Conference Agenda

Abstract—The integration of peer mentoring into engineering academic programs as a mechanism to support students facing academic difficulties has received considerable attention in higher education worldwide, particularly in developed countries. This study employed a mixed-methods review approach to examine the benefits of peer mentoring through a program implemented by high-achieving engineering students. The study's findings underscore the significance of implementing peer mentoring as a support framework for students grappling with critical challenges, owing to its manifold advantages. Furthermore, it emphasizes the necessity of ascertaining the diverse factors that contribute to the efficacy of peer mentoring programs.

The Peer Mentoring of Excellence (PME) Program has demonstrated a consistent track record of maintaining satisfaction rates in excess of 90%. The study's findings indicate that collaborative learning with peers fosters an environment conducive to learning, thereby enhancing students' knowledge and skills. In a similar vein, students who act as mentors have attested to the development of leadership, communication, and assertiveness skills that has resulted from their engagement in the program. Furthermore, these students have noted that their participation serves to reinforce the knowledge they have acquired.

This study proposes a pedagogical model aimed at strengthening the relationship between academic training and employability in technical-professional education. Based on a university case study conducted between 2020 and 2025, the research analyzes the association between training processes and graduate labor market insertion through a quantitative, non-experimental design. Descriptive and inferential statistical analyses, including chi-square tests, ANOVA, and logistic regression, were applied to identify differences across academic programs and to determine the predictive effect of composite employability scores on employment status. The findings reveal that employability outcomes are not solely determined by technical competencies, but rather by the degree of pedagogical articulation between curriculum design, industry linkage, competency certification, and graduate follow-up mechanisms. Significant variations across programs confirm that institutional coherence and structured collaboration with the productive sector play a decisive role in employment outcomes. In response to these findings, the MEFEP Pedagogical Model is proposed as an integrative framework that aligns competency-based training, experiential learning, industry engagement, and continuous evaluation mechanisms to enhance graduate employability and institutional relevance.

The study analyzed the use of the Model Canvas adapted to scientific research as a pedagogical strategy in higher education. A quantitative approach with a quasi-experimental design was employed, comparing two intact groups of students from administration and marketing programs (N = 40). The experimental group integrated the Model Canvas into the development of the academic research product, while the comparison group followed the traditional methodology. Outcomes were assessed using rubrics that considered product quality, coherence and planning of the research process, and student autonomy and self-regulation. The analysis included descriptive statistics, the Mann–Whitney U test, effect size estimation (Glass’s delta), and a complementary Bayesian analysis. The findings revealed significant differences in favor of the experimental group in product quality and coherence and planning, while no conclusive differences were observed in autonomy. It is concluded that the Model Canvas constitutes an effective pedagogical strategy for improving the structure and quality of research products in higher education.

Abstract: Given the rapid adoption of Generative Artificial Intelligence (GAI) in higher education, it poses significant risks and opportunities. This study analyzes GAI tools among master's students in Project Management at a university in Honduras. Using an expert-validated instrument and a quantitative approach applied to a sample of 70 students, the benefits, perceived risks, patterns of use, and ethical considerations were explored. The results show almost universal use (95.71%), with frequent weekly use and tools such as ChatGPT, Copilot, and Gemini being the most widely used. Students appreciate GAI primarily for its potential to understand complex concepts and explore new ideas. They also expressed high ethical awareness and low training in the subject. This provides evidence of the urgent need for clear institutional policies coupled with digital literacy programs in GAI.

The objective of this study was to identify the relationship between the use of Artificial Intelligence tools by teachers pursuing a master’s degree in Basic Education at the Bolivarian University of Ecuador and their academic performance in learning activities in contact with the teacher, autonomous and practical-experimental. A quantitative methodology with cross-sectional design and correlational scope was applied, using structured questionnaires to 314 teachers. The results showed a frequent use of tools such as ChatGPT, Grammarly and DeepL, and a significant positive correlation between their use and academic performance in the different types of learning. It is concluded that the autonomous and diversified integration of Artificial Intelligence strengthens the formative process in graduate distance education, consolidating itself as a key pedagogical resource.

The integration of artificial intelligence (AI) into higher education presents a strategic opportunity to transform teaching and learning processes in engineering, particularly in Latin American contexts facing structural challenges related to equity, student retention, and pedagogical innovation. This paper presents a mixed-methods study conducted at the National Technological University, Delta Regional Faculty (UTN–FRD), aimed at analyzing the impact of AI tools on engineering student learning, explicitly integrating a gender perspective and an ethical approach.

The study examines the implementation of intelligent tutors, content recommendation systems, learning analytics, and adaptive assessments in selected courses from the basic and advanced cycles. Variables related to academic performance, motivation, self-regulated learning, and perceived equity are analyzed. The expected results aim to provide empirical evidence to support the responsible integration of AI as a driver of educational innovation and as a tool for reducing gender gaps.