Conference Agenda

This article presents a preliminary diagnostic study aimed at designing a faculty evaluation and recognition model for engineering programs at five Ecuadorian higher education institutions, within the framework of the Erasmus+ EENTITLE project. The study is based on a review of the state of the art in faculty evaluation, academic recognition, and institutional models in higher education, as well as on the need to strengthen the quality of university teaching through more comprehensive systems capable of articulating evaluation, continuous improvement, professional development, and institutional recognition of faculty members.

Using a mixed-methods, exploratory-descriptive, cross-sectional approach, a structured questionnaire was administered in January 2024 to five Ecuadorian universities and polytechnic schools. The instrument examined eleven faculty evaluation and recognition mechanisms through closed-ended and open-ended questions, as well as rating scales, in order to identify their feasibility for implementation in the participating institutions.

The results show that the mechanisms with the greatest institutional alignment and priority are research support, professional development opportunities, international collaboration, academic leadership roles, and public recognition. Likewise, the most relevant internal factors for their implementation are faculty and university administration; among the external factors, cooperation with other higher education institutions and funding entities stands out; and among the necessary resources, institutional time and funding are particularly prominent.

Overall, the study provides an initial basis for building a shared, contextualized, sustainable, and potentially replicable model within Ecuador’s higher education system in engineering.

Abstract– This study examines the factors influencing the choice and retention of engineers as adjunct professors in a Dominican multicampus university (n=150). Using a cross-sectional quantitative design grounded in Self-Determination Theory (SDT), the Job Demands–Resources (JD-R) model, and organizational justice theory, validated composite indices were constructed for intrinsic and extrinsic motivation, positive and negative emotions, professional contribution, institutional satisfaction, and institutional difficulties. Results show that intrinsic motivation predominates over extrinsic (M=4.26 vs. 3.19; p<.001, r=0.776), and that negative emotions are the strongest predictor of turnover intent (ρ=+0.421, p<.001). Experiencing a critical breaking point multiplies the probability of not continuing fivefold (OR=4.86, p=.004). Institutional communication and collegial climate are the top-rated facilitators; pay equity and assignment stability are the main obstacles.

This paper presents an innovative simulation-supported engineering learning methodology in which finite element method (FEM) validation is embedded in a project-based learning environment for the design of a high-performance spoke-type permanent magnet synchronous motor (PMSM). Rather than treating simulation as a final verification step, the proposed teaching strategy positions FEM as the main epistemic bridge between conceptual design, electromagnetic modeling, material selection, and the interpretation of prototype discrepancies. The learning sequence was applied to a Formula Student-oriented spoke-type interior permanent magnet machine, reconstructed and validated from design targets before being used as a pedagogical platform for iterative inquiry. Students progressed from machine geometry reconstruction and back-EMF matching to saturation mapping, inductance extraction, torque constant estimation, and manufacturing-sensitivity analysis. This workflow leads to a technically defensible comparison of candidate magnetic materials and to a simulated explanation of the prototype underperformance. The results show that the spoke topology amplifies saturation effects, making material behavior and process quality immediately visible in the learning process. Premium cobalt-iron alloys achieved torque constants near 0.33 Nm/A and torques up to 18.9 Nm at rated current, whereas standard silicon steels exhibited a 10-15% torque reduction due to earlier saturation. A faulted model representing unannealed laminations and poor stacking factor reduced the torque constant to approximately 0.09 Nm/A, reproducing the observed discrepancy and reinforcing the instructional value of simulated validation. The paper argues that this methodology strengthens both deep technical understanding and engineering judgment by linking design decisions to physically interpretable numerical evidence.

This paper presents an innovative Challenge-Based Learning (CBL) project undertaken by undergraduate Mechanical/Manufacturing Engineering students from the REEdI (Rethinking Engineering Education in Ireland) program at Munster Technological University (MTU), Tralee. The project involved the fabrication, assembly, and programming of an ExoMy Mars Rover—a scaled-down, open-source robotic platform originally developed by the European Space Agency (ESA) as an educational analogue to the ExoMars rover.

