Estado emocional del aula a través de inteligencia artificial para mejorar el proceso enseñanza-aprendizaje en una Universidad

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 improve 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 it was possible to perceive various emotional states during the class session of the students who are with the camera on, even if they have a low-resolution camera in their image quality, these results are shown to the teacher globally for each emotional state to determine the situation in their classroom and thus can improve their teaching-learning strategies. It is concluded that when the teacher identifies the emotional state of his students, he can improve his classes by motivating them 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 even before being shown in the final consolidated to the teacher.