Conference Agenda

Abstract– The underrepresentation of women in STEAM fields remains an educational and social challenge, and teachers play a key role as agents who can reinforce or challenge expectations, stereotypes, and career choices. This paper analyzes teachers' perceptions of the gender gap in technical subjects, distinguishing between participation, academic performance, and perceived discrimination. A Likert-type questionnaire (1–5) is used, consisting of five items that assess: (i) perceived differences in the number of students by gender in STEAM, (ii) stereotypes of technical ability, (iii) beliefs about performance differences, (iv) discrimination associated with the greater male presence, and (v) the perceived "masculine" suitability of STEAM studies. The study employs a descriptive and comparative quantitative approach by gender, using non-parametric tests when assumptions of normality are not met. The results indicate a high perception of inequality in participation (more boys than girls in STEAM) and, simultaneously, disagreement with statements that attribute academic superiority to male students. Perceived discrimination tends to show greater variability and may differ according to teacher characteristics. Implications for teacher training, early academic guidance, and the design of strategies that promote belonging, self-efficacy, and equitable participation in STEAM pathways are discussed.

Gut-brain Axis Disorders (GBAD) are a major public health issue in poor nations, although Central American epidemiology and risk factor modeling are lacking. Bayesian Network (BN) models were developed and validated to characterize probability correlations between demographic, behavioral, and clinical characteristics related with GBAD in Honduran communities. A cross-sectional survey was undertaken in September–December 2025 among 1,838 12-49-year-olds in six health regions. Use of gastrointestinal literature with Maximum Likelihood Estimation to create the BN structure. The model validation used 5-fold cross-validation, resulting in an AUC-ROC of 0.686 (±0.028). Total GBAD prevalence was 56.0%. Conditional probability analysis showed that prior medical diagnosis (P(GBAD|Diagnosis)=0.840), smoking (P(GBAD|Smoking)=0.815), and alcohol intake each had the highest relationships with GBAD. A comprehensive healthy lifestyle reduced GBAD likelihood by 32.0% relative to baseline in what-if intervention analysis. Prior diagnosis (MI=0.047), alcohol intake (MI=0.017), and smoking (MI=0.016) were the most informative predictors. The suggested BN framework helps public health practitioners evaluate intervention scenarios and allocate community GBAD preventive resources in a clear, interpretable manner

The increasing use of predictive models in clinical medicine underscores the need for interpretability mechanisms capable of elucidating the internal logic behind algorithmic decisions. Although black box models often achieve high predictive accuracy, their limited transparency poses significant barriers to clinical adoption. This study presents a comprehensive in silico evaluation framework for Explainable Artificial Intelligence (XAI) techniques, focusing on SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model Agnostic Explanations) applied to supervised models trained on synthetically generated clinical cohorts. A reproducible benchmarking methodology was developed to compare XAI approaches across multiple model architectures (Decision Trees, Random Forest, XGBoost) under carefully controlled simulation scenarios. Technical performance metrics—including fidelity, consistency, and stability—were computed across Monte Carlo replicates with bootstrap derived confidence intervals. SHAP demonstrated superior fidelity (0.89 ± 0.04, 95% CI [0.85, 0.93]) and stability (0.81 ± 0.07) relative to LIME, which achieved a fidelity of 0.74 ± 0.08. Robustness analyses showed that explanation stability decreased by 15.2% (p < 0.001) under 5% input perturbations, with substantial variability across noise levels (η² = 0.34).The proposed framework provides evidence based guidance for selecting and validating XAI methods in clinical decision support applications, emphasizing reproducibility and methodological rigor in alignment with emerging governance standards for AI transparency.

This paper analyzes the epistemic status of generative artificial intelligence systems, with an emphasis on large language models (LLMs), from a perspective explicitly oriented toward engineering design, evaluation, and education. Adopting an operational definition of knowledge as the capacity to construct predictive representations that guide successful action , the paper examines the epistemic limitations of purely textual LLMs arising from their lack of sensorimotor grounding , and contrasts this condition with the potential—and current limits—of multimodal and embodied AI systems to acquire functional world models through interaction with the environment ]. Drawing on theoretical frameworks from embodied cognition and predictive neuroscience], it is argued that multimodal systems can develop operational knowledge relevant to applications in robotics, simulation, and autonomous systems, while remaining conceptually distinct from human knowledge associated with subjective experience and self-representation . As its main contribution, the paper proposes an engineering-oriented epistemic taxonomy and derives from it a set of empirical evaluation criteria intended to guide the design, certification, and training of AI systems in educational and technological contexts.

