30 AI Words Every Kid Should Know (With Simple Definitions)

You can't fully understand AI without knowing its vocabulary. Here are the 30 most important AI terms, explained simply. I've used all of these in my teaching at KidsFunLearnClub. After reading this, you'll have enough language to read almost any AI article aimed at a general audience.

— Parikshet More

Core Concepts

Artificial Intelligence (AI): The ability of a computer to do things that normally require human intelligence — recognising faces, understanding speech, making recommendations.

Machine Learning (ML): A type of AI where the computer learns from examples rather than being explicitly programmed with rules.

Neural Network: An AI system of connected mathematical nodes, loosely inspired by the human brain. Data flows through layers of nodes to produce outputs.

Deep Learning: Machine learning using neural networks with many layers. "Deep" = many layers. The technology behind modern image recognition, voice assistants, and language AI.

Algorithm: A set of step-by-step instructions a computer follows to solve a problem. Every AI model is an algorithm.

Model: The trained result of the machine learning process — the "finished product" that can make predictions or generate outputs.

Training: The process of feeding data to an AI and adjusting its settings to improve accuracy. Training happens before you use the AI.

Training Data: The collection of examples the AI learns from. The quality and diversity of training data shapes everything about the AI's performance.

Inference: When you use a trained AI model to make predictions. This is what happens when you talk to ChatGPT or your phone recognises your face.

Language AI

Large Language Model (LLM): An AI trained on enormous amounts of text to understand and generate language. ChatGPT, Claude, and Google Gemini are LLMs.

Prompt: The instruction or question you give an AI tool. The words in your prompt shape the quality of the response.

Prompt Engineering: The skill of writing effective prompts to get better AI responses.

Hallucination: When an AI produces false information with full confidence — because it doesn't know what it doesn't know.

Token: The basic unit LLMs process — roughly a word or part of a word. LLMs predict the next token given all previous tokens.

Context Window: How much text an LLM can "remember" at once in a conversation — its working memory limit.

Fine-tuning: Training an already-trained model further on specific data to specialise it for a particular task.

Learning Types

Supervised Learning: AI learning from labelled examples (this is a cat, this is a dog).

Unsupervised Learning: AI finding its own patterns in unlabelled data — discovering groups and structures without being told what to look for.

Reinforcement Learning: AI learning by trial and error — getting rewards for good actions and penalties for bad ones.

Practical Terms

Dataset: A collection of data used to train or test an AI model.

Bias: When an AI produces unfair or systematically skewed outputs because of patterns in its training data.

Overfitting: When an AI learns its training data too precisely and fails to generalise to new examples — like memorising answers rather than understanding the subject.

Accuracy: The percentage of predictions an AI model gets right on test data.

Generative AI: AI that creates new content — text, images, music, video — rather than just classifying or analysing existing content.

Computer Vision: AI that can interpret and understand visual information — images and video.

Natural Language Processing (NLP): AI that understands and generates human language.

Autonomous AI / AI Agent: An AI that can take sequences of actions to complete a task, not just respond to a single question.

AGI (Artificial General Intelligence): Hypothetical AI that can perform any intellectual task a human can. We don't have this yet. Current AI is "narrow" — excellent at specific tasks but unable to generalise the way humans do.

AI Safety: The field focused on ensuring AI systems behave safely, reliably, and in accordance with human values — especially as AI becomes more powerful.

Deepfake: AI-generated media (video, audio, images) that realistically depicts real people saying or doing things they never actually said or did.

Frequently Asked Questions

What is the most important AI vocabulary term to know?

Hallucination — because it describes AI's most dangerous limitation and is relevant every time you use any AI tool.

What is AGI?

Artificial General Intelligence — hypothetical AI that could do any intellectual task a human can. We don't have it yet. All current AI is narrow (good at specific tasks).

What is the difference between AI and machine learning?

AI is the broad concept. Machine learning is the main technique. All machine learning is AI, but not all AI uses machine learning.