The relevance of the study is as follows. Traditional methods of pedagogy do not allow objective and continuous monitoring of the emotional state of each student. The teacher usually relies on subjective observations — facial expressions behavior in a group — which may be inaccurate. At the same time modern technologies provide new opportunities: cameras and sensors backed by AI algorithms are able to automatically recognize facial expressions voice intonations and other signs of an emotional state. There are already commercial solutions for facial emotion recognition which have been used in marketing and security. Their potential application in education is an important topic as automated emotion analysis could provide educators with objective data on how engaged a group is whether students are overworked and who is experiencing difficulties or stress. It is the problem of mass learning fatigue and emotional burnout according to empirical studies that makes the introduction of such systems modern and practically significant and is the topic of this article. The article notes that the introduction of AI systems that can monitor both behavioral and physiological indicators opens up new opportunities for objective registration of cognitive states. For example sensors and wearable devices can measure heart rate heart rate variability brain activity and other parameters related to the level of concentration and mental stress. Computer vision can track eye gaze direction blinking frequency and posture which indirectly indicates the level of attention or fatigue. The study presents the results of a psychometric testing study using validated tools for emotional involvement in the educational process and a study of the factors of burnout and knowledge fatigue among full-time students at the Novorossiysk branch of the Financial University under the Government of the Russian Federation. The sample consisted of 125 respondents.

knowledge fatigue, inclusion in the educational process, psychometric testing, emotion recognition, emotional artificial intelligence, emotion recognition programs, study of students' emotional levels using neural networks.