In this tutorial, we will present the emerging direction of explainability that we will refer to as Natural-XAI. Natural-XAI aims to build AI models that (1) learn from natural language explanations for the ground-truth labels at training time, and (2) provide such explanations for their predictions at deployment time. For example, a self-driving car would not only see at training time that it has to stop in a certain environment, but it would additionally be told why this is the case, e.g., “Because the traffic light in front is red.”. At usage time, the self-driving car would also be able to provide such natural language explanations for its actions, thus reassuring the passengers. This direction has recently received increasingly large attention.