AI Glossary for Educators

Computer systems designed to perform tasks that typically require human intelligence, such as learning, problem-solving, pattern recognition, and decision-making.

A subset of AI where computers learn from data and improve their performance over time without being explicitly programmed for every scenario.

An advanced form of machine learning that uses neural networks with multiple layers to process complex patterns in large amounts of data.

A set of step-by-step instructions that tells a computer how to solve a problem or complete a task.

The information used to teach an AI system how to perform tasks. The AI learns patterns from this data to make predictions or decisions.

The result of training an AI system on data. It's like a trained brain that can make predictions or generate content based on what it learned.

A computer system inspired by the human brain, made up of interconnected nodes (like neurons) that process information in layers.

The branch of AI that helps computers understand, interpret, and generate human language in a way that is meaningful and useful.

The process of breaking down text into smaller pieces (tokens) like words or phrases that AI can process and understand.

AI's ability to detect emotions and attitudes in text, determining whether writing is positive, negative, or neutral.

AI systems that can create new content—text, images, code, or other media—based on patterns learned from training data.

A type of AI trained on vast amounts of text data that can understand and generate human-like language.

A specific type of large language model developed by OpenAI that's particularly good at understanding context and generating coherent text.

A conversational AI tool developed by OpenAI that uses GPT models to have natural conversations and assist with various tasks.

The instruction or question you give to an AI system to tell it what you want it to do or create.

The skill of crafting effective instructions for AI to get the best possible results. It's like learning how to ask the right questions.

The background information you provide to AI to help it understand your specific situation and generate more relevant responses.

Giving AI a few examples of what you want before asking it to create something similar.

When AI systems produce unfair or prejudiced results because of biases in their training data or design.

When AI generates information that sounds plausible but is actually incorrect or made up.

The practice of developing and using AI in ways that are fair, transparent, accountable, and respect human rights and privacy.

Following the Family Educational Rights and Privacy Act, which protects student education records and personal information.

Any information that can be used to identify a specific individual, such as names, addresses, or student ID numbers.

Protecting information from unauthorized access, use, or theft through encryption and secure storage practices.

Software that uses AI to help improve writing through suggestions, corrections, and content generation.

A way for different software applications to communicate and share data with each other.

Educational technology that adjusts content and pacing based on individual student performance and needs.

Using AI to evaluate and score student work, particularly for objective assessments and writing assignments.

The process of helping people and organizations transition to new tools, processes, or ways of working.

A small-scale test of a new tool or approach before rolling it out to everyone.

Hypothetical AI that can understand, learn, and apply knowledge across any domain, like human intelligence. This doesn't exist yet.

AI systems that can process and generate multiple types of content—text, images, audio, and video—simultaneously.