This study focuses on the potential of generative artificial intelligence tools in education, particularly through the practical application of the 4PADAFE instructional design matrix. The objective was to evaluate how these tools, in combination with the matrix, can enhance education and improve the teaching–learning process. Through surveys conducted with teachers from the University of ESPE Armed Forces who participated in the MOOC course “Generative Artificial Intelligence Tools for Education: GPT Chat Techniques”, the study explores the impact of these tools on education. The findings reveal that generative artificial intelligence tools are crucial in developing massive MOOC virtual classrooms when integrated with an instructional design matrix. The results demonstrate the potential of generative artificial intelligence tools in university education. By utilizing these tools in conjunction with an instructional design matrix, educators can design and deliver personalized and enriching educational experiences. The devices offer opportunities to enhance the teaching–learning process and tailor educational materials to individual needs, ultimately preparing students for the demands of the 21st century. The study concludes that generative artificial intelligence tools have significant potential in education. They provide innovative ways to engage students, adapt content, and promote personalized learning. Implementing the 4PADAFE instructional design matrix further enhances the effectiveness and coherence of educational activities. By embracing these technological advancements, education can stay relevant and effectively meet the digital world’s challenges.

Outdoor air pollution costs millions of premature deaths annually, mostly due to anthropogenic fine particulate matter (or PM_2.5). Quito, the capital city of Ecuador, is no exception in exceeding the healthy levels of pollution. In addition to the impact of urbanization, motorization, and rapid population growth, particulate pollution is modulated by meteorological factors and geophysical characteristics, which complicate the implementation of the most advanced models of weather forecast. Thus, this paper proposes a machine learning approach based on six years of meteorological and pollution data analyses to predict the concentrations of PM_2.5 from wind (speed and direction) and precipitation levels. The results of the classification model show a high reliability in the classification of low (25 µg/m³) and low (<10 µg/m³) versus moderate (10–25 µg/m³) concentrations of PM_2.5. A regression analysis suggests a better prediction of PM_2.5 when the climatic conditions are getting more extreme (strong winds or high levels of precipitation). The high correlation between estimated and real data for a time series analysis during the wet season confirms this finding. The study demonstrates that the use of statistical models based on machine learning is relevant to predict PM_2.5 concentrations from meteorological data.

The large amount of text that is generated daily on the web through comments on social networks, blog posts and open-ended question surveys, among others, demonstrates that text data is used frequently, and therefore; its processing becomes a challenge for researchers. The topic modeling is one of the emerging techniques in text mining; it is based on the discovery of latent data and the search for relationships among text documents. In this paper, the objective of the research is to evaluate a generic methodology based on topic modeling and text network modeling, that allows researchers to gather valuable information from surveys that use open-ended questions. To achieve this, this methodology has been evaluated through the use of a case study in which the responses to a teacher self-assessment survey in an Ecuadorian university have been studied. The main contribution of the article is the inclusion of clustering algorithms in order to complement the results obtained when executing topic modeling. The proposed methodology is based on four phases: (a) Construction of a text database, (b) Text mining and topic modeling, (c) Topic network modeling and (d) The relevance of the identified topics. In previous works, it has been observed that the human interpretative contribution plays an important role in the process, especially in phases (a) and (d). For this reason, the visualization interfaces, such as graphs and dendograms, are of critical importance for researchers in order allow topic to efficiently analyze the results of the topic modeling. As a result of this case study, a compendium of the main strategies that teachers carry out in their classes with the aim of improving student retention is presented. In addition, the proposed methodology can be extended to the analysis of the unstructured textual information found in blogs, social networks, forums, etc.

The market for electric vehicles (EVs) has grown with each year, and EVs are considered to be a proper solution for the mitigation of urban pollution. So far, not much attention has been devoted to the use of EVs for public transportation, such as taxis and buses. However, a massive introduction of electric taxis (ETs) and electric buses (EBs) could generate issues in the grid. The challenges are different from those of private EVs, as their required load is much higher and the related time constraints must be considered with much more attention. These issues have begun to be studied within the last few years. This paper presents a review of the different approaches that have been proposed by various authors, to mitigate the impact of EBs and ETs on the future smart grid. Furthermore, some projects with regard to the integration of ETs and EBs around the world are presented. Some guidelines for future works are also proposed.

Nowadays, one of the learning resources in the educational area are serious games, also called training games; they are games designed with a different purpose than fun, whose main objective is to reinforce the new concepts more creatively. However, not all existing serious games are accessible in a way that allows access to a more significant number of users. Therefore, this research proposes to apply a combined method to evaluate accessibility in serious games, considering the Web Content Accessibility Guidelines (WCAG) 2.1. As a case study, we evaluated the accessibility of 82 serious games developed by Physical Education Technology Interactive Simulations at the University of Colorado. We propose to replicate this combined method for users with various types of disabilities, considering the various accessibility barriers. As future work, we suggest generating an accessibility heuristic evaluation focused on serious games, based on the accessibility issues identified. Finally, we believe it is essential to strengthen accessibility policies in each country, as well as implement best practices that generate innovation by incorporating diversity in building and designing more inclusive serious games.

The present work aims to carry out an analysis of the Amazon rain-forest deforestation, which can be analyzed from actual data and predicted by means of artificial intelligence algorithms. A hybrid machine learning model was implemented, using a dataset consisting of 760 Brazilian Amazon municipalities, with static data, namely geographical, forest, and watershed, among others, together with a time series data of annual deforestation area for the last 20 years (1999–2019). The designed learning model combines dense neural networks for the static variables and a recurrent Long Short Term Memory neural network for the temporal data. Many iterations were performed on augmented data, testing different configurations of the regression model, for adjusting the model hyper-parameters, and generating a battery of tests to obtain the optimal model, achieving a R-squared score of 87.82%. The final regression model predicts the increase in annual deforestation area (square kilometers), for a decade, from 2020 to 2030, predicting that deforestation will reach 1 million square kilometers by 2030, accounting for around 15% compared with the present 1%, of the between 5.5 and 6.7 millions of square kilometers of the rain-forest. The obtained results will help to understand the impact of man’s footprint on the Amazon rain-forest.