Currently, the integration of technologies such as the Internet of Things and big data seeks to cover the needs of an increasingly demanding society that consumes more resources. The massification of these technologies fosters the transformation of cities into smart cities. Smart cities improve the comfort of people in areas such as security, mobility, energy consumption and so forth. However, this transformation requires a high investment in both socioeconomic and technical resources. To make the most of the resources, it is important to make prototypes capable of simulating urban environments and for the results to set the standard for implementation in real environments. The search for an environment that represents the socioeconomic organization of a city led us to consider universities as a perfect environment for small-scale testing. The proposal integrates these technologies in a traditional university campus, mainly through the acquisition of data through the Internet of Things, the centralization of data in proprietary infrastructure and the use of big data for the management and analysis of data. The mechanisms of distributed and multilevel analysis proposed here could be a powerful starting point to find a reliable and efficient solution for the implementation of an intelligent environment based on sustainability.

The events that took place in the year 2020 have shown us that society is still fragile and that it is exposed to events that rapidly change the paradigms that govern it. This has been shown by a pandemic like Coronavirus disease 2019; this global emergency has changed the way people interact, communicate, study, or work. In short, the way in which society carries out all activities has changed. This includes education, which has bet on the use of information and communication technologies to reach students. An example of the aforementioned is the use of learning management systems, which have become ideal environments for resource management and the development of activities. This work proposes the integration of technologies, such as artificial intelligence and data analysis, with learning management systems in order to improve learning. This objective is outlined in a new normality that seeks robust educational models, where certain activities are carried out in an online mode, surrounded by technologies that allow students to have virtual assistants to guide them in their learning.

Traditional teaching based on masterclasses or techniques where the student develops a passive role has proven to be inefficient methods in the learning process. The use of technology in universities helps to generate active learning where the student’s interest improves making him the main actor in his education. However, implementing an environment where active learning takes place requires a great deal of effort given the number of variables involved in this objective. To identify these variables, it is necessary to analyze the data generated by the students in search of patterns that allow them to be classified according to their needs. Once these needs are identified, it is possible to make decisions that contribute to the learning of each student; for this, the use of artificial intelligence is considered. These techniques emulate the processes of human thought using structures that contain knowledge and experience of human experts.

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.

Teaching Science, Technology, Engineering, and Mathematics (STEM) disciplines faces significant challenges due to the increasing complexity of content and the diversity in students’ knowledge levels. Traditional teaching methods, characterized by their standardized approach, fail to adapt to individual needs, limiting learning potential. This problem is particularly evident in mathematics, physics, and programming, where mastering critical concepts requires a logical and sequential progression. This study proposes an artificial intelligence-based intelligent tutoring system to provide a real-time personalized and adaptive learning experience. The system integrates advanced deep learning and natural language processing models, allowing for targeted feedback and dynamic adjustment of learning trajectories. The results show significant improvements in several key metrics. The experimental group achieved an average precision of 85% in programming and 78% in mathematics, significantly outperforming the control group. Furthermore, a linear regression model identified a positive correlation between the time of interaction with the system and the rate of progress in fundamental concepts. Student perceptions were also highly positive, with 80% appreciating the usefulness of adaptive feedback. These findings identify the system’s potential to transform STEM teaching, address the lack of personalization, and improve learning in various educational settings.