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Since its first applications, artificial intelligence has presented a vertiginous development, manifesting applications in multiple academic and business fields. In this article we will explore the risks to which the academic community, and especially students, are exposed to the growing wave of artificial intelligent systems, which have materialized through the use of both students and teachers. We will discuss how, through the bad behavior that the models currently present, the problems of interaction with these tools arise, supporting our hypothesis by means of an interaction that was carried out with one of the most popular existing programs among the student community: ChatGPT. This work concludes with the suggestion of three strategies that could be considered when implementing systems powered by artificial intelligence in classrooms, which seek to mitigate the risks presented throughout this paper, so that the pedagogical work of the teacher is optimized, while enhancing the learning process by students. [ABSTRACT FROM AUTHOR]

Tuberculosis is a respiratory disease that affects lungs and it is caused by the bacillus Mycobacterium tuberculosis (MTB), which is spread when people who are sick with tuberculosis expel bacteria into the air by coughing. Before the coronavirus (COVID-19) pandemic, tuberculosis was the leading cause of death from infectious agents even ranking above HIV/AIDS. Growing MTB on solid medium is the main diagnostic reference method. In addition, this is the standard method for identifying the bacterial resistance profile. However, the waiting period for the results is long, during which time a patient can be highly contagious. In turn, delaying treatment for tuberculosis can increase disease severity, but treating without diagnostic confirmation can lead to bacterial resistance, a higher rate of adverse events, and higher costs. Therefore, pulmonary tuberculosis disease must be rapidly diagnosed by a costeffective method. This paper proposes models for the detection of pulmonary tuberculosis by using different artificial intelligence techniques. These models can be used to support decision making of doctors and are intended to identify whether a patient has tuberculosis or not. In particular, four supervised learning techniques are used (neural networks, decision trees, and two ensemble methods). Each model allows predicting a positive or negative diagnosis of pulmonary tuberculosis based on previously recorded diagnostic variables taken from patients in Cali, Colombia. According to the results, the Extra Trees method reaches the highest accuracy compared to the other techniques used for the prediction of pulmonary tuberculosis with an area under the ROC curve of 95,63%. [ABSTRACT FROM AUTHOR]