This study examines the impact of ChatGPT on narrative scriptwriting abilities and cognitive load in a sample of 41 master's students enrolled in a Digital Narratives course. Using a randomized experimental design, participants were divided into two groups: an experimental group (n = 20) that interacted with ChatGPT and a control group (n = 21) that did not. Our methods involved pre- and post-tests to assess changes in digital storytelling skills and cognitive load, as defined by intrinsic, extraneous, and germane load measures. The results indicated no significant improvement in digital storytelling skills for the experimental group compared to the control group, suggesting that the use of ChatGPT does not markedly enhance narrative writing abilities in the short term. However, a significant reduction in germane cognitive load was observed among the experimental group, pointing to ChatGPT's potential to facilitate the learning process by reducing the mental effort required for task integration and application. The study underscores the complexity of integrating AI into learning environments and highlights the need for strategic AI implementation tailored to specific educational objectives. It also points to the importance of longitudinal research to fully understand the long-term effects of AI on learning and cognitive development. Este estudio examina el impacto de ChatGPT en las habilidades de escritura de guiones narrativos y la carga cognitiva en una muestra de 41 estudiantes de maestría matriculados en un curso de Narrativas Digitales. Utilizando un diseño experimental aleatorio, los participantes se dividieron en dos grupos: un grupo experimental (n = 20) que interactuaba con ChatGPT y un grupo de control (n = 21) que no lo hacía. Nuestros métodos incluyeron pruebas previas y posteriores para evaluar los cambios en las habilidades de narración digital y la carga cognitiva, según lo definido por medidas de carga intrínsecas, extrañas y pertinentes. Los resultados no indicaron una mejora significativa en las habilidades de narración digital para el grupo experimental en comparación con el grupo de control, lo que sugiere que el uso de ChatGPT no mejora notablemente las habilidades de escritura narrativa a corto plazo. Sin embargo, se observó una reducción significativa en la carga cognitiva relevante entre el grupo experimental, lo que apunta al potencial de ChatGPT para facilitar el proceso de aprendizaje al reducir el esfuerzo mental requerido para la integración y aplicación de tareas. El estudio subraya la complejidad de integrar la IA en entornos de aprendizaje y destaca la necesidad de una implementación estratégica de la IA adaptada a objetivos educativos específicos. También señala la importancia de la investigación longitudinal para comprender plenamente los efectos a largo plazo de la IA en el aprendizaje y el desarrollo cognitivo.

This article addresses the implementation of pedagogical strategies based on the use of digital resources in emergency remote education (ERE) in Ecuador, in response to the Covid-19 pandemic. The research reveals that, despite increasing digitization, there is a notable lack of knowledge and familiarity with artificial intelligence (AI) among teachers, which limits its effective integration into the educational process. Forty-four percent of educators report occasional contact with AI, while 25% almost never inquire about the topic, evidencing a disconnect between interest in AI and teacher training in new technologies. The results indicate that teachers consider it essential to strengthen their digital competencies, as well as to improve the planning and evaluation of their classes. Although motivation and dynamics in the classroom are relevant, a greater focus on the integration of technology in teaching is required. In addition, prompts for the elaboration of academic reports and active learning activities are proposed, aimed at improving students' academic and socioemotional performance. In conclusion, the lack of adequate training in emerging technologies represents a significant barrier to the adoption of AI in education. The need for continuous training for teachers is emphasized, in order to enhance their ability to integrate digital tools in their pedagogical practices and thus improve the quality of education in the current context.

Unbalanced data can have an impact on the machine learning (ML) algorithms that build predictive models. This manuscript studies the influence of oversampling and undersampling strategies on the learning of the Bayesian classification models that predict the risk of suffering preeclampsia. Given the properties of our dataset, only the oversampling and undersampling methods that operate with numerical and categorical attributes will be taken into consideration. In particular, synthetic minority oversampling techniques for nominal and continuous data (SMOTE-NC), SMOTE—Encoded Nominal and Continuous (SMOTE-ENC), random oversampling examples (ROSE), random undersampling examples (UNDER), and random oversampling techniques (OVER) are considered. According to the results, when balancing the class in the training dataset, the accuracy percentages do not improve. However, in the test dataset, both positive and negative cases of preeclampsia were accurately classified by the models, which were built on a balanced training dataset. In contrast, models built on the imbalanced training dataset were not good at detecting positive cases of preeclampsia. We can conclude that while imbalanced training datasets can be addressed by using oversampling and undersampling techniques before building prediction models, an improvement in model accuracy is not always guaranteed. Despite this, the sensitivity and specificity percentages improve in binary classification problems in most cases, such as the one we are dealing with in this manuscript.

One of the most common causes of maternal deaths during pregnancy is preeclampsia. It is therefore important to analyze the resources needed to provide timely care and thus avoid health complications. One of those resources is hospital beds. Thus, to explore in-depth the inpatient beds occupation to treat preeclampsia at the IESS Los Ceibos Hospital in Guayaquil-Ecuador, Descriptive, Diagnostic, Predictive, and Prescriptive (DDPP) analytics will be applied together to the medical records collected retrospectively. In the descriptive analysis, the effects of the COVID-19 pandemic can be seen during the period March-September 2020. The diagnostic analysis helps us to show a consistent upward trend in the time series. In the predictive analysis, techniques such as Dynamic Bayesian Networks (DBN), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM), are considered to forecast the future inpatient beds occupation. The model trained with MLP achieved the lowest MAPE, which was 11.58%. It predicts that in September and November of 2024, there will be peaks of 274 and 258 inpatient beds, respectively. Finally, in our prescriptive analysis, we show that closing the hospitalization unit would produce, on average, 130.33 monthly referrals. To our knowledge, we do not find any work that applies all the DDPP analytics together in preeclampsia. Moreover, to our knowledge, there is no study that applies all the DDPP analytics together in univariate time series. Therefore, this work may serve as a basis for future studies in the joint application of all DDPP analytics to time series and to health.

The United Nations Educational, Scientific and Cultural Organization (UNESCO) Chair in the open educational movement for Latin America aims to promote scientific, technological, entrepreneurial, and innovative development. In Mexico, face-to-face bootcamps occur every two years, where prototypes of high-value solutions for education are developed using design and active learning methodologies, with the aim of scaling complex thinking and sustainable development. In the 2023 edition, 94 academicians from 12 countries participated (Argentina, Bolivia, Chile, Colombia, Ecuador, Germany, Guatemala, Mexico, Peru, Spain, the Dominican Republic, and the United Kingdom). Experts from Tecnológico de Monterrey, the Open University, the University of Leeds, and Siemens Stiftung supervised the process. This article presents the prototyped proposals and the results of applying the Play2Train4C methodology, enabling participants to develop educational innovation projects under the paradigm of complex thinking. The results indicate that (1) the participants managed to prototype ten proposals validated by experts and (2) their perceptions of their systemic, critical, scientific, and innovative thinking (sub-competencies of complex thinking) improved by the end of the bootcamp. The conclusion was that the UNESCO Chair boosted solution-building skills for the complex problems arising in Latin America and enabled key agents in education to improve their complex thinking skills for quality education, as promulgated by sustainable development goal 4.