Conference Agenda

This study proposes a complementary education model for students of business administration careers. To this end, the gaps between learning outcomes and employers' requirements in an environment of digital transformation have been analyzed. For the operationalization of the proposal, a plan with three modules is proposed that include key activities and minimum contents. The relevance of the complementary education model is not only due to its contribution to the achievement of learning outcomes, but also its institutional reputation and contribution to international quality accreditation processes.

This study analyzes the relationship between satisfaction, intention to use, and current use of an Intelligent Tutoring System (ITS) through the empirical validation of the AIDET system, implemented at the Technological University of Santiago (UTESA), Dominican Republic. Using a structural model based on PLS-SEM, a sample of 280 university students was evaluated with the objective of confirming whether satisfaction directly influences system use, and whether this relationship is mediated by the intention to use. The results showed that satisfaction has a significant direct effect on the intention to use (R² = 0.793), and that intention to use, in turn, significantly and directly influences the current use of the system (R² = 0.598). However, satisfaction did not have a significant direct effect on current use, confirming the full mediation of the intention to use in this model. These findings contribute both to the theoretical framework of educational technology acceptance and to institutional practice, offering key guidelines for the design, implementation, and evaluation of ITS in automated higher education environments.

E-learning has emerged as a transformative paradigm in modern education, with substantial potential for integrating various advanced technologies. Among these, the Internet of Things (IoT) holds a particularly significant role in enhancing educational processes through real-time data collection, connectivity, and interactivity. This study conducts a bibliometric analysis of publications on e-learning and IoT from the Web of Science database, spanning 2002 to 2023, to map the evolution of research and identify key trends at the intersection of these fields. The study employs analytical techniques such as publication distribution, authorship and source analysis, keyword exploration, and strategic knowledge mapping. Results reveal a growing publication trend. Open-access journals of high quality and impact dominate the field, with leading contributions from countries like China and Saudi Arabia, which also exhibit strong research collaboration. Emerging themes, including smart learning and smart education, highlight the convergence of diverse technologies and interdisciplinary knowledge domains. Key trends focus on security, privacy, citizen science, licensing, and multi-technology integration and convergence. The findings suggest that future research should prioritize applied and experiential methodologies over purely theoretical frameworks, facilitating practical advancements in e-learning supported by IoT

This article explores how experiential learning, based on digital simulations and hands-on activities, can bridge educational gaps in engineering education, particularly in the field of supply chain management. A practical case study was implemented with Product Design Engineering students at Universidad EAFIT, combining manual vehicle assembly with simulations on the V-Logistics platform. The study aimed to enhance students' decision-making skills and understanding of logistical processes through an integrated approach that merges theoretical concepts with practical applications. Results indicate significant improvements in students' ability to make informed decisions, optimize resources, and manage supply chain operations effectively. Additionally, students reported a positive perception of this methodology, highlighting its effectiveness in preparing them for real-world challenges. The findings suggest that integrating experiential learning methods, such as simulations and hands-on activities, can significantly enhance engineering education by closing the gap between theory and practice

This study investigates the factors influencing load handling during fire truck preparation, where rapid response is essential. It focuses on the ergonomic and safety challenges firefighters face, such as lifting heavy equipment under time constraints, which impact both operational efficiency and occupational health, particularly musculoskeletal disorders. Despite existing ergonomic guidelines, practical gaps remain in integrating safety and efficiency in this high-pressure environment. Objectives: The research aims to evaluate current working conditions and propose interventions to improve safety and efficiency, reduce injury rates, and enhance operational readiness. Methodology: Using a mixed-methods approach, the study combines ergonomic assessments, surveys, and observational studies while reviewing safety regulations to develop solutions that mitigate fatigue and optimize operations. Expected Results: Implementing ergonomic measures is anticipated to lower occupational hazards, improve efficiency, and reduce injury rates during emergencies. Conclusion: The study emphasizes environmental sustainability by advocating for recycling practices within fire stations. It proposes a model integrating safety and environmental responsibility to reduce costs and create safer, more efficient work environments for firefighters and their communities.

According to recent research, Large Language Models (LLMs) perform well when processing input in the English language. Still, they struggle when processing input in other languages or inputs containing non-English syntax or symbols, such as different languages and programming queries. Therefore, this study evaluates whether programming queries, particularly code generation queries in Spanish, which is a widely spoken non-English language, present challenges similar to those in code generation tasks as compared to English queries. By doing this, this study attempts to find accuracy differences in the code generated by LLMs (Codex and Copilot) in English and Spanish input on a set of programming problems sourced from LeetCode.
The study compares the LLMs’ performance on various task complexities, including basic, medium, and advanced task complexities. The results show accuracy differences in the code generated by both LLMs; overall, Codex and Copilot perform better on English input than Spanish. Codex shows a significant decline in accuracy for Spanish inputs (85%) compared to English (92%), with higher error rates across syntax, runtime, and logical errors, particularly as task complexity increases. In contrast, Copilot exhibits better performance in generating error-free code on multilingual tasks, achieving consistently high accuracy across English (96%, 92%, 90%) and Spanish queries (90%, 86%, 83%), with smaller performance gaps (-6%) and reduced error rates. These findings highlight Copilot's superior adaptability and reliability in handling multilingual programming tasks compared to Codex. These results highlight the need for improvement in multilingual capabilities and language-dependent limits of LLMs.

This study presents the results of a pilot implementation of “NOMBRE DEL STI,” an intelligent tutoring system designed for higher education. Involving 500 students, the findings highlight the platform’s ability to personalize teaching and learning processes by providing immediate feedback and dynamically adjusting activity difficulty. Quantitative and qualitative analyses indicate notable increases in motivation, autonomy, and user engagement. Furthermore, ongoing interaction with the system fostered a flexible pedagogical approach grounded in theoretical principles and adaptive artificial intelligence resources. The results suggest that “NOMBRE DEL STI” stimulates educational innovation and promotes meaningful knowledge construction, offering fresh perspectives for integrating technology into university contexts.