Accuracy measures how often an AI model's predictions or answers are correct compared to the true results, showing its overall performance.

Bias in AI happens when a model unfairly favors certain ideas or groups, often because the data it learned from wasn’t fully balanced or fair.

AI literacy means understanding how AI works, its uses, and its risks, helping people make smart, ethical decisions about AI tools and technology.

An AI model is a computer program trained on data to recognize patterns and make decisions, predictions, or generate content based on that training.

Precision shows how many of an AI model’s positive results were actually correct, helping evaluate how exact the system’s responses are.

Recall measures how well an AI model finds all the correct answers, showing its ability to capture every relevant result from the data.

An algorithm is a step-by-step set of rules or instructions a computer follows to solve a problem or perform a specific task efficiently and accurately.

Artificial Intelligence is the ability of machines to perform tasks that typically require human intelligence, such as learning, reasoning, and decision-making.

Automation uses technology to perform tasks with little or no human help, speeding up processes like grading, scheduling, or data entry.

A chatbot is an AI tool designed to simulate conversation with users, answering questions, providing help, or chatting in a human-like way.

Classification is when an AI sorts data into categories, like labeling emails as spam or not spam, based on learned patterns.

A context window is the amount of text or information an AI model can remember and consider at one time when generating responses or making decisions.

A dataset is a collection of organized information—like texts, images, or numbers—used to train, test, or evaluate an AI system’s performance.

Deep Learning is a branch of machine learning that uses layered neural networks to process large amounts of data and solve complex problems like vision and language.

Ethics in AI focuses on making sure AI systems are fair, transparent, safe, and respectful of human rights while avoiding harm and bias.

Generative AI creates new content—like writing, art, music, or code—by learning patterns from existing data and producing original outputs.

Image recognition is when an AI identifies objects, people, or scenes in pictures, helping with things like facial recognition or photo tagging.

A neural network is a computer system inspired by the human brain, made of layers that process data and learn patterns to make decisions or predictions.

A Large Language Model is an AI trained on huge amounts of text to understand, generate, and predict human language across many topics and tasks.

Machine Learning is a type of AI where computers learn from data and improve over time without being explicitly programmed for each task.

NLP helps computers understand, interpret, and respond to human language in ways that are meaningful and useful.

In this guide, we’ll be covering the meaning of an neural network: what it is, its key aspects, and why its relevant to education.

Overfitting happens when an AI model learns its training data too exactly, making it less accurate when working with new, unseen data.

A prompt is the text or question you give an AI to tell it what you want it to do, like answering, writing, explaining, or creating something.

Prompt engineering is the skill of crafting effective instructions or questions to get better, more accurate results from an AI system.

Regression is when an AI predicts a continuous value, like forecasting a student’s score or house prices, based on input data.

Speech recognition is when an AI listens to spoken words and converts them into text or actions, like voice typing or virtual assistants.

A system prompt is an instruction given to an AI to set its behavior or role, guiding how it responds throughout a conversation or task.

A token is a small piece of text, like a word or part of a word, that AI models use to read, understand, and generate language step-by-step.

Training data is the information used to teach an AI model how to recognize patterns, answer questions, or make predictions based on examples.

Underfitting happens when an AI model is too simple to learn the patterns in its training data, leading to poor performance and wrong predictions.