Secondary Artificial Intelligence (AI) Glossary of Terms
Welcome to the Secondary AI Glossary: Decode the Digital World
Artificial Intelligence is shaping how we learn, communicate, and think—and it’s already part of your life. This glossary helps you understand key AI terms in plain language, with real-world examples you can relate to. Whether you’re using a smart assistant, writing with AI help, or exploring tech careers, this guide equips you with the knowledge to be informed, responsible, and empowered in a tech-driven world.
What it Means:
A set of step-by-step instructions a computer follows to solve a problem or complete a task.
Why It Matters: Algorithms power the tools we use every day—from recommending videos to grading assignments automatically.
Example: When Netflix suggests a movie you might like, it’s using an algorithm based on your viewing history.
What it Means:
A branch of computer science focused on making machines think, learn, and solve problems like humans.
Why It Matters: AI is changing the way we learn, communicate, and make decisions—understanding it helps you use it wisely.
Example: AI powers apps like Grammarly that check your grammar and offer writing suggestions.
What it Means:
A person who builds and trains AI systems and machine learning models to solve real-world problems.
Why It Matters: AI engineers help create the apps, voice assistants, and recommendation systems we use every day.
Example: An AI engineer might help develop the algorithm that makes Netflix suggest movies you’ll like.
What it Means:
AI powers apps like Grammarly that check your grammar and offer writing suggestions.
Why It Matters: Knowing how AI works helps you become a smart and ethical user in school, work, and life.
Example: When you use a chatbot, you can ask yourself who created it and whether it’s giving you fair or accurate info.
What it Means:
A person who leads the planning and design of AI features or tools, making sure they are useful, easy to use, and solve real problems.
Why It Matters: AI product managers connect the tech team with the people who will actually use the AI—making sure the product works for real users.
Example: An AI product manager might help design a classroom app that uses AI to recommend personalized lessons to students.
What it Means:
Technology that adds digital elements to your real-world environment using devices like phones or AR glasses.
Why It Matters: AR helps visualize information in 3D, making learning more interactive and fun.
Example: Using your phone to see a virtual solar system appear on your desk during science class.
What it Means:
Using technology to make systems or processes work with little or no human help.
Why It Matters: Automation saves time and effort but also changes how people work and learn.
Example: Scheduling messages to send automatically or using tools to grade quizzes instantly.
What it Means:
A preference or prejudice in data or algorithms that can lead to unfair outcomes.
Why It Matters: AI systems trained on biased data can make unfair or inaccurate decisions.
Example: If a resume filter favors male applicants, that’s an example of algorithmic bias.
What it Means:
An AI tool that can talk with people using text or voice, often used for help or information.
Why It Matters: Chatbots let users interact with machines in natural ways, like asking questions or getting feedback.
Example: Asking a chatbot to help summarize a book or explain a science term.
What it Means:
A set of coded instructions that tells a computer how to do something.
Why It Matters: Programs are the foundation of all apps and systems we use—from games to learning platforms.
Example: A student writing code in Python to build a math game for class.
What it Means:
A type of AI that helps machines see, identify, and understand images and videos.
Why It Matters: Computer vision is used in everything from facial recognition to diagnosing medical images.
Example: An app that lets you scan and translate handwritten notes uses computer vision.
What it Means:
Information that computers collect, store, and analyze to make decisions or predictions.
Why It Matters: AI tools depend on data to learn patterns and improve performance.
Example: Your quiz scores over time help a learning app suggest topics you should review.
What it Means:
Protecting your personal information and controlling how it’s shared or used.
Why It Matters: AI tools often use your data, so it’s important to know your rights and how to protect yourself.
Example: Reading the privacy settings before using an educational app at school.
What it Means:
A professional who analyzes large amounts of data to find patterns, make predictions, and help businesses or organizations make decisions.
Why It Matters: Data is at the heart of AI. Without clean, smart data, AI systems can’t learn or work effectively.
Example: A data scientist might help a city figure out the best traffic routes using data from thousands of cars.
What it Means:
Fake media created using AI that looks or sounds real, often used to mislead.
Why It Matters: Knowing how to spot deepfakes helps protect you from misinformation and online scams.
Example: A video showing a celebrity saying something they never actually said.
What it Means:
Being a safe, responsible, and respectful user of digital tools and platforms.
Why It Matters: Good digital citizens help create positive, ethical, and informed online communities.
Example: Checking facts before sharing posts or respecting others in online discussions.
What it Means:
AI that tries to detect human emotions using facial expressions, voice, or text.
Why It Matters: While it can improve user experiences, it also raises privacy and fairness concerns.
Example: A video app that guesses how engaged you are based on your facial expressions.
What it Means:
Someone who ensures that AI systems are designed and used in ways that are fair, safe, and respect human rights.
Why It Matters: AI decisions can affect real people—so someone needs to ask: “Is this right?”, “Who might be harmed?” and “How can we make this fair?”
Example: An AI ethicist might guide a company to make sure their facial recognition software doesn’t discriminate against people of color.
What it Means:
A set of rules or ideas about what is right or wrong when creating and using technology.
Why It Matters: Ethical thinking helps us make choices that protect people’s rights and safety in a digital world.
Example: Asking whether it’s fair to use AI to monitor student behavior or grade work.
What it Means:
Technology that identifies people by analyzing their facial features.
Why It Matters: It’s used in phones, security, and schools—but can raise issues about surveillance and bias.
Example: Unlocking your phone with your face or checking in for attendance at school.
What it Means:
A type of machine learning where AI models are trained across many devices (like phones or computers) without the data ever leaving those devices.
