Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

In the current context, Virtual Assistants have emerged as effective solutions to facilitate learning, especially for evening students who must balance work and study. This research is justified by the growing need to support non-traditional education, addressing the challenges these students face in managing time and accessing educational resources, which negatively affects their academic performance. A systematic review was conducted using the SALSA framework, analyzing studies published between 2020 and 2024 in databases such as WoS, Scopus, Dialnet, and SciELO. The findings indicate that they significantly improve time management and access to resources by providing assistance tailored to individual needs. However, challenges were identified, such as a lack of technological training among teachers and resistance to change. Despite the progress made, it is essential to address these obstacles to maximize the educational potential of these applications, strengthening their implementation in educational environments.

The Special Section of this issue of the Latin American Journal of Educational Studies, coordinated by Dr. Jimena Hernández Fernández, responds to the need to consolidate knowledge and foster rigorous academic dialogue about upper secondary education (USE), a strategic level for regional development. The compiled articles, selected for their methodological rigor and substantive contribution, offer a multidimensional perspective on USE complexities: from public policies and their fluctuations, to access and retention experiences of vulnerable students, including the relevance of job training and challenges in educational quality. This volume seeks to enrich academic debate and provide solid empirical foundations for formulating more stable, equitable and effective educational policies. It aspires that the reflections will promote new research lines and strengthen collaboration among researchers, policymakers and educational actors, building a more promising future for USE in Mexico and the region.

This is a systematization of teaching experience following the delivery of professional practice courses with two successive groups at a Higher Normal School. It focuses on authentic literacy in three moments of the practices: 1) the research and prior preparation that student teachers do on the topics assigned to them, 2) the selection of texts and materials they bring to the classroom to work with their students, and 3) the reading and writing activities they implement in classrooms. To carry out the systematization, the materials from the practice course were reviewed, including lesson plans developed by the student teachers, observations and feedback on their practice by the professor, and their reflection texts. From the systematization, learnings emerge to strengthen teacher education through authentic literacies. 

This study analyzes the job training proposal of the Comprehensive Community High School (Bachillerato Integral Comunitario - BIC). It examines the experience of BIC graduates from Mazatlán Villa de Flores, Oaxaca, which promotes three career orientations: indigenous language interpreter, community development, and community health promotion. The methodology guiding the analysis is a traditional and virtual ethnographic approach, with in-depth interviews establishing a dialogue with research background. The results indicate that no graduate mentions dedicating themselves to any of these specialties, since the local job market is homogeneous, centered on informal subsistence agricultural work through the use of traditional technology. The conclusions highlight the importance of conducting a systematic reflection on job training that is efficient and relevant; particularly, in intercultural high schools, a reformulation is necessary that considers the new social processes experienced by indigenous communities.

In recent decades, digital coexistence has become a relevant topic for secondary education. This study addresses its teaching in social sciences, using a qualitative methodology and a multiple case design. Didactic sequences were implemented in three courses, focused on the co-construction of knowledge between teachers and students. The results show a lack of clear definitions about digital coexistence in educational programs. Three key dimensions were identified: social media experiences, problem identification, and normalization of violence. It is concluded that teaching should be a co-construction process, promoting dialogue and critical reflection to form responsible and critical citizens in digital environments.

Pediatric diabetes research is often constrained by data scarcity, hindering the development of accurate predictive models for clinical applications. This study addresses this limitation by evaluating the effectiveness of Generative Adversarial Networks (GANs) and Wasserstein GANs (WGANs) in generating synthetic datasets that replicate the statistical properties of real pediatric diabetes data. A structured methodology was applied, incorporating preprocessing, model design, and dual evaluation metrics: Jensen-Shannon and Kullback-Leibler divergences for statistical fidelity, and a classification model to assess practical utility. Results demonstrate that both models produce high-fidelity synthetic datasets, with WGANs showing superior performance in capturing complex patterns due to improved training stability. Nonetheless, challenges remain in replicating the inherent variability of pediatric data, influenced by growth and developmental factors. This work highlights the potential of synthetic data to augment pediatric diabetes datasets, facilitating the development of robust and generalizable predictive models. Limitations include the dependency on initial data quality and the specificity of the models to pediatric datasets. By addressing critical gaps in data availability, this study contributes to advancing AI-driven healthcare solutions in pediatric diabetes research.

