COVID-19 has changed the way we use networks, as multimedia content now represents an even more significant portion of the traffic due to the rise in remote education and telecommuting. In this context, in which Wi-Fi is the predominant radio access technology (RAT), multicast transmissions have become a way to reduce overhead in the network when many users access the same content. However, Wi-Fi lacks a versatile multicast transmission method for ensuring efficiency, scalability, and reliability. Although the IEEE 802.11aa amendment defines different multicast operation modes, these perform well only in particular situations and do not adapt to different channel conditions. Moreover, methods for dynamically adapting them to the situation do not exist. In view of these shortcomings, artificial intelligence (AI) and machine learning (ML) have emerged as solutions to automating network management. However, the most accurate models usually operate as black boxes, triggering mistrust among human experts. Accordingly, research efforts have moved towards using Interpretable-AI models that humans can easily track. Thus, this work presents an Interpretable-AI solution designed to dynamically select the best multicast operation mode to improve the scalability and efficiency of this kind of transmission. The evaluation shows that our approach outperforms the standard by up to 38%.