Why It Matters: It allows AI systems to learn from data while keeping it private and secure—perfect for protecting user information like health data or personal messages.
Example: Your phone might improve its autocorrect by learning how you type, but that data stays on your device. The AI learns from everyone’s devices together without collecting personal info in one place.
What it Means:
AI that can create new content like text, music, art, or code based on patterns it has learned.
Why It Matters: Generative AI helps students brainstorm, create projects, and explore ideas in new ways.
Example: Using ChatGPT to help draft a story or design a logo with an AI image tool.
What it Means:
An approach where humans work with AI to guide decisions or correct mistakes.
Why It Matters: It keeps humans in control of AI tools, especially in important or sensitive tasks.
Example: A teacher reviewing AI-graded essays to ensure fairness and accuracy.
What it Means:
A type of AI trained on massive amounts of text to understand and generate human language.
Why It Matters: LLMs like ChatGPT can write, explain, and answer questions in natural language.
Example: Typing a prompt into a chatbot and getting a detailed response or story.
What it Means:
A process where computers learn from data without being explicitly programmed for every task.
Why It Matters: It allows AI to improve over time, based on patterns and feedback.
Example: An app that gets better at suggesting articles the more you use it.
What it Means:
A scientist who studies and develops new ways for computers to learn from data and improve over time.
Why It Matters: These researchers help push the limits of what AI can do—creating smarter, faster, and more ethical systems.
Example: A machine learning researcher might develop a new algorithm that helps self-driving cars make better decisions in unexpected situations.
What it Means:
AI that helps machines read, understand, and respond to human language.
Why It Matters: NLP powers many tools you use—like chatbots, auto-correct, and language translation.
Example: Using a writing assistant to fix grammar or rewrite a paragraph.
What it Means:
A system of algorithms inspired by the human brain that helps AI make sense of complex data.
Why It Matters: It helps AI recognize images, understand speech, and make decisions.
Example: A neural network helps AI identify whether a photo shows a dog or a cat.
What it Means:
AI’s ability to find similarities or trends in data.
Why It Matters: It helps AI make predictions and recommendations based on past behavior.
Example: Noticing you usually study math on Wednesdays and suggesting a new practice quiz then.
What it Means:
A way of using technology and data to adapt learning experiences to fit each student’s strengths, needs, and pace.
Why It Matters: Personalized learning helps students stay engaged and make progress in ways that work best for them—whether they need extra help or more challenge.
Example: An AI-powered learning app might suggest different reading levels or problem types based on how you’ve been performing.
What it Means:
A person who specializes in writing clear, effective instructions (called prompts) to get the best responses from AI models like ChatGPT.
Why It Matters: As more people use AI in everyday work, knowing how to talk to it clearly is becoming a valuable skill.
Example: A prompt engineer might help a company automate email replies or summarize research using the right prompts.
What it Means:
Designing clear, effective instructions to get the best results from AI tools.
Why It Matters: Better prompts mean better responses, helping you get exactly what you need from AI.
Example: Typing ‘Explain the water cycle for a 6th grader in 3 sentences’ instead of just ‘water cycle.’
What it Means:
AI that suggests content or products based on your preferences and past behavior.
Why It Matters: These systems shape what we watch, read, and buy—so it’s important to know how they work.
Example: Spotify suggesting new music based on what you’ve listened to.
What it Means:
Creating and using AI in a way that is ethical, fair, and safe for everyone. This includes reducing bias, protecting privacy, and being transparent.
Why It Matters: AI impacts real people, so it’s important that it works correctly and doesn’t harm or exclude anyone.
Example: A responsible AI system will not make decisions based on race or gender and will clearly explain how it makes choices—like in hiring or grading.
What it Means:
A machine that can be programmed to carry out tasks automatically, sometimes with AI.
Why It Matters: Robots are used in science, medicine, industry, and even classrooms to help with tasks.
Example: A robot that delivers materials in a hospital or teaches coding in class.
What it Means:
A person who designs, builds, and programs robots—often using AI so the robots can see, move, or make decisions on their own.
Why It Matters: Robots with AI are used in medicine, space, factories, and even homes—roboticists bring these smart machines to life.
Example: A roboticist might build a robot that can help elderly people with tasks like picking up dropped items or reminding them to take medicine.
What it Means:
A program that uses voice or text to help users with tasks like setting reminders, playing music, or answering questions.
Why It Matters: Smart assistants show how AI can interact with people in helpful, everyday ways, using natural language and learning your preferences over time.
Example: Saying “Hey Siri, what’s the weather?” or “Alexa, remind me to study at 7 PM” uses a smart assistant powered by AI.
What it Means:
A branch of computer science focused on making machines think, learn, and solve problems like humans.
Why It Matters: AI is changing the way we learn, communicate, and make decisions—understanding it helps you use it wisely.
Example: AI powers apps like Grammarly that check your grammar and offer writing suggestions.
What it Means:
Technology that allows computers to understand and respond to spoken language.
Why It Matters: It’s used in voice typing, accessibility tools, and voice-controlled assistants.
Example: Using voice-to-text to write an email or control your smart speaker.
What it Means:
The information AI uses to learn how to make decisions or predictions.
Why It Matters: The quality and diversity of training data affect how fair and accurate an AI tool is.
Example: Feeding thousands of images of cats and dogs to train a program to tell them apart.
What it Means:
A computer-generated environment you can explore and interact with using special devices.
Why It Matters: VR creates immersive learning experiences in subjects like history, science, and art.
Example: Putting on a headset to tour ancient Egypt or explore the inside of a volcano.