AI is no longer just science fiction — it’s in your home, your kids’ classrooms, and the apps you use every day. AI can be incredibly beneficial for children’s learning and development, but it also carries risks if not used responsibly. The more parents know about AI, the better they can help their kids navigate its use.
But understanding the language around AI can feel like learning a foreign language. This guide breaks down the most common AI terms parents should know (no tech degree required).
Artificial General Intelligence (AGI)
AGI refers to a theoretical AI that would be as smart and adaptable as a human across a wide range of tasks — not just one specific job like answering questions or generating images. Unlike today’s AI, AGI could learn, reason, and make decisions much like a person.
Example: If an AI could do your taxes, help your child with math homework, write a novel, and learn a new language without being specially trained for each task, that would be AGI. Right now, AGI doesn’t exist, but many scientists and companies are working toward it.
AI Agent
A system that can take actions on its own to complete a task or goal, often based on instructions, data, or learning from its environment. It doesn’t just respond. It acts, plans, and sometimes adapts over time.
Example: When you ask a virtual assistant to book a flight, and it searches prices, compares dates, and completes the booking for you. That’s an AI agent doing the work for you without needing step-by-step help.
Algorithm
A set of rules or steps a computer follows to solve a problem or make decisions.
Example: TikTok uses an algorithm to decide which videos show up on your child’s For You Page based on what they watch and like.
Artificial Intelligence (AI)
A broad term for technology that can perform tasks normally requiring human intelligence, such as understanding language, recognizing images, or making decisions.
Example: Siri, Alexa, and ChatGPT are all powered by AI.
Bias (in AI)
When an AI reflects unfair patterns from its training data, often leading to skewed or discriminatory results.
Example: If an AI used to screen job applications was trained mostly on resumes from men, it might develop a bias that favors male applicants. That bias could cause the AI to rank a qualified woman lower simply because of patterns it “learned” from the data.
Big Data
Big data refers to extremely large and complex sets of information that are too massive for regular computers to handle easily. AI systems use big data to learn patterns, make predictions, or improve performance.
Example: A traffic app like Waze uses big data (millions of drivers’ locations, speeds, and reports) to predict traffic jams and suggest faster routes in real time.
Chatbot
An AI tool designed to have conversations with humans, usually through text.
Example: When your child chats with a homework helper like ChatGPT or a customer service bot, they’re talking to a chatbot.
Data Mining
The process of analyzing large sets of data to find patterns, trends, or useful information. It helps companies and AI systems make decisions or predictions based on what the data reveals.
Example: A school district might use data mining to look at years of student test scores and attendance to find out which schools need extra support.
Dataset
A collection of related information or data that’s organized and used for analysis or training AI models. It can include anything from numbers and text to images and audio.
Example: To teach an AI how to recognize bears, developers might use a dataset made up of thousands of labeled bear photos. The dataset helps the AI learn what a bear looks like.
Deepfake
An AI-generated video, image, or audio that looks or sounds real but is fake.
Example: A deepfake video might show a popular celebrity endorsing a product they’ve never actually used. Because it looks real, kids (and adults) might believe the video is true — even though it was entirely made with AI.
Deep Learning
A type of machine learning where an AI uses many layers of processing (like a very complex brain) to understand patterns in data, especially things like images, sound, or language.
Example: When a photo app automatically tags your family members in pictures, it’s using deep learning to recognize their faces.
Fine-Tuning
The process of training an existing AI model to do something more specific by giving it additional, focused data.
Example: A company that makes parental control apps might fine-tune an AI to recognize slang or coded language teens use online, so it can alert parents to signs of cyberbullying or dangerous behavior.
Generative AI
AI that creates new content such as text, images, videos, or music, based on patterns it has learned.
Example: Your child might use generative AI to instantly create a picture of a “unicorn riding a skateboard in space,” or to write a spooky story about a haunted treehouse for a school project. The AI didn’t copy it from anywhere, it actually generated something brand new based on what it’s learned.
