IK
Chapter 7 of 9
~15 min read
Last reviewed July 17, 2026

New and Emerging Technologies in Computing

Get introduced to Artificial Intelligence and its subfields alongside the Internet of Things (IoT) — and see how both are applied in healthcare, education, and transportation.

Written and reviewed by the IK Learning team

  • Artificial Intelligence (AI)
  • Machine Learning & Deep Learning
  • Natural Language Processing
  • Internet of Things (IoT)
  • Real-World AI Applications

This chapter surveys emerging technologies including Artificial Intelligence and its subfields — Machine Learning, Deep Learning, and Natural Language Processing — alongside the Internet of Things (IoT). It highlights real-world applications in healthcare, education, and transportation.

Chapter Introduction

What this chapter is about, and why it matters

This chapter looks at technologies that are still changing while you study them. That makes it unusual: the definitions are examinable and stable, but the examples date quickly, so it is worth knowing at least one current example for each idea rather than relying on the ones in any printed book.

The section students most often get wrong is the relationship between AI, machine learning and deep learning. They are not three competing things — they are nested, each one a subset of the one before. Getting that hierarchy right is usually worth more marks than any individual definition.

The Internet of Things is the easiest topic here to explain badly. An object is only part of the IoT if it both collects or acts on data AND connects to a network. A digital watch that does neither is not IoT, however modern it looks.

What You Will Learn

The skills this chapter is assessed on

  • Define Artificial Intelligence and explain how machine learning and deep learning relate to it.
  • Describe Natural Language Processing and give a real application.
  • Explain what the Internet of Things is and what makes a device part of it.
  • Give examples of emerging technologies applied in healthcare, education and transport.
  • Discuss both the benefits and the risks of these technologies.

Key Concepts Explained

6 core ideas — each with its definition and a separate worked example

Artificial Intelligence (AI)

Definition

Artificial Intelligence is the branch of computer science concerned with building systems capable of performing tasks that would normally require human intelligence, such as understanding language, recognising images, reasoning and making decisions.

Example

The system that decides which emails go to your spam folder is AI. It examines each message and judges it, rather than following one fixed rule written by a person — which is why it catches spam it has never seen before.

Machine Learning (ML)

Definition

Machine Learning is a subset of Artificial Intelligence in which a system improves its performance at a task by learning patterns from data, rather than being explicitly programmed with the rules for that task.

Example

To detect spam with traditional programming you would write rules by hand: block anything containing "free prize". With ML you instead supply 100,000 emails already labelled spam or not spam, and the system works out the distinguishing patterns itself — including ones no person thought of.

Detailed Explanation

The defining shift is where the rules come from. Traditional program: human writes rules, computer applies them. Machine learning: human supplies examples, computer derives the rules. This one sentence answers most "difference between" questions in this chapter.

Deep Learning

Definition

Deep Learning is a subset of machine learning that uses artificial neural networks with many layers to learn complex patterns directly from large amounts of raw data.

Example

Recognising a handwritten Urdu character is very hard to describe with rules. A deep learning network is shown thousands of examples and builds up its own understanding in stages — early layers detect edges and curves, later layers assemble those into whole characters.

Detailed Explanation

Hold the hierarchy in mind: AI is the whole field, machine learning is one approach within it, and deep learning is one approach within machine learning. Every deep learning system is machine learning and is AI; the reverse is not true.

Natural Language Processing (NLP)

Definition

Natural Language Processing is a field of AI that enables computers to understand, interpret and generate human language in written or spoken form.

Example

When you speak "set an alarm for six" to a phone, NLP converts the sound to text, identifies "set an alarm" as the intent and "six" as the time, and triggers the right action. Understanding the *meaning*, not just the words, is the NLP part.

Detailed Explanation

Human language is difficult for computers because it is ambiguous and context-dependent. "Book a table" and "read a book" use the same word for entirely different things, and only surrounding context resolves it.

Internet of Things (IoT)

Definition

The Internet of Things refers to a network of physical objects embedded with sensors, software and network connectivity, which allows them to collect data and exchange it with other devices and systems over the internet.

Example

A smart electricity meter records consumption every few minutes and transmits it to the utility company automatically. Nobody visits to read it, billing is based on real usage, and the customer can see their consumption in an app.

