In the contemporary era, the intersection of the Internet of Things and artificial intelligence revolutionizes how industries monitor and optimize their operations. In this work, we present a system that combines real-time monitoring provided by Internet of Things devices with predictive analytics based on artificial intelligence. This system detects anomalies in real-time and anticipates possible failures, allowing proactive interventions to maximize efficiency and minimize operating costs. Our findings reveal a significant improvement in the early detection of abnormal trends, as the system consistently identifies potential problems long before they become critical failures. Our evaluation employed data sets collected from controlled and industrial production environments, with more than 1 million records, including critical parameters such as temperature, humidity, and pressure. The results highlight a significant improvement in the early detection of abnormal trends, with a temperature detection accuracy of 98.7%, exceeding reference values and demonstrating the system’s effectiveness in preventing critical failures. The analysis also revealed previously unrecognized operational patterns, offering opportunities for industrial process optimization. This work highlights the effective integration of the Internet of Things and artificial intelligence to improve industrial monitoring, highlighting the tangible benefits of such integration, such as the adaptability and continuous learning of the system, ensuring its long-term effectiveness.

The rapid expansion of artificial intelligence poses significant challenges in terms of data security and privacy. This article proposes a comprehensive approach to develop a framework to address these issues. First, previous research on security and privacy in artificial intelligence is reviewed, highlighting the advances and existing limitations. Likewise, open research areas and gaps that require attention to improve current frameworks are identified. Regarding the development of the framework, data protection in artificial intelligence is addressed, explaining the importance of safeguarding the data used in artificial intelligence models and describing policies and practices to guarantee their security, as well as approaches to preserve the integrity of said data. In addition, the security of artificial intelligence is examined, analyzing the vulnerabilities and risks present in artificial intelligence systems and presenting examples of potential attacks and malicious manipulations, together with security frameworks to mitigate these risks. Similarly, the ethical and regulatory framework relevant to security and privacy in artificial intelligence is considered, offering an overview of existing regulations and guidelines.

Given the inherent diversity in learning styles and rhythms, the current educational landscape demands continuous adaptation toward methodologies that enhance individualized learning. This study addresses the effectiveness of learning personalization using machine learning models to adapt educational content to individual learning styles. Focusing our attention on a cohort of 450 university students, we implemented classification algorithms and neural networks to diagnose learning styles and personalize educational resources accordingly. The results are revealing: the students’ average grades experienced a significant increase, going from 70 to 75 points on a scale of 100 after the personalized intervention. Additionally, increased engagement was recorded, evidenced by more substantial interaction with educational materials tailored to their learning preferences. These findings suggest that personalization of learning is a powerful and effective tool that can improve both academic performance and students’ educational experience. This work confirms the relevance of educational personalization supported by artificial intelligence and provides a practical model for its effective implementation. The implications of this study are particularly pertinent to the evolution of pedagogical practices and curriculum design in the digital age.

Educational models currently integrate a variety of technologies and computer applications that seek to improve learning environments. With this objective, information technologies have increasingly adapted to assume the role of educational assistants that support the teacher, the students, and the areas enrolled in educational quality. One of the technologies that are gaining strength in the academic field is computer vision, which is used to monitor and identify the state of mind of students during the teaching of a subject. To do this, machine learning algorithms monitor student gestures and classify them to identify the emotions they convey in a teaching environment. These systems allow the evaluation of emotional aspects, based on two main elements, the first is the generation of an image database with the emotions generated in a learning environment such as interest, commitment, boredom, concentration, relaxation, and enthusiasm. The second is an emotion recognition system, through the recognition of facial gestures using non-invasive techniques. This work applies techniques for the recognition and processing of facial gestures and the classification of emotions focused on learning. This system helps the tutor in a modality of face-to-face education and allows him to evaluate emotional aspects and not only cognitive ones. This arises from the need to create a base of images focused on the spontaneous learning of emotions since most of the works reviewed focus on these acted-out emotions.

This work delves into the emerging field of the metaverse and its application in education, exploring how virtual and augmented reality technologies can transform teaching and learning methods. Focusing on aspects of educational effectiveness, security, privacy, accessibility, and inclusive design, the study provides a comprehensive analysis of the potential of the metaverse in the educational field. Through a methodology that combines quantitative and qualitative analyses, including ANOVA and Pearson’s correlation coefficient, we evaluate the impact of the metaverse on academic performance and student engagement. The results indicate significant improvements in knowledge retention, student engagement, and user satisfaction, evidencing the value of the metaverse as an immersive and engaging educational tool. The study also highlights the importance of security and privacy in virtual learning environments, with tests and audits confirming the robustness of the metaverse against various cyber threats. Additionally, particular emphasis is placed on accessibility and inclusive design, demonstrating how the metaverse can provide equal learning opportunities for all students, regardless of their abilities or background. The results show that the metaverse represents a promising opportunity to revolutionize education, offering a more interactive, personalized, and accessible environment

Currently, private universities, as a result of the pandemic that the world is facing, are going through very delicate moments in several areas, both academic and financial. Academically, there are learning problems and these are directly related to the dropout rate, which brings financial problems. Added to this are the economic problems caused by the pandemic, where the rates of students who want to access a private education have dropped considerably. For this reason, it is necessary for all private universities to have support to improve their student income and avoid cuts in budgets and resources. However, the academic part represents a great effort to fulfill their academic activities, which are the priority, with attention on those interested in pursuing a training programs. To solve these problems, it is important to integrate technologies such as Chatbots, which use artificial intelligence in such a way that tasks such as providing information on an academic courses are addressed by them, reducing the administrative burden and improving the user experience. At the same time, this encourages people to be a part of the college.