Despite decades of policies to increase female participation in STEM, women remain underrepresented in Mexico, especially in valued fields like engineering and physics. This systematic review examines how structural inequalities generate the phenomenon of 'quiet
quitting' among women in Mexican STEM. Analyzing 11 studies published between 2016-2025, we document that quiet quitting emerges when gendered organizations demand extraordinary efforts, impose unrecognized work, require constant emotional management, but deny critical
resources and equitable opportunities. The main contribution is theoretically articulating how gendered organization theory connects with quiet quitting as an adaptive strategy against hostile institutional conditions.

This article presents a predictive methodological framework based on Six Sigma DMAIC to transform manufacturing fixture
management from a reactive to a preventive approach in the automotive industry. Through a real case study involving the launch of
evaporators for automotive refrigeration systems, the research documents how out-of-specification baking fixtures generated flatness defects
that affected final product quality. The systematic implementation of the five DMAIC phases (Define, Measure, Analyze, Improve, Control)
enabled root cause identification through coordinate measuring machine (CMM) dimensional analysis, experimental validation of causeeffect
correlation, and implementation of both immediate technical and permanent systemic solutions. Quantifiable results include
improvement in Rolled Throughput Yield (RTY) from 86.59% to 94.3%, an 83.8% reduction in rejection rate, and waste reduction exceeding
70%. The study demonstrates that systematic fixture control constitutes an effective risk management strategy in product launch projects,
applicable beyond the specific automotive context studied.

Failures in industrial machinery represent a significant risk, as they can lead to production losses, reduced competitiveness, and accidents. In this context, vibration measurement has become the primary non-invasive method for detecting and predicting faults in rotating machinery, since failures are typically manifested through progressive increases in vibration levels. However, the effectiveness of this technique largely depends on expert interpretation, which limits its automation and large-scale application.

To overcome this limitation, this study proposes the development of a fault diagnosis model based on machine learning techniques using vibration signals to automatically and accurately classify the condition of a gearbox. Vibration data from the Open Energy Data Initiative (OEDI) repository corresponding to a wind turbine gearbox were used in this work. The methodology included signal preprocessing, feature extraction in the time and frequency domains, and the training of multiple supervised classification models. The results demonstrated that the Support Vector Machine model achieved the best performance, providing a promising solution for optimizing predictive maintenance strategies and preventing failures.

This study examined academic engagement in 438 Argentine university students, analyzing the predictive role of academic self-efficacy, emotional exhaustion, and disciplinary area using hierarchical regression analysis. The final model explained 20% of the variance in engagement. Self-efficacy for goal attainment (β = .27, p < .001) and coping (β = .18, p < .001) showed significant positive associations, whereas emotional exhaustion was negatively related to engagement (β = −.16, p < .001). Regarding disciplinary area, students in Health Sciences reported higher engagement than the reference group (β = .23, p = .023), while no significant differences were observed for Social Sciences and Humanities (β = −.17, p = .106). These findings confirm academic self-efficacy as a key predictor of academic engagement, while emotional exhaustion acts as an inhibiting factor, with contextual variations across disciplines. The results highlight the importance of interventions aimed at strengthening self-efficacy and reducing emotional exhaustion to enhance student retention and well-being, particularly in high-demand fields.

Abstract– The integration of immersive technologies such as virtual reality (VR) in higher education represents a key opportunity to enhance innovation, motivation, and experiential learning in engineering. This study surveyed 312 engineering professors from 12 Latin American countries following a two-week training program on VR. The questionnaire assessed digital competence, technical aspects, usability, disadvantages, future outlook, and didactic value. The sample was not homogeneous by generation (62% digital immigrants and 38% digital natives) and was predominantly male. The results revealed very high evaluations in technical, usability, and didactic dimensions, while digital competence received comparatively lower ratings. Significant generational differences were identified: digital natives reported higher levels of digital competence, whereas digital immigrants rated technical aspects more positively. These findings highlight the need to strengthen digital competence across generations and promote the pedagogical use of immersive technologies in engineering education.