Detailed Explanation

Two conditions must both hold: the object senses or acts in the physical world, and it is connected to a network. A calculator is neither. A smart doorbell is both. Test any example against those two conditions rather than against how modern it seems.

Training data

Definition

Training data is the collection of examples supplied to a machine learning system so it can learn the patterns needed to make predictions on new, unseen data.

Example

A model that identifies diseased crops is trained on thousands of labelled photographs of healthy and diseased plants. If every training photo was taken in bright sunlight, the model will perform badly on a cloudy day — because that situation was never in the data.

Detailed Explanation

This is where AI bias comes from. A system trained on unrepresentative data produces unrepresentative results, and it does so confidently, which makes the error harder to spot than an obvious crash.

Where This Is Used in Real Life

The same ideas, outside the syllabus

Healthcare

AI systems help radiologists by highlighting areas of a scan that resemble known abnormalities, and IoT wearables monitor heart rate continuously and raise an alert if a pattern becomes dangerous. In both cases the technology assists a professional rather than replacing the diagnosis.

Education

Adaptive learning platforms adjust the difficulty of the next question based on how a student answered the previous ones, so two students using the same app receive different practice. NLP also powers automatic captioning, which makes recorded lessons accessible.

Transport

Traffic systems use sensors at junctions to adjust signal timing to actual traffic volume rather than a fixed schedule. Navigation apps combine live location data from many devices to detect congestion and reroute drivers around it.

Agriculture

Soil-moisture sensors in a field report to an irrigation controller that waters only the sections that need it. This is IoT solving a genuinely local problem — less water used, better yield, no guesswork.

Common Mistakes to Avoid

Errors that cost marks in this chapter, and the correction for each

Mistake

Treating AI, machine learning and deep learning as three separate fields.

Correct Approach

They are nested: deep learning ⊂ machine learning ⊂ AI. Drawing them as three concentric circles is worth doing in the exam.

Mistake

Defining AI as "a computer that thinks like a human".

Correct Approach

AI performs tasks that would normally require human intelligence. It does not claim to think, feel or understand in a human sense, and examiners mark down the stronger claim.

Mistake

Calling every modern gadget an IoT device.

Correct Approach

IoT requires sensing or acting in the physical world AND network connectivity. A digital calculator has neither.

Mistake

Listing only benefits when a question asks you to discuss a technology.

Correct Approach

"Discuss" means both sides. Pair each benefit with a genuine risk — privacy, job displacement, bias or security.

Mistake

Saying machine learning systems are always right because they use data.

Correct Approach

A model is limited by its training data. Biased or incomplete data produces confident wrong answers, which is more dangerous than an obvious error.

Exam Preparation Tips

Technique specific to this chapter

  • Draw the AI / ML / deep learning relationship as nested circles. It communicates the hierarchy instantly and is hard to get wrong once drawn.
  • Keep one specific, current example ready for each term. A named example almost always earns a mark that a general statement does not.
  • For "discuss" or "evaluate" questions, plan two benefits and two risks before writing. Balance is what the command word is testing.
  • When defining ML, use the phrase "learns from data rather than being explicitly programmed" — it is the distinguishing idea.
  • For IoT questions, check your example against both conditions (sensing/acting + connectivity) before you commit to it.

Quick Revision Summary

The whole chapter in one screen — read this the night before

  • AI = machines performing tasks that normally need human intelligence.
  • ML ⊂ AI: learns patterns from data instead of following hand-written rules.
  • Deep learning ⊂ ML: multi-layer neural networks learning from raw data.
  • NLP = computers understanding and generating human language.
  • IoT = physical objects with sensors + network connectivity.
  • Training data quality determines model quality — bias in, bias out.
  • Applications: healthcare diagnostics, adaptive education, smart traffic, precision farming.
  • Risks: privacy loss, bias, security vulnerabilities, job displacement.

Glossary of Terms

Words used in this chapter, defined plainly

Neural network
A computing structure loosely inspired by brain cells, arranged in connected layers.
Algorithm (in ML)
The procedure a system uses to find patterns in training data.
Model
The result of training — what the system uses to make predictions.
Sensor
A device that detects a physical property such as temperature or motion.
Automation
Carrying out a task with minimal human intervention.
Bias (in AI)
Systematic unfairness in results, usually caused by unrepresentative training data.

