for this application?
Ans: Because the chatbot needs to understand and respond to human language in text form.
c) Name one other application that uses the same AI domain.
Ans: Voice assistants or language translation tools.
4. Short Answer Questions (with Answers)
1. What are AI domains?
Ans: AI domains are categories of Artificial Intelligence based on the type of data used and the nature of problems solved.
2. Name the three main AI domains.
Ans: Data Domain, Computer Vision Domain, and Natural Language Processing (NLP) Domain.
3. What type of data does the Data Domain work with?
Ans: Numerical data, structured data, and patterns and trends in data.
4. Give two applications of the Data Domain.
Ans: Weather prediction and market trend analysis.
5. What is Computer Vision in AI?
Ans: Computer Vision is the AI domain that enables machines to see, understand, and interpret images and videos.
6. Name two applications of Computer Vision.
Ans: Face recognition and traffic monitoring.
7. What is Natural Language Processing (NLP)?
Ans: NLP is the AI domain that helps machines understand human language, read text, listen to speech, and respond meaningfully.
8. Give two applications of NLP.
Ans: Chatbots and voice assistants.
9. What does mapping an AI problem to the correct domain mean?
Ans: It means identifying the type of problem and matching it with the most suitable AI domain to ensure accuracy and efficiency.
10. Give an example of a real-world AI application that uses more than one domain.
Ans: Traffic signal monitoring (Computer Vision + Data Domain) or a voice assistant (NLP + Data Domain).
5. Long Answer Questions (with Answers)
1. Explain the meaning of AI domains and why it is important to classify AI into domains.
Answer:
AI domains are categories of Artificial Intelligence based on the type of data used and the nature of problems solved. Classifying AI into domains helps in choosing the correct AI approach, building accurate AI solutions, and avoiding wrong or ineffective models. By understanding AI domains, developers can match a problem with the most suitable domain, which improves efficiency, accuracy, and proper use of resources.
2. Describe the Data Domain of AI. Include the types of data used and applications.
Answer:
The Data Domain deals with numerical and structured data. AI systems in this domain analyze numbers, identify patterns and trends, and make predictions. Types of data used include student marks, weather readings, sales records, and health statistics. Applications of the Data Domain include weather prediction, disease risk analysis, performance evaluation, and market trend analysis. Problems involving mainly numbers and statistics belong to the Data Domain.
3. Explain the Computer Vision domain and give examples of its applications.
Answer:
Computer Vision is the AI domain that enables machines to see, understand, and interpret images and videos, similar to how human eyes and brain work together. AI systems in this domain use cameras, image data, and algorithms to process visual information. Types of data include images, videos, and live camera feeds. Applications of Computer Vision include face recognition, traffic monitoring, object detection, and medical image analysis. Any problem involving images or videos belongs to this domain.
4. What is Natural Language Processing (NLP)? Describe its applications.
Answer:
Natural Language Processing (NLP) is the AI domain that allows machines to understand human language. It helps machines read text, listen to speech, and respond meaningfully, enabling natural communication between humans and machines. Types of data used in NLP include text messages, emails, voice commands, and documents. Applications of NLP include chatbots, voice assistants, language translation, and sentiment analysis. Problems involving text or speech belong to the NLP domain.
5. Explain how mapping AI problems to the correct domain helps in building efficient AI solutions.
Answer:
Mapping AI problems to the correct domain means identifying the type of problem and matching it with the appropriate AI domain (Data, Computer Vision, or NLP). Correct mapping ensures better accuracy, efficient AI solutions, and proper use of resources. For example, a voice assistant uses NLP to understand voice commands and the Data Domain to process information. Some problems may involve multiple domains, such as traffic signal monitoring, which uses Computer Vision and Data Domain. Proper domain selection improves performance and effectiveness of AI systems.
Chapter 5- AI Use Cases and Project Cycle Applications
1. MCQs
1️⃣ General AI Use Case
- What is an AI use case?
A. A theoretical AI algorithm
B. A situation where AI is applied to solve a real-world problem
C. A programming language used in AI
D. A type of computer hardware
Answer: B - Which of the following is NOT a purpose of an AI use case?
A. Improve efficiency
B. Increase errors
C. Support decision-making
D. Save time
Answer: B - To understand a real-world AI use case, we must know:
A. The problem, affected people, data, AI help, and expected outcome
B. Only the data required
C. The coding language used
D. The number of programmers
Answer: A - Example of an AI use case in agriculture:
A. Soil digging
B. Predicting crop disease
C. Selling seeds
D. Using fertilizers
Answer: B
2️⃣ AI Project Cycle Stages
- What is the first stage of an AI project cycle?
