Emotions are a very important factor that can significantly affect the level of success of students, their ability to think and analyze information during learning and make appropriate decisions. The emotional states of students studying online and the impact of different types of educational content presentation during a lecture on their emotions were studied. Using MorphCast, seven main emotions (Neural, Happy, Surprise, Sad, Angry, Fear, Disgust) representing the facial expressions of students during an online lecture were obtained, based on which a report on the level of attention and engagement was generated. The majority of students were ready to use modern AI-related technologies to collect information to help them in their studies and were willing to share their data with teachers. Verbal consent was obtained from the participants before the study began. Participants who did not want to participate were not included in this study. The pedagogical experiment on the implementation of facial emotion recognition (FER) of students lasted during the 2023–2024 academic year at the National University of Life and Environmental Sciences of Ukraine (NULES). To evaluate AI-based emotion recognition tools, 17 teachers and 39 students were surveyed on certain assessment indicators after a series of lecture classes. As a result of the pedagogical experiment, recommendations have been made that to improve the quality of online classes, it is worth introducing interactive teaching methods, and various forms of presenting different types of educational content during online lectures.