If an AI is trained with data that already contains the answers, what kind of learning is this?

The type of learning described in your question is known as supervised learning. In supervised learning, an AI model is trained on a labeled dataset, where each training example is paired with an output label. This means that the model learns to associate specific inputs with their corresponding outputs or answers.

In supervised learning, the model makes predictions based on input data and is guided by the correct answers during the training process. As it processes more data, the model adjusts its parameters to minimize the difference between its predicted outputs and the actual outputs provided in the training set. This approach is commonly used in tasks such as classification, where the goal is to categorize items into predefined classes, and regression, where the goal is to predict continuous values.

In contrast, other types of learning include unsupervised learning, where the model works with unlabelled data to find patterns or groupings, and reinforcement learning, where an agent learns to make decisions by taking actions in an environment to maximize cumulative rewards.