A. Data Collection
B. Problem Identification
C. Model Idea
D. Evaluation
Answer: B - Which stage involves cleaning data and studying patterns?
A. Model Idea
B. Evaluation
C. Data Exploration
D. Data Collection
Answer: C - In which stage does AI learn from data to make predictions?
A. Problem Identification
B. Model Idea (Modelling)
C. Data Exploration
D. Evaluation
Answer: B - If the AI model gives unsatisfactory results, what should be done?
A. Discard the project
B. Repeat the process and improve the model
C. Ignore results
D. Collect new data without analysis
Answer: B - Data collected for AI must be:
A. Accurate, sufficient, and relevant
B. Random and incomplete
C. Only images
D. From the internet only
Answer: A
3️⃣ Sector-Based AI Projects – Agriculture
- Which of the following is an AI use case in agriculture?
A. Sorting defective items in factories
B. Predicting crop disease
C. Monitoring hospital patients
D. Tracking deliveries
Answer: B - Which data is needed for crop disease prediction?
A. Student grades
B. Crop images, weather, and soil data
C. Animal health records
D. Factory production data
Answer: B - Benefit of AI in agriculture:
A. Increase crop losses
B. Reduce human labor only
C. Higher crop yield and reduced losses
D. More expensive crops
Answer: C
4️⃣ Sector-Based AI Projects – Food Processing
- AI in food processing helps in:
A. Planting crops
B. Quality checking and sorting defective items
C. Monitoring dairy animals
D. Diagnosing diseases
Answer: B - The AI project cycle stage where food images are analyzed to identify defects is:
A. Data Collection
B. Data Exploration
C. Model Idea
D. Evaluation
Answer: B - Outcome of AI in food processing:
A. Faster disease spread
B. Improved food quality and safety
C. Reduced crop production
D. Decreased milk yield
Answer: B
5️⃣ Sector-Based AI Projects – Dairy Farming
- AI use case in dairy farming:
A. Disease diagnosis in humans
B. Milk quality monitoring and animal health tracking
C. Food quality sorting
D. Weather forecasting
Answer: B - Which stage involves studying animal health patterns?
A. Problem Identification
B. Data Exploration
C. Model Idea
D. Evaluation
Answer: B - Benefit of AI in dairy farming:
A. Poor milk quality
B. Better dairy management and productivity
C. Increased animal disease
D. Random feeding
Answer: B
6️⃣ Sector-Based AI Projects – Healthcare
- AI use case in healthcare includes:
A. Automated feeding systems
B. Medical image analysis and disease diagnosis
C. Crop disease prediction
D. Sorting defective food
Answer: B - The final stage of an AI project ensures:
A. Problem identification only
B. Accuracy and reliability of AI results
C. Random predictions
D. Only data collection
Answer: B
2. Assertion-Reason Questions – AI Use Cases & Project Cycle
Instructions:
For each question, choose:
- A: Both Assertion (A) and Reason (R) are true, and R is the correct explanation of A
- B: Both A and R are true, but R is NOT the correct explanation of A
- C: A is true, R is false
- D: A is false, R is true
1️⃣
Assertion (A): AI can predict crop disease in agriculture.
Reason (R): AI uses historical data, weather patterns, and images to detect patterns and predict outcomes.
Answer: A ✅
Explanation: AI in agriculture works on data and pattern recognition to predict crop diseases accurately.
2️⃣
Assertion (A): Data collection is the most important stage in the AI project cycle.
Reason (R): Without accurate, sufficient, and relevant data, AI models cannot learn or give reliable results.
Answer: A ✅
Explanation: Quality data ensures that the AI model can make accurate predictions.
3️⃣
Assertion (A): AI is only useful in technology companies.
Reason (R): AI can be applied in agriculture, healthcare, food processing, and other sectors.
Answer: D ✅
Explanation: The assertion is false because AI is used in multiple sectors, not just technology companies.
4️⃣
Assertion (A): In AI project evaluation, models may need to be improved if results are unsatisfactory.
Reason (R): Evaluation checks accuracy and reliability, guiding improvements in the AI system.
Answer: A ✅
Explanation: Evaluation is used to measure performance and improve the AI model if needed.
5️⃣
Assertion (A): AI can monitor animal health in dairy farming.