Practice Questions

Now test yourself on the concepts above. Collapse the answers to make it a real practice run.

Multiple Choice Questions with Explanations

6 MCQs — pick an option to check yourself, then read why the answer is right

1Which of the following is not a subfield of AI?

Correct answer: D — DBMS

DBMS (Database Management System) is not a subfield of Artificial Intelligence.

2Which of these counts as a security concern in IoT deployments?

Correct answer: D — All of the above

IoT devices face multiple security concerns including device vulnerability, data privacy, and lack of standardization.

3Which of the following is a use of AI in healthcare?

Correct answer: D — All of the above

AI is applied across healthcare in drug development, diagnosis, and patient monitoring.

4What is the main purpose of using AI techniques in machine learning models?

Correct answer: A — To improve accuracy

AI techniques are mainly aimed at improving the accuracy of machine learning models.

5What is the key difference between explainable (whitebox) and unexplainable (blackbox) AI models?

Correct answer: B — The ability to understand the decision-making process

Explainable (whitebox) models let humans understand how a decision was made; blackbox models don't.

6Which of the following is a use of IoT in the transportation domain?

Correct answer: D — All of the above

IoT in transportation includes smart traffic management, V2V communication, and predictive maintenance.

Short Questions with Answers

6 short-answer questions

Artificial Intelligence (AI) is the simulation of human thinking ability in computer systems, letting machines think, learn, and make decisions in ways similar to humans.
Two examples of AI applications in healthcare are: - Diagnosing diseases using AI-powered analysis of medical data. - Personalizing treatment plans for patients.
AI techniques in machine learning models help computers learn from data, improve performance over time, and produce accurate predictions or decisions.
The Internet of Things (IoT) is a network of physical objects fitted with sensors, software, and other technologies that connect to the internet to collect and exchange data.
IoT matters because it connects devices and systems, enabling seamless data sharing, improving efficiency, delivering better services, and creating new opportunities in areas like healthcare, agriculture, and smart homes.
Applications of IoT in the transportation domain include smart traffic lights, connected vehicles, real-time tracking systems, and predictive maintenance to make transportation safer and more efficient.

Long Questions with Detailed Answers

3 in-depth answers

- Artificial Intelligence (AI) in Education Artificial Intelligence (AI) is playing an increasingly important role in education by enhancing learning experiences, supporting teachers, and improving administrative efficiency. - Personalized Learning One of the key uses of AI in education is personalized learning, where AI-powered systems analyze a student's performance, learning style, and pace to provide customized learning materials and exercises. This helps students grasp concepts more effectively and makes sure no one is left behind. - Automation of Administrative Tasks AI is also used to automate administrative tasks, such as grading assignments, managing attendance, and tracking student progress. This reduces the workload of teachers, letting them focus more on teaching and mentoring. - AI-driven Tutoring Systems Additionally, AI-driven tutoring systems can provide instant feedback and guidance to students, answering questions and offering explanations similar to a human tutor. - Predictive Analytics Other uses include predictive analytics, which can flag students at risk of falling behind or dropping out, allowing educators to step in early. - Virtual Classrooms and E-learning AI is also built into virtual classrooms and e-learning platforms, enabling interactive learning, adaptive assessments, and immersive simulations.
- Introduction to Internet of Things (IoT) IoT is a revolutionary concept that is transforming the way we live and work. It involves connecting everyday devices and systems to the internet, letting them communicate and interact with each other. - Definition IoT is a network of physical "objects," or "things," fitted with sensors, software, and other technologies that enable them to exchange data with other devices and systems over the internet. - Significance of IoT IoT matters because it allows for the seamless integration of the physical and digital worlds. This connection lets devices collect and share data, which can be analysed to improve efficiency, provide better services, and create new opportunities in various fields such as healthcare, agriculture, and smart homes. - Components of IoT Systems An IoT system typically consists of the following components: - Sensors: These are devices that detect and measure physical properties like temperature, humidity, light, and motion. Sensors collect data from the environment. - Actuators: These are devices that convert energy into motion — in IoT an actuator can act on data to generate output. - Devices: These include everyday objects like smartwatches, refrigerators, and cars that are connected to the internet. Devices use the data collected by sensors to carry out specific tasks. - Networks: These are the communication pathways that connect sensors and devices to the internet, letting them share data. Networks can be wired or wireless. - Data Analysis: This involves processing and analysing the data collected by sensors to gain insights and make decisions. Data analysis can happen on the device itself, in the cloud, or on a central server. Example: Smart Home System One of the practical uses of IoT is a smart home system. A "smart home" has several internet-connected appliances, including the temperature control system, lighting, and surveillance cameras.
AI covers several subfields, each focusing on different aspects of intelligence and technology: - Machine Learning: Machine learning is a type of artificial intelligence where computers learn from experience and improve over time without being explicitly programmed. It's like teaching a computer by showing it lots of examples, and it works out how to do things on its own. - Deep Learning: Deep learning is a special kind of machine learning. It uses complex structures called neural networks, which are inspired by how our brains work. These networks help computers learn from lots of data and make decisions or recognize patterns even better. - Natural Language Processing (NLP): NLP is a technology that helps computers understand and talk to us in our language. It's like teaching a computer to read, write, and even chat with us. Example: When you ask Siri or Alexa a question, they use NLP to understand what you're saying and give you a helpful answer. Another example is when your phone suggests words to complete your sentence. - Computer Vision: Computer vision is a field of Artificial Intelligence (AI) that enables computers to see and understand the visual world. It helps computers interpret images and videos. - Robotics: Robotics is the science of building and programming robots. Robots are machines that can carry out tasks for us, like cleaning the floor or building cars. Some robots can even think and make decisions.

