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The objective of this study was to program a school assistant based on artificial intelligence and integrated with surveillance cameras and loudspeakers in a university classroom. This was done to complement the supervision carried out by university teachers to identify student distractions, guarantee the proper use of masks, and evaluate student behaviors in class. The virtual assistant was developed using Python to generate audio warnings through a graphical interface built in the PyCharm environment. The results demonstrated the desired functionality of the virtual assistant, its ability to meet the requirements of a university classroom, and thereby the effectiveness of the YOLO v5 network andPyCharm used for the training, execution, construction, and implementation of the system.

This study aimed to identify the existing techniques, applications, equipment, and technologies applied for recognizing images with artificial vision via a systematic review of the literature during the period 2020-2022. PRISMA was used for selecting and analyzing 142 articles obtained from the EBSCO, Engineering Source, ProQuest, and ScienceDirect databases. Studies that were not directly related to the proposed objectives were not included, leaving 28 articles for full-text review. The review results strongly suggest that Hopfield-type convolutional artificial neural networks are highly effective for image recognition and classification tasks. Similarly, the combination of technological tools such as YOLO, Roboflow, Python, and OpenCV shows that image processing and deep learning are driving new applications that improve the various performance metrics of these tasks. Therefore, artificial vision, unlike technologies that incorporate electronic devices with sensors, allows the interpretation of an environment with a high degree of representation of reality, confirming its robustness in the complexity of data processing.