Reason (R): AI cannot process medical records or health patterns of animals.
Answer: C ✅
Explanation: The assertion is true, but the reason is false. AI can process health data and predict issues.
6️⃣
Assertion (A): Crop images, weather data, and soil data are collected during data collection stage.
Reason (R): AI projects in agriculture do not require exploration or modeling after data collection.
Answer: C ✅
Explanation: Assertion is true, but the reason is false because exploration and modeling are necessary after data collection.
7️⃣
Assertion (A): AI in healthcare can provide early diagnosis.
Reason (R): AI systems can analyze medical images, symptom patterns, and patient reports to detect diseases early.
Answer: A ✅
Explanation: AI assists doctors in early detection and better treatment outcomes.
8️⃣
Assertion (A): AI improves food quality in food processing industries.
Reason (R): AI can detect defects in food products and classify quality based on predefined parameters.
Answer: A ✅
Explanation: AI ensures safety and quality by detecting defective items accurately.
9️⃣
Assertion (A): The AI model stage is where predictions are made.
Reason (R): Data collection involves training the AI system to make predictions.
Answer: C ✅
Explanation: Assertion is true; reason is false. Predictions happen in the modeling stage, not data collection.
10️⃣
Assertion (A): Group projects using AI project cycle enhance teamwork and creativity.
Reason (R): AI projects can be done only individually, as teamwork slows down the learning process.
Answer: C ✅
Explanation: Assertion is true, but reason is false. Group projects foster collaboration, not hinder it.
3. Case Study-Based Questions
Case Study 1: Agriculture – Crop Disease Prediction
Scenario:
Farmers in a village are facing repeated crop losses due to a disease affecting tomato plants. A local AI team decides to help by using AI to predict disease outbreaks. They collect images of healthy and diseased crops, record weather patterns, and soil conditions over the past two years. After analyzing the data, they develop an AI model that can identify early signs of the disease and alert farmers.
Questions:
- Which stage of the AI project cycle involved collecting images, weather data, and soil data?
- How does the AI model help farmers according to the case study?
- Suggest one improvement that could make the AI system more effective.
Answers:
- Data Collection Stage ✅
- The AI model predicts early signs of disease, allowing farmers to take preventive action and reduce crop losses. ✅
- Improvement: Include real-time weather updates, satellite imagery, or sensor-based soil health data to increase prediction accuracy. ✅
Case Study 2: Healthcare – Early Disease Diagnosis
Scenario:
A hospital wants to reduce late diagnosis of pneumonia in children. They use an AI system that analyzes chest X-ray images and patient medical reports. The system identifies patterns in symptoms and detects pneumonia at an early stage. Doctors can then start treatment promptly, improving recovery rates.
Questions:
- What problem is being addressed in this AI use case?
- Which data is required for the AI system to work effectively?
- Which stage of the AI project cycle ensures the AI system’s predictions are reliable?
Answers:
- Late diagnosis of pneumonia in children. ✅
- Chest X-ray images, patient medical reports, symptom patterns. ✅
- Evaluation Stage – to check accuracy and reliability of predictions. ✅
Case Study 3: Food Processing – Quality Control
Scenario:
A biscuit manufacturing company faces complaints about defective biscuits reaching stores. They implement an AI system that scans each biscuit on the production line. The AI model classifies biscuits as “good” or “defective” and automatically removes defective ones. Over time, customer satisfaction improves, and product wastage decreases.
Questions:
- Identify the AI use case in this scenario.
- Explain the benefit of AI for the company.
- Which stage of the AI project cycle involves analyzing biscuit images to train the AI model?
Answers:
- Quality checking and defect detection in food processing. ✅
- Benefits: Improved product quality, reduced wastage, and increased customer satisfaction. ✅
- Data Exploration Stage – analyzing biscuit images to identify patterns and train the model. ✅
4. Short Answer Questions – AI Use Cases & Project Cycle
⃣
Q: What is an AI use case?
A: An AI use case is a real-life problem where Artificial Intelligence is applied to improve efficiency, save time, increase accuracy, or support decision-making.
⃣
Q: Name the first stage of the AI project cycle and its purpose.
A: Problem Identification – to clearly define the problem, identify who is affected, and determine the goal of the AI solution.
⃣
Q: List three types of data that can be collected for an AI project in agriculture.
A: Crop images, weather data, and soil data.
⃣
Q: What is the main goal of the Data Exploration stage?
