Conference Agenda

In recent years, Artificial Intelligence (AI) has experienced remarkable growth, influencing a wide range of industries, particularly the educational sector. This became more noticeable during the pandemic, forcing distance learning. The incorporation of AI-driven educational platforms has enabled continuous assistance to students and educators, largely fueled by advancements in Deep Learning. Within engineering education, AI-based learning platforms have emerged as tools for customizing educational experiences, boosting academic outcomes, and enhancing administrative processes. Research indicates that these platforms can elevate student engagement by up to 23% and significantly improve knowledge retention. Nonetheless, their widespread adoption also introduces challenges, such as fostering technological dependency and diminishing critical thinking, particularly in self-directed learning environments. As these systems become increasingly embedded in academic settings, it is essential to assess their real impact on learners and faculty. Moreover, the flexibility of Natural Language Processing (NLP) technologies may lead to misuse due to limited oversight and technical literacy, compromising education. Although many publications have explored AI’s role in education, few have provided an analytical approach to its consequences. This research aims to fill that gap by evaluating how AI-powered platforms can be better leveraged to support balanced, responsible, and effective learning practices in engineering higher education.

Engineering projects in desertification-affected regions like Valparaíso, Chile, must address complex environmental, technical, and socio-economic challenges such as water scarcity and soil degradation. Traditional Root Cause Analysis (RCA) methods often fall short in these dynamic contexts due to limited scalability and adaptability. This study presents a novel methodology integrating Artificial Intelligence (AI), Large Language Models (LLMs), and Automatic Speech Recognition (ASR) to enhance RCA in environmental adaptation and mitigation efforts.

The approach leverages the 9 Windows Model from the TRIZ methodology for multi-level, time-scaled problem analysis. It involves three stages: (1) collecting and transcribing environmental discussions via ASR, (2) using LLMs to extract RCA insights aligned with the 9 Windows framework, and (3) generating automated reports with visualizations and strategic recommendations.

A case study in Valparaíso examines the impact of desertification on water availability and agricultural productivity, demonstrating improved decision-making speed and quality. The approach reduces diagnostic time and supports more effective mitigation strategies.

While AI-related challenges like bias and data dependency persist, the study emphasizes the importance of a human-in-the-loop model. This research offers a scalable, structured framework for applying AI to environmental management and supports innovation in multidisciplinary problem-solving.

This research meticulously assesses the effectiveness of public cloud solutions in Costa Rica, using a mixed-methods framework to address the dichotomy between the democratizing potential of cloud computing and its actual results in developing countries. By combining Resource-Based View (RBV) theory with regional contextual analysis, we look at data from 200 firms and Chief Executive Officer (CEO) interviews to uncover three important things: (1) Enterprises achieve 22% greater cost savings than Small and Medium-sized Enterprise (SMEs) (*p* < 0.05), underscoring RBV’s resource advantage hypothesis while exposing its neglect of contextual barriers like regulatory fragmentation (29% higher compliance costs) and talent scarcity; (2) Leadership commitment mediates 68% of performance variance (β = 0.68), necessitating the expansion of Strategic Alignment Models (SAM) to include cultural preparedness as a measurable construct; and (3) Costa Rica’s reliance on foreign hyperscale’s exacerbates latency (40% worse than global averages) and vendor lock-in (63% prevalence), challenging universalist cloud frameworks. The research presents a Strategic-Regional Alignment Model (SRAM) that incorporates legal readiness assessments and infrastructural standards, in addition to a validated five-dimensional cultural readiness instrument. Policy suggestions for regulatory harmonization (like a National Cloud Office) and solutions for small and medium-sized businesses (like multi-cloud pilots) are some of the practical consequences.

The purpose of this research was to design a temperature control system to increase profits in the noodle drying process. The company under study produces a variety of products such as fine, thick and ribbon noodles. However, the drying process presents problems due to non-uniform distribution of hot air, which generates moisture levels outside the quality parameters and causes reprocessing. To address this problem, we began with a diagnosis of the process, identifying causes and evaluating reprocessing percentages by means of statistical sampling. The current dryer was modeled in SolidWorks and the economic impact of rework was calculated. Subsequently, the design of a new temperature control system was developed to determine the optimum drying time, a PID controller programmed in MATLAB was implemented, the control plane was designed in Cade Simu and the control system was simulated in SoMachine. Finally, a cost-benefit analysis was performed, which showed an improvement by reducing the percentage of shredded noodles to 10 % and of wet noodles to 12 %. Finally, the improvement made it possible to achieve an annual profit in the first year of S/ 991 876 and a cost benefit of 2,91, which showed that the project was viable and profitable.

The aim of this article is to present the development of STEM project-oriented kits to generate interest in mechatronics engineering among young people. These kits are divided into three areas: Fun, Ecology, and Innovation. Each kit contains projects related to the proposed topics. Students must program the devices and build models from 3D-printed parts. These components are combined to create engaging projects that will allow them to learn the basics of an engineer's work.
These projects were validated through testing with high school students. They were provided with scenarios for assembling and programming them. Surveys were conducted before and after these activities to understand their perceptions.

The monitoring of physical activity through wearable devices has significantly advanced, mainly focusing on basic parameters such as heart rate and calories burned. However, these devices often lack the capability to assess the correct execution of movements, a critical factor in preventing sports injuries. Existing patents and technologies, including optical sensors in smart glasses, gesture-controlled smartwatches, and electromyographic armbands, demonstrate progress in detecting physiological parameters and body movements but have limitations in active injury prevention. Therefore, integrating motion and heart rate sensors to identify and alert users about harmful movements during physical activity presents an innovative opportunity to enhance the safety and effectiveness of sports training.

This research evaluates the potential of blockchain technology to mitigate corruption in Costa Rica's public procurement system, employing a mixed-methods approach that encompasses an analysis of 14,327 procedures (2018-2023), cryptographic audits, and stakeholder ethnography (n = 37). Results show that blockchain can cut processing times by 37.5% (95% Confidence Interval (CI) [32.1, 42.9]) and boost Small and Medium-sized Businesses (SME) participation by 40%. However, there are still institutional barriers: 27.1% of contracts don't follow transparency rules, and the blockchain adaptability index (α = 0.88) only scores 4.2/10, which is below the level required for sustainable adoption. The research proposes an institutional cryptography framework, demonstrating the adaptability of corruption to digital systems, with cryptographic audits indicating that 12% of data exhibit manipulation. A hybrid anti-corruption model is suggested, which includes changes to the law (amendments to Law 7494), expanding capacity, and phased blockchain implementation (Hyperledger Fabric at 80 TPS). This model is 42% more effective at reducing corruption than alternatives that work on their own. Innovative methods include live blockchain simulations with 4,821 transactions, game-theoretic stakeholder analysis, and seminars for SMEs that allow everyone to participate.

Abstract– The study analyzes how green marketing infl uences consumer behavior in promoting eco-friendly products, especially among young people and Generation Z. It addresses the research question: What are the relationships between green marketing strategies and the pro-environmental behavior of diff erent consumers in the decision to purchase eco-friendly products? Key factors identifi ed include responsible attitudes, environmental awareness, eco-labeling, and the role of social media. Eff ective strategies such as the green marketing mix, emotional advertising, and educational campaigns are highlighted. Despite growing interest, barriers such as high prices and lack of information persist. The research proposes strengthening environmental education and adapting strategies to diff erent social profi les.