Hallucination
When an AI confidently gives a response that’s factually wrong or made up.
Example: When asked to list fun facts about penguins, the AI said “penguins can fly short distances if they flap hard enough.” That’s a hallucination — the AI made it up, even though it sounds like it could be real.
Large Language Model (LLM)
A type of AI trained on huge amounts of text data to understand and generate human-like language.
Example: ChatGPT is an LLM — it can help write essays, summarize articles, or answer questions because it’s read billions of words.
Machine Learning (ML)
A method for computers to learn from data and get better at tasks without being directly programmed for every step. Instead of being told exactly what to do, the AI finds patterns and improves over time.
Example: When Netflix suggests movies your family might like, it’s using machine learning by learning from what you’ve watched before to recommend new shows.
Model (in AI)
A specific version of an AI that has been trained to perform tasks like writing, recognizing images, or playing music
Example: DALL·E is a model that generates pictures, while ChatGPT is a model that generates text.
Natural Language Processing (NLP)
NLP is how a computer turns information into words or speech that people can understand. It helps machines write or talk in a way that sounds natural to humans.
Example: When a voice assistant reads you a weather report or a chatbot writes a story, that’s NLP turning data into human language.
Neural Network
A computer system inspired by the human brain’s structure, made up of layers that process data to help AI “think” and analyze step-by-step.
Example: When your phone’s camera automatically detects a face and focuses on it, that’s a neural network at work. The AI is learning to spot patterns that tell it, “This is a face!”
OpenAI
OpenAI is a technology company that builds and provides access to artificial intelligence models. It developed popular AI models such as GPT (including ChatGPT), DALL·E (for image generation), and Codex (for coding assistance). Some of its AI tools are free to use with limited features, while more advanced versions require a subscription or payment.
Example: OpenAI created ChatGPT, one of the most popular AI tools used today.
Parameter
A parameter is a setting or value inside an AI model that helps it make decisions and understand information. AI models have millions or even billions of these parameters working together.
Example: When a voice assistant learns how you say “play music,” its parameters adjust to recognize your voice better, so it knows what you mean even if you speak differently from other people.
Prompt
The input or question you give to an AI to get a response.
Example: Typing “Write a bedtime story about a talking teddy bear” into Gemini is giving it a prompt.
Token
A small piece of text that an AI uses to understand and generate language. Tokens can be whole words, parts of words, or even punctuation. AI breaks sentences into these tokens to process and respond accurately.
Example: The sentence “I love pizza!” has four tokens: “I,” “love,” “pizza,” and “!”. Or take the word “unbelievable”—it might be split into three tokens: “un,” “believ,” and “able.” Each token helps the AI understand bit by bit.
Training Data
The information fed into an AI model so it can learn how to do tasks.
Example: If you want an AI to recognize animals, its training data might include thousands of labeled pictures of cats, dogs, and birds.
Turing Test
The Turing Test is a way to measure whether a machine can act like a human in conversation. It was created by Alan Turing, a British mathematician and early computer scientist who helped crack secret codes during World War II.
Example: If you can’t tell whether you’re chatting with a person or a chatbot, the AI might be passing the Turing Test.
Understanding AI Together
Understanding these key AI terms can help us feel more confident and informed as technology becomes a bigger part of daily life. Knowing the basics makes it easier to talk with our kids about AI and stay involved in how they use it safely and responsibly.
If you want to learn more, we also have other articles that explore AI tools or specific AI chat assistants such as ChatGPT, Gemini, and Replika.
What other AI terms have you heard or want to learn more about? Share your thoughts or questions in the comments below. We’d love to hear from you!
Author Jackie Baucom
Jackie is a Gabb staff writer. She is the mother of four children and is passionate about keeping kids safe, happy, loved, and thriving. She has a degree in Mass Communication from the University of Utah.
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