According to a survey by the National Institute of Statistics in Spain, around 2.2% of the hearing impaired population, equivalent to 27 300 people, use Sign Language to communicate. This highlights the importance of ensuring accessibility to Sign Language as a fundamental resource to guarantee inclusion and equal opportunities for deaf and hearing impaired people. To achieve this goal, it is essential to promote and disseminate Sign Language and to remove barriers to its usage and access. Technology can be of great help in this field. Therefore, this work presents EduSign, a new AI-based application that classifies the alphabet of the Spanish Sign Language in real time aiming at facilitating the communication of people with hearing disabilities. Specifically, EduSign is a web application that runs a machine learning algorithm (i.e., a neural network) where users can learn and practice the Spanish Sign Language alphabet. The methodology that enabled the creation of EduSign consisted on capturing the coordinates of the gestures made in Sign Language using the MediaPipe tool. These coordinates were then used to train several machine learning classifiers (i.e., K-Nearest Neighbors, Ridge Regression, Gradient Boosting, Random Forest, Logistic Regression and a Neural Network). Out of these classifiers, the neural network offered the best performance. More specifically, using a custom dataset with the complete Spanish alphabet, it shown an accuracy of 99%. These results reinforce the idea that the application of artificial intelligence techniques can be of great help in improving communication for deaf and hearing impaired people. Hence, it is hoped that the system can be used effectively by the target audience.