The Big AI Glossary

A set of instructions or rules a computer follows to solve a problem or perform a task.

A field of computer science that focuses on creating machines capable of performing tasks that typically require human intelligence.

A process used in training neural networks where the model adjusts its internal weights to reduce errors.

Very large sets of data that are analyzed by machines to uncover patterns, trends, and associations.

A conversational AI program that simulates dialogue with users via text or speech.

A structured AI system that tracks how students think, learn, and grow; feedback supports effort, not just outcomes.

A large collection of text used to train language-based AI models.

Protecting sensitive personal information used by AI tools, critical in education settings.

A subset of machine learning that uses neural networks with many layers to process complex patterns.

AI systems designed so humans can understand how decisions are made.

Customizing a pre-trained AI model by training it further on a smaller, specific dataset (e.g., to support school curriculum content).

The accurate, real-world information used to validate an AI’s predictions or outputs.

The process by which an AI model uses its training to make predictions or generate content based on new data.

A powerful AI trained on massive amounts of text data to understand and generate human-like language.

A series of connected algorithms designed to mimic how the human brain processes information.

The input you give to an AI model to get a response (question, instruction, etc.).

The information fed to an AI model to help it learn patterns or behavior.

A theoretical form of AI that can understand, learn, and apply intelligence across a broad range of tasks—like a human.

A concept where AI enhances human decision-making, not replaces it.

When AI models reflect or amplify unfair, unbalanced, or inaccurate patterns in their training data.

A system (like some AI models) whose inner workings are not visible or easily understood by humans.

A machine learning task where items are categorized into defined groups (e.g., “spam” vs. “not spam”).

A field of AI that trains computers to interpret and understand visual information like images and video.

Tagging data (like photos or text) with labels so an AI model can learn from it.

A collection of data used to train, test, or validate AI models.

The practice of developing and using AI systems responsibly, transparently, and fairly.

When the outputs of an AI model influence its future behavior, for better or worse.

AI that can create new content, such as writing, music, images, or code.

When AI generates incorrect or fictional information but presents it as fact.

The data or prompt given to an AI system to generate a result.

AI systems that can process multiple types of input (e.g., text + images).

When an AI system can understand or generate a wide range of words without being restricted to a specific list.

A chunk of text (like a word or part of a word) used by AI to understand language.

The space where humans interact with AI systems, such as a dashboard or chat window.