Conference Agenda

This study investigated the relationship between the use of e-rubrics as a formative assessment strategy and the level of satisfaction of university students at a public university during 2024. Under a quantitative approach and correlational design, data were collected from a representative sample of 44 students through surveys based on a Likert scale, evaluating their satisfaction at three key moments of the course: beginning, development, and closure. The findings revealed positive and significant correlations at each stage, highlighting that the continuous application of the e-rubric significantly increases student satisfaction, especially in the final phase of the course. This instrument not only promotes objective assessment but also enhances the learning experience by facilitating planning and reflection on the achievements attained. It is concluded that the incorporation of e-rubrics from the beginning of the course, along with constant feedback, is essential to optimize the educational process and strengthen educational quality.

This research analyses the technological advances in flexible pavement assessment methods, focusing especially on three key indicators: the Pavement Condition Index (PCI), the International Roughness Index (IRI) and the Pavement Serviceability Index (PSI). The main objective of this research was to examine how new technologies transform road infrastructure management and improve the accuracy and efficiency in the assessment of pavement condition. The research is bibliographical and documentary with a deductive method, qualitative approach and applied orientation. It is descriptive and explanatory, with a non-experimental, transversal and retrospective design. For data collection, the documentary review technique was used and the instrument was retrospective based on a review of scientific articles published in the Scopus database between 2019 and 2025. The results obtained show that the incorporation of techniques such as machine learning, neural networks and specialized algorithms allow for a significant improvement in the accuracy of the prediction of these indices, surpassing traditional methodologies. In addition, the use of technologies such as computer vision and drones transform pavement inspection and allow for faster, more precise and automated analysis. The use of smart sensors and Internet of Things (IoT) technologies facilitate continuous and real-time monitoring of road infrastructure, optimizing resources for its maintenance and contributing to extending the useful life of pavements.

This systematic investigation aims to analyze the influence of human resource management on the recruitment process and identify strategies to improve its quality and efficiency. A systematic review of scientific literature published between 2019 and 2024 in the Scopus database was conducted. Fifteen relevant studies meeting the eligibility criteria were identified. The results show that the participation of the HR department is crucial in all stages of recruitment, from defining the job profile to the final candidate selection. This ensures the hiring of the most suitable personnel, translating into better performance and higher productivity for the organization. Additionally, the reviewed studies identified that business sectors implementing HR have shown improvements in both internal and external aspects. Indicators reflecting this correlation include talent management and organizational effectiveness, process automation and innovation, and KPI development. The most addressed topics in the articles were candidate evaluation for productivity, the impact of technology on HR, and effective recruitment strategies. In conclusion, this study demonstrates the importance of effective human resource management for successful recruitment, impacting organizational performance and competitiveness.

Student dropout in Colombian higher education is a complex problem that affects educational quality and the socioeconomic development of the country. This research focused on the development and implementation of a web platform to analyze and predict student dropout in a denominational higher education institution in Colombia, using a logistic regression model. The research was developed in four phases: 1) literature review and expert consultation to identify input variables related to dropout, 2) requirements definition, system architecture design and technology selection, 3) project development using Kanban methodology, and 4) implementation of the solution, execution of a pilot test and satisfaction evaluation through a survey. The predictive algorithm, with an accuracy of over 90%, allows the institution's professionals to detect early on students at risk of dropping out. The web platform, developed using Python provides an intuitive interface to visualize the current dropout status of faculties and programs, predict student dropout risk and understand the influencing factors. Validation through a pilot test and a satisfaction survey confirmed the effectiveness and usability of the platform, although areas of improvement for future implementations were also identified.

The use of artificial intelligence (AI) in higher education is constantly growing, especially in the teaching of sciences. This study examines its impact on university education by identifying its applications, ethical challenges, benefits, and regulatory frameworks. To achieve this, a systematic review of scientific literature was conducted.
Findings indicate that faculty members and researchers recognize the potential of AI in the learning process; however, they acknowledge their limited proficiency in using these technologies and emphasize the need for continuous training in AI applications. On the other hand, students extensively rely on AI tools for their academic activities, which may negatively impact the development of analytical reasoning and computational skills.
In disciplines such as differential equations, fluid mechanics, geometry, and trigonometry, the most commonly used AI tools include ChatGPT, Bard, Gemini, Copilot, and You. Regarding mathematical computations, error rates in AI-generated responses range from approximately 30% to 70%, depending on problem complexity, raising concerns about their reliability.
In this context, universities face the challenge of establishing regulatory frameworks that incorporate ethical principles, information security, and data governance for AI usage in the educational sector.

The recent article addresses the study of the optimization in the retention of human talent in commercial companies, this being notable in these companies, and is guided by their growth, facilitating adaptation and survival. The objective is to explore empirical studies on the retention of human talent in commercial companies between the years 2019 to 2024. The methodology used to carry out the systematic review used six sources of information, the data banks mentioned below, the rules for selecting the articles were made using Boolean equations, covering the words: talent retention, worker retention, talent management, staff resignation, retention strategies, the criteria used were of inclusion and exclusion for the search of said data. During the search, points were filtered such as year of publication, not related to the health sector, among others, theses, books that are not part of the search, among others. The result showed different strategies such as professionais development programs and within them incentive plans such as the inclusive organizational climate, however, there are still points to investigate and execute for the development of this study.

The objective of this research was to compare the level of job burnout of the leaders of manufacturing companies in Cortes, Honduras according to their personality style. Therefore, it was carried out using a quantitative approach with a descriptive-correlational scope, a non-experimental-cross-sectional design, applying a survey to which two instruments were integrated: Maslach's Burnout Inventory and the DISC test of leadership styles. According to the results, it was shown that Burnout syndrome affects managerial profiles presenting a medium level in characteristics and symptomatology (54% of the sample) and 38% present a high level, proving to be a health risk. As for the leadership profile according to the DISC dimensions, managers showed on average greater characteristics linked to the dominant and controlling style; on the other hand, the style of strength and influence reflect a lower level in the behaviors expressed. When assessing the relationships between Burnout and leadership styles, it was shown that the dimensions of the dominant and solid personality show a statistically significant association with the prevalence of job burnout (r=0.628 and r=-0.432 respectively). This reflects that managers whose behavioral characteristics are linked to a greater need to direct, control, take risks and less need to serve, being optimistic, calm and patient will be more likely to manifest symptoms linked to Burnout.