Conference Agenda

Food service establishments operate in environments where demand variability, ingredient availability, and delivery speed directly impact operational performance. In this context, a diagnostic assessment conducted in a pizza restaurant revealed many important inefficiencies such as inventory inaccuracy because of insufficient supplies; inefficient mise in place due to long time to find supplies; and unproductive times caused by inadequate control in the process and poorly optimized layout. To address these main issues, this study implements an integrated model that uses a Random Forest algorithm to forecast weekly demand with high accuracy and synchronizes purchasing and production using an EOQ-MRP system with Kanban as a visual control mechanism, in addition of standard work and station redesign to reduce preparation time. The proposed model generates highly accurate demand predictions, enabling more efficient inventory planning and improved coordination between purchasing and preparation activities. Hybrid validation shows that process compliance increases from 79% to 97% after implementation. These findings show indeed that combining Machine Learning forecasting with Lean Service tools significantly improves synchronization between all the processes and stabilizes ingredient supply, enhancing operational performance in foodservice environments. These results open the path for future research integrating predictive analytics with Lean Service methodologies to address operational variability in dynamic foodservice settings.

The rapid expansion of online learning during the COVID-19 pandemic led universities worldwide to adopt virtual teaching modalities. While many institutions returned to face-to-face instruction, several programs retained virtual components as a strategy to address large enrollment courses and promote flexible learning environments. This study presents the evaluation of a virtual teaching hour implemented in large theoretical engineering courses at the Universidad de La Frontera (Chile) as part of a post-pandemic innovation strategy.

The virtual sessions were designed using the ADDIE instructional design framework (Analysis, Design, Development, Implementation, and Evaluation), a widely used model for developing structured and effective learning experiences. As part of the institutional curriculum innovation process in the Civil Engineering programs, these virtual sessions were aligned with the development of key competencies such as innovation, engineering design, and social responsibility within the Integrated Engineering and Science Education Track (LIFIC).

To ensure educational quality, a validated evaluation instrument was applied to systematically monitor students’ perceptions of the virtual teaching hour. Data were collected during the 2025 academic year, with 944 student responses in the first semester and 560 in the second semester, totaling 1,504 responses. The results provide evidence regarding student engagement, perceived effectiveness of the virtual sessions, and the role of instructional design in supporting learning in large enrollment courses.

The findings highlight the relevance of structured instructional design and systematic quality monitoring in sustaining hybrid learning strategies in engineering education. The study contributes empirical evidence for institutions seeking scalable and quality-assured solutions for teaching in mass engineering courses.

Industrial digitalization requires highly skilled personnel in Computer Numerical Control (CNC) to address the challenges of precision and automation associated with Industry 4.0. The purpose of this study was to evaluate the impact of a training intervention focused on CNC in order to strengthen the professional competencies of metal mechanics in Guayaquil and Caracas. Methodologically, a quantitative approach with a quasi-experimental design was applied under the ADDIE instructional model. The program was delivered in a hybrid modality over 100 academic hours with 109 participants. The dimensions assessed included technical knowledge, design/simulation, and practical application, using a Likert-type questionnaire. The main results showed statistically significant differences (p < .001) across all dimensions after the intervention. When evaluated by the trainers, the experimental group reached “strongly agree” levels in technical mastery, whereas the control group maintained ratings of disagreement from the trainers. It is concluded that the program achieved its objective, demonstrating that it is an effective tool for standardizing competencies and improving professional performance in the industrial sector

Intermediate cities in Latin America face structural mobility challenges linked to socio-spatial inequality, informal transportation systems, climate vulnerability, and infrastructure deficits. Although Artificial Intelligence (AI) has demonstrated significant potential for optimizing sustainable urban systems, its structured integration into higher education curricula in engineering and urban planning remains limited. This paper presents the conceptual design of the AI-MUS model (Artificial Intelligence – Mobility Urban Sustainability), a pedagogical architecture integrating data science, participatory data collection, predictive modeling, spatial segmentation, generative optimization, and climate resilience modeling under a Latin American contextual framework. A Design-Based Research approach is adopted in its conceptual phase, structuring a replicable methodological framework prepared for future quasi-experimental validation. The model addresses the need for adapted indicators for intermediate Latin American cities and strengthens data literacy for sustainable urban mobility decision-making.

This study analyzes the level of digital teaching skills among academics at an engineering faculty at a regional university in Chile, using the European DigCompEdu framework. The research was applied to a purposive sample of 18. A structured questionnaire with
40 items (34 associated with DigCompEdu, 5 with emerging technologies, and 1 open-ended item for critical reflection) was used, validated by expert judgment. The results show a generally high-intermediate level of competence, with strengths in professional commitment and use of
digital resources, while the areas of student empowerment and facilitation of digital competence show lower levels of development. It is concluded that, although teachers show a willingness to innovate pedagogically, differentiated training programs and institutional policies are needed to promote a sustained digital culture in higher education.

Basic concepts in introductory mathematics remain a persistent challenge in STEM education, particularly for students entering with heterogeneous levels of prior knowledge. Among technology-enhanced approaches, gamified learning environments and adaptive learning systems have shown promise; however, their differential effectiveness as a function of students’ initial proficiency is not yet well understood. This study examines the impact of these two instructional strategies through a quasi-experimental pre-test–post-test design using intact classroom groups in introductory mathematics courses. A gamified learning platform and an adaptive learning platform were implemented over comparable instructional periods. Learning outcomes were assessed using normalized learning gain, while engagement-related behavior was examined through a normalized dedication metric. Statistical analyses included chi-square tests to examine pre-post performance transitions and one-way ANOVA to evaluate the effect of initial proficiency on learning gain within each instructional condition. Results indicate that both approaches led to improvements in post-test performance; however, distinct learning gain profiles emerged across proficiency levels. Gamified instruction was associated with higher gains among students with low initial proficiency, whereas adaptive learning produced more homogeneous gains across performance groups. Interpreted through an aptitude–treatment interaction framework, these findings suggest that gamified and adaptive learning approaches may serve complementary roles when aligned with learner characteristics. The study contributes empirical evidence to inform the design of personalized instructional strategies in introductory mathematics and motivates future research on integrated, aptitude-sensitive learning models.

Unequal access to physical laboratories in engineering education continues to limit equity, experimental repeatability, and students’ engagement with real-world sustainability challenges, particularly in resource-constrained contexts. This study presents a quasi-experimental evaluation of AI assisted virtual laboratories with intelligent feedback as a pedagogical strategy to support Engineering without Borders principles in higher education. The intervention was implemented in a Physical Experimentation and Statistical Thinking course through an authentic challenge focused on designing a sustainable agricultural irrigation system using a Mariotte Bottle. A mixed-methods quasi-experimental design was conducted with 93 undergraduate engineering students, divided into a control group (n = 47) using traditional physical experimentation and an experimental group (n = 46) combining physical practice with AI assisted virtual laboratories. Data were collected using pre- and post-tests, validated Likert-type surveys (α > 0.85), standardized rubrics, and qualitative reflections. Statistical analyses included independent-samples t-tests, one-way ANOVA, correlation analysis, effect sizes, and power analysis (> 0.80). Results revealed significantly higher conceptual learning gains, motivation, technological acceptance, and problem-solving consistency in the experimental group. The normalized learning gain reached g = 0.67 compared to g = 0.34 in the control group (p < 0.05). Qualitative findings showed deeper integration of the Sustainable Development Goals (SDGs), particularly SDGs 2, 4, 6, 9, 12, and 13. The findings indicate that AI assisted virtual laboratories can complement physical experimentation, offering an ethically grounded instructional approach with potential for application in similar higher education contexts.