Emotional state of the classroom through artificial intelligence to improve the teaching-learning process in a university (LACCEI, 2024)
The objective of this research was to develop an application that allowed facial recognition using artificial intelligence to identify the emotional state of students and thus improved the teaching-learning process within the virtual classroom at a university. The methodology used was a data science model based on convolutional neural networks that collected information from the students through facial biometric analysis using an application developed in Python where the different emotional states of the students were determined in real time during the sessions. virtual class. The results obtained show that through facial recognition various emotional states can be perceived during the class session of the students who must have a camera on, even if they have low image resolution, these results are shown to the teacher globally by each emotional state to determine the situation in your classroom and thus can improve your teaching-learning strategies. It is concluded that when the teacher identifies the emotional state of his students, he can improve his classes by motivating the students and this allows him to fulfill the competencies of the course, in the same way, it is concluded that the security of the information is maintained by destroying the images in real time of the participants once they have been processed and evaluated before being shown even to the teacher