This article presents the classification of emotional artificial intelligence technologies used in the educational process according to various criteria, including types of educational activities. The potential of affective computing for personalizing learning, increasing student motivation and improving their well-being is considered. The author analyzes classification approaches by data types, learning methods, types of emotions and ways of interaction. Examples of technologies such as emotion analysis and automatic assessment are presented, as well as their role in optimizing the educational process. The prospects of using emotional artificial intelligence are highlighted, including improving interaction between students and teachers, and the risks associated with data confidentiality and ethical aspects are also noted. The article indicates the need for further research and policy development for the safe and effective implementation of technologies in the educational environment.