Students were tasked with interpreting ESA’s technical documentation, 3D printing 40+ custom components, sourcing electronic hardware, and integrating mechanical and software systems to produce a fully functional rover. The project emphasised multidisciplinary skills, combining additive manufacturing, mechanical assembly, electronics integration, and Python-based programming for autonomous navigation. This hands-on approach aligned with CBL principles by engaging students in a real-world engineering challenge that required problem-solving, collaboration, and iterative design.

Feedback collected from the students indicated high levels of engagement and satisfaction. Students highlighted the tangible nature of the project, the opportunity to apply theoretical knowledge in a practical context, and the development of transferable skills such as teamwork and project management. Key lessons learned included the importance of early risk assessment for component tolerances, effective time management during iterative prototyping, and the need for clear documentation when adapting open-source designs.

The project demonstrates the innovative potential of CBL in engineering education - leveraging space exploration themes to inspire creativity and technical competence. It also underscores the value of integrating emerging technologies nto undergraduate curricula to prepare students for Industry 4.0.

In engineering education, the development of practical skills is essential to enable students to apply scientific and engineering principles to real-world problem solving. Throughout the curriculum, multiple learning experiences, such as laboratory work, workshops, and projects, aim to foster both technical and interpersonal skills. This article presents a Project-Based Learning (PBL) strategy integrated throughout the engineering curriculum, designed to strengthen students' technical skills (such as engineering design and product development) and their interpersonal skills in effective communication and teamwork. Three key experiences are articulated: the first-semester project at the STEAM challenge, the science projects developed between the second and fourth semesters and presented at the Science Fair, and the final-year engineering project, which is geared toward solving real-world problems in the productive or social sectors. These experiences provide progressive immersion in design, experimentation, validation, and technical communication, aligning with the ABET-established student outcomes. Longitudinal cohort analysis reveals a systematic evolution in design and teamwork skills as students progress through their academic journey. The results suggest that the strategic repetition of the project model throughout the curriculum functions as a spiral curriculum scheme that consolidates technical memory and engineering judgment.

The objective of this work was the design of a knowledge management system enhanced with generative artificial intelligence (GenAI-KMS) to promote formative research development in universities. The governance model was developed using Design Science Research (DSR), integrating knowledge management system standards (ISO 30401), the APQC® framework for knowledge management programs, and learning services outside formal education (ISO 29993:2017). Empirical findings from two student perception studies (n=30 for mobile app experience; n=30 for AI in education) were incorporated, evidencing high disposition toward emerging technologies (87% favorable perception for AI) and current usability limitations (40% report technical frustrations). The artifact presented, based on process management, integrates four AI modules: Intelligent Literature Assistant, Research Question Co-pilot, Adaptive Virtual Mentor, and Knowledge Asset Generator. Based on the study results, it is concluded that the model meets the DSR evaluation criteria at the design level and is framed within the use of good practices, offering a replicable path for institutions facing the challenge of scaling formative research.

This research focuses on prioritizing productive capacities that enable improved performance of agri-food SMEs in lagging areas of the Coquimbo Region of Chile. A face-to-face questionnaire was developed to gather preliminary information and characterize SMEs (n=56), the study used the AHP methodology to examine and prioritize criteria for improving performance in SMEs. The results indicate that the highest priority production capacity is the nutritional (physical-chemical) analysis of products, for the purposes of analysis, the products were identified by group: goat dairy products, dehydrated products, products such as honey and syrups, preserves and baked goods, analysis with their respective (p<0.05) according to Fisher's LSD test. In conclusion, it is important to continue supporting both the SMEs studied and the lagging territory, the AHP methodology is relevant for prioritizing production capacities, and having nutritional analysis of products allows for the development of nutritional labeling that enables compliance with regulations and entry into more informed consumer markets.