Proprioceptive training encompasses interventions aimed at enhancing proprioceptive function to improve motor function performance. Three types of interventions are considered: Movement Training (MT); Somatosensory Stimulation Training (SST), and Force Reproduction Training (FRT). This study analyzes the potential of emerging technologies, such as exoskeletons, mechanical devices, Artificial Intelligence (AI), Virtual Reality (VR), the Internet of Things (IoT), and sensors, highlighting their application in proprioceptive therapies, with particular emphasis on MT, SST, and FRT. A total of 107 articles published in scientific journals were reviewed, of which 30 complied with inclusion criteria: 1) Implementation of proprioceptive intervention therapy; 2) use of technology; 3) publication after 2019, and 4) written in the English language. Of the studies analyzed, 43 % employed AI, indicating its increasing adoption, while IoT was the least utilized technology, with only 3 %. It is concluded that emerging technologies plays a crucial role in proprioceptive rehabilitation by enabling the analysis of data before and after surgical procedures, real-time pattern assessment, and the classification of sensory signals. Moreover, it offers alternatives to traditional measurement methods.

In medical imaging for Pap smear tests, accurately identifying regions of interest, such as the nucleus and cytoplasm, remains a critical challenge due to the complex morphology and overlapping structures in cervical cell images. This complexity increases the risk of misidentification, potentially leading to false positives in computer-assisted diagnosis. To address this issue, this study introduces a novel approach by developing and evaluating a framework for the precise segmentation of nuclei and cytoplasm in cervical cell images using a cGAN-based model, Pix2Pix, applied to a dataset validated by specialists. The generated images are compared with target images, converted to binary, and an AND operation is performed to evaluate pixel overlap in the areas of interest. The evaluation metrics highlight a segmentation accuracy of 88.8 % and sensitivity of 89.62 % for nuclei, while for cytoplasm, precision reached 89.62 % and sensitivity 99.34 %. The Jaccard indices were 80.89 % for nuclei and 96.71 % for cytoplasm. These results demonstrate the effectiveness of the model in segmenting nuclei and cytoplasm in cervical cells.

The aim of this work is to analyze the usage of the boundary element method (BEM) as a fast computational tool for solving large ultrasonic field problems, i.e. 3D models. A proposed tridimensional radiating surface SR was modeled by means of BEM and the finite element method (FEM).  Four time-harmonics models were developed: two containing the entire SR and two considering a symmetrical plane at half-length of the radiator. BEM solutions were validated with FEM models by contours at -3 dB and -6 dB pressure decays, areas within the contours, elliptical shape ratio Er and ellipsoidal focal volume approximations. The average differences in pressure and distance at the focus were 39.875 Pa and 0.4515 mm, respectively; the areas within the contours show differences between 0.6 mm2 and 2.3 mm2. The Er of the focal zone was over 92 %, while the ellipsoidal volume approximation showed differences between 0.0817 mm3 to 1.4632 mm3 at -3 dB, and 1.2354 mm3 to 4.1144 mm3 at -6 dB. Analyzed data suggest the use of BEM to model the ultrasonic beam pattern in a lossless medium during ultrasonic biomedical applicators design, reducing the solution time from 22 h with FEM to 2 min with BEM.

Phlebotomy is a procedure to obtain blood samples, mainly for laboratory clinical analysis. The amount of blood, tube identification, and the use of the appropriate tube are characteristics that the health professional visually inspects. Being a manual activity, the possibility of error is latent and can affect quality, workflow, and efficiency. Despite the advancement of industry 4.0 technologies, including artificial intelligence (AI), there is little evidence of applications in clinical laboratories. This study aims to evaluate the suitability of using deep learning (DL) in inspecting tubes with blood samples. Specifically, three architectures, YOLOv5, YOLOv7, and YOLOv8, are tested to detect six classes, including cap color and the presence of labels. The highest precision performance was presented by the YOLOv8 model, obtaining a precision of 0.927 in detection, which shows a high capacity to inspect important characteristics in the phlebotomy service. Therefore, being DL is a suitable alternative to assist health professionals in inspection activities. Future work includes expanding the number of images in a balanced manner.