A: To clean the data, remove errors, study patterns, and understand trends or relationships for modeling.
⃣
Q: Give an example of an AI use case in healthcare.
A: Disease diagnosis using medical image analysis or patient monitoring.
⃣
Q: What happens during the Model Idea (Modelling) stage of an AI project?
A: An AI model is developed to learn from data and make predictions or support decisions.
⃣
Q: Why is the Evaluation stage important in an AI project?
A: To test the AI solution, check its accuracy, and suggest improvements if results are unsatisfactory.
⃣
Q: Name two benefits of using AI in food processing.
A: Improved food quality and safety, and detection of defective items to reduce wastage.
⃣
Q: How can AI help in dairy farming?
A: AI can monitor animal health, track milk quality, predict production, and automate feeding systems.
Q: Why is teamwork important in group AI projects?
A: Teamwork allows students to divide tasks, collaborate creatively, and present sector-specific AI solutions effectively.
5. Long Answer Questions – AI Use Cases & Project Cycle
1️⃣ Explain the concept of an AI use case with an example from agriculture.
Answer:
An AI use case is a real-life situation where Artificial Intelligence is applied to solve a specific problem efficiently, accurately, and in a timely manner. It explains how AI helps improve decision-making or processes.
Example – Agriculture:
Farmers often suffer crop losses due to diseases. Using AI, crop images, weather data, and soil conditions are collected. The AI model analyzes this data to predict whether a crop is healthy or diseased. Farmers are then alerted in advance, allowing them to take preventive measures.
Benefits:
- Reduces crop losses
- Increases crop yield
- Saves time and effort for farmers
2️⃣ Describe the stages of the AI project cycle with examples.
Answer:
The AI project cycle is a structured approach to solving real-world problems using AI. It has the following stages:
- Problem Identification: Identify the real problem and define the goal.
- Example: Farmers face crop disease. Goal: Predict disease early.
- Data Collection: Collect relevant, accurate, and sufficient data.
- Example: Crop images, weather, and soil data.
- Data Exploration: Clean data, remove errors, and study patterns.
- Example: Analyze patterns between weather conditions and disease outbreaks.
- Model Idea (Modelling): Develop an AI model to make predictions.
- Example: AI predicts healthy or diseased crops based on patterns.
- Evaluation: Test the model, check accuracy, and make improvements if needed.
- Example: Compare predicted results with actual crop conditions and refine the model.
3️⃣ Explain how AI is applied in the healthcare sector with a real-life example.
Answer:
Application of AI in healthcare:
AI helps in early disease diagnosis, medical image analysis, and patient monitoring.
Example:
A hospital uses AI to detect pneumonia in children. Chest X-rays and patient medical reports are analyzed by the AI system. The system identifies patterns in symptoms and predicts the presence of disease. Doctors can start treatment early, improving recovery rates.
Benefits:
- Early diagnosis reduces health risks
- Supports doctors in decision-making
- Improves patient care and treatment outcomes
4️⃣ Describe the benefits of AI in food processing and dairy farming.
Answer:
Food Processing:
- AI inspects food products for quality and safety
- Detects defective items automatically
- Reduces wastage and increases customer satisfaction
Example: A biscuit factory uses AI to scan biscuits. Defective biscuits are removed automatically, ensuring high-quality products reach customers.
Dairy Farming:
- AI monitors animal health and milk quality
- Tracks production patterns and predicts outputs
- Automates feeding systems, improving efficiency
Example: AI tracks the health of cows and predicts milk yield, allowing better farm management and increased productivity.
Overall Benefits:
- Improved product quality
- Reduced losses and wastage
- Efficient farm and food management
5️⃣ How can students learn from AI projects, and what activities can be suggested to improve understanding of AI use cases?
Answer:
Students can learn AI concepts by applying them to real-life problems. Project-based learning allows them to understand AI use cases and follow the AI project cycle.
Suggested Activities:
- Project-Based Learning:
- Students select a real-world problem, collect data, and develop AI solutions.
- Example: Predicting plant growth or analyzing class performance using AI.
- Group AI Projects:
- Students work in groups, divide tasks, and collaborate creatively.
- Encourages teamwork and practical understanding of AI applications.
- Presentation of Sector-Specific AI Solutions:
- Students present their AI project findings, explain the problem, data, model, and outcome.
- Improves communication skills and confidence.
Benefits for Students:
- Develops problem-solving skills
- Enhances critical thinking and creativity
- Helps understand real-world AI applications