AI Glossary for K12 Teachers and Students

A teacher-friendly reference, with simple explanations you can share directly with students.

Core Concepts

Artificial Intelligence (AI): Computers or machines that can perform tasks that normally require human intelligence, like recognizing speech, finding patterns, or making decisions.
Machine Learning (ML): A type of AI where computers learn from data instead of being programmed with step-by-step instructions.
Neural Network: A computer system inspired by the brain, used to recognize patterns in data (like photos, speech, or text).
Algorithm: A step-by-step set of instructions that tells a computer how to solve a problem.
Training Data: The examples (text, images, numbers, etc.) given to an AI system to help it learn.
Model: The result of training an AI — it’s the system that can now make predictions or generate outputs.

AI in Action

Chatbot: A computer program that can “chat” with humans, often powered by AI.
Generative AI: AI that creates new content (like writing, art, or music) based on patterns it has learned.
Natural Language Processing (NLP): A branch of AI that helps computers understand and respond to human language.
Computer Vision: AI that helps machines “see” and understand images or video.
Recommendation System: AI that suggests content (like movies, songs, or products) based on your past choices.

Classroom-Relevant Terms

Bias: When an AI system treats some people unfairly because of problems in the data it was trained on.
Hallucination: When an AI confidently makes up information that isn’t true.
Misinformation: False or misleading information that can be spread by people or AI.
Transparency: How much we can understand and explain what an AI system is doing.
Ethics: Questions about what’s right or wrong when using AI, such as privacy, fairness, and responsibility.
Digital Citizenship: Using technology safely, responsibly, and respectfully, including when interacting with AI.

Advanced (Teacher Reference)

Large Language Model (LLM): A powerful AI trained on vast amounts of text to generate and understand language (e.g., ChatGPT, Gemini, Claude).
Prompt: The input or question you give to an AI system.
Token: A chunk of text (like a word or part of a word) that AI uses to process language.
Training vs. Inference: Training is when the AI learns from data; inference is when it uses what it has learned to make predictions or generate answers.
Supervised vs. Unsupervised Learning: In supervised learning, AI is trained with labeled examples (like “this is a cat”); in unsupervised learning, AI looks for patterns without labels.

Core Concepts

Artificial Intelligence (AI): Computers or machines that can perform tasks that normally require human intelligence, like recognizing speech, finding patterns, or making decisions.

Machine Learning (ML): A type of AI where computers learn from data instead of being programmed with step-by-step instructions.

Neural Network: A computer system inspired by the brain, used to recognize patterns in data (like photos, speech, or text).

Algorithm: A step-by-step set of instructions that tells a computer how to solve a problem.

Training Data: The examples (text, images, numbers, etc.) given to an AI system to help it learn.

Model: The result of training an AI — it’s the system that can now make predictions or generate outputs.

AI in Action

Chatbot: A computer program that can “chat” with humans, often powered by AI.

Generative AI: AI that creates new content (like writing, art, or music) based on patterns it has learned.

Natural Language Processing (NLP): A branch of AI that helps computers understand and respond to human language.

Computer Vision: AI that helps machines “see” and understand images or video.

Recommendation System: AI that suggests content (like movies, songs, or products) based on your past choices.

Classroom-Relevant Terms

Bias: When an AI system treats some people unfairly because of problems in the data it was trained on.

Hallucination: When an AI confidently makes up information that isn’t true.

Misinformation: False or misleading information that can be spread by people or AI.

Transparency: How much we can understand and explain what an AI system is doing.

Ethics: Questions about what’s right or wrong when using AI, such as privacy, fairness, and responsibility.

Digital Citizenship: Using technology safely, responsibly, and respectfully, including when interacting with AI.

Advanced (Teacher Reference)

Large Language Model (LLM): A powerful AI trained on vast amounts of text to generate and understand language (e.g., ChatGPT, Gemini, Claude).

Prompt: The input or question you give to an AI system.

Token: A chunk of text (like a word or part of a word) that AI uses to process language.

Training vs. Inference: Training is when the AI learns from data; inference is when it uses what it has learned to make predictions or generate answers.

Supervised vs. Unsupervised Learning: In supervised learning, AI is trained with labeled examples (like “this is a cat”); in unsupervised learning, AI looks for patterns without labels.