Important Questions for Revision

5 high-priority questions

AI is the simulation of human thinking ability in computer systems, letting machines think, learn, and make decisions in ways similar to humans.
IoT is a network of physical objects fitted with sensors and software that connect to the internet to collect and exchange data.
Sensors, Actuators, Devices, Networks, and Data Analysis.
Machine Learning, Deep Learning, and Natural Language Processing (NLP) are common subfields of AI, alongside Computer Vision and Robotics.
Diagnosing diseases using AI-powered analysis of medical data, and personalizing treatment plans for patients.

Frequently Asked Questions

6 quick answers to common questions about this chapter

AI is the broad goal of making machines behave intelligently. Machine Learning is one approach to achieving AI, where machines learn patterns from data instead of following fixed rules. Deep Learning is a more advanced form of machine learning that uses layered neural networks, useful for complex tasks like image recognition.
No. The regular Internet mainly connects computers and phones for people to use directly. IoT extends that same networking idea to everyday physical objects — like smart thermostats or connected cars — letting them collect and exchange data automatically, often without a person actively involved.
When you ask a voice assistant like Siri or Google Assistant a question and it understands and answers you correctly, that's NLP at work — it's the branch of AI that lets computers understand and respond to human language, spoken or written.
IoT lets everyday devices share data and act on it automatically — a smart streetlight that dims when no one is around, or a smart irrigation system that only waters crops when soil sensors detect dryness. This automation improves efficiency and convenience without needing constant human control.
Yes. AI and its subfields, Machine Learning, Deep Learning, NLP, and IoT — along with their real-world applications in healthcare, education, and transportation — are examinable and appear as MCQs and short/long answer questions.
AI can quickly analyze large amounts of medical data — like scans or patient records — to help doctors diagnose diseases faster and suggest personalized treatment plans. It doesn't replace the doctor's judgment, but it acts as a powerful assistant that spots patterns a human might take much longer to notice.

Chapter Test

6 questions with the answers hidden — check what you actually remember

You have just read the explanations above. This checks whether they stuck. The answers stay hidden until you finish, so it is closer to exam conditions than scrolling through the notes again.

  • 6 questions, one at a time — no time limit.
  • You can move back and change an answer before submitting.
  • Afterwards you get your score, every explanation, and what to re-read.

Your score is saved in this browser only. No account, nothing sent anywhere.

Related Topics in the Other Class

Class 10Introduction to AI and Machine LearningClass 10Applications of AI