AI Terms To Be Familiar With:

Understanding these terms can help educators navigate discussions about AI
and technology in the classroom and equip students with the knowledge needed for the future.

Algorithm: A set of rules or instructions given to a computer to solve a specific problem or perform a particular task.

Chatbot: A computer program designed to simulate conversation with human users, especially over the Internet.

Bias in AI: Systematic and unfair favoritism or prejudice in the decisions made by AI systems, often reflecting existing biases in the training data.

Neural Network: A computational model inspired by the human brain that is used in machine learning to recognize patterns.

Machine Learning (ML): A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.

Training Data: The dataset used to train a machine learning model, consisting of examples used to teach the model patterns and relationships.

Supervised Learning: A type of machine learning where the algorithm is trained on a labeled dataset, with input-output pairs.

Unsupervised Learning: A type of machine learning where the algorithm is given unlabeled data and must find patterns and relationships without explicit guidance.

Reinforcement Learning: A type of machine learning where an agent learns to make decisions by receiving feedback in the form of rewards or penalties.

Computer Vision: A field of AI that enables machines to interpret and make decisions based on visual data, such as images and videos.

What can AI be used for in Education?