Conference Agenda

In recent years, many questions have arisen regarding the use of generative AI in programming courses. There are concerns about the development of students’ programming skills, the relevance of continuing to teach computer programming to all students, the best way to introduce AI tools to programming classes, ethical issues, and their appropriate use, among other aspects. In this work, we conducted a survey with a group of professors at our university to gather their perceptions on these topics. We found that all the professors are aware of the AI tools and that most students will use them, whether they are instructed to do so or not. Many professors support the idea that AI tools must be included in programming courses, although some still believe these tools should not be used. Most professors think that basic programming should be taught to students of all majors, and they are concerned about ethical issues related to the use of AI tools. Most of the professors who answered the survey agree that the AI tool should be used as a support tool, never as the main tool in programming courses, and they also state that these tools must be used after having a solid knowledge of the programming fundamentals.

This paper presents the development and validation of a hybrid artificial intelligence framework integrating predictive and prescriptive capabilities to optimize maintenance in the textile industry. The research addresses the high failure rate of critical textile machinery components—such as needles, feeders, guides, and tensioners—whose traditionally reactive management leads to unplanned downtime, increased operational costs, and deterioration of final product quality. The methodology comprises: historical data acquisition, preprocessing, feature engineering (MTBF, MTTR), development of predictive models (ARIMA, XGBoost), and a prescriptive module that transforms predictions into optimized decisions through multi-objective optimization subject to operational constraints. The results demonstrate significant improvements: reduced downtime from unexpected breakages, near-total elimination of fabric waste due to defective seams, decreased sudden failures, increased specific MTBF, and reduction of non-conforming products. It is concluded that the value of predictive maintenance is enhanced when complemented by a prescriptive module that translates predictions into contextualized decisions, bridging the gap between theory and its practical application in real production environments. The framework provides a replicable roadmap for extending the benefits of prescriptive maintenance to the textile sector.

Artificial intelligence (AI) has become a transformative technology that redefines professional profiles and employability conditions, making it imperative for higher education institutions (HEIs) to prepare graduates to face these changes. This critical review examines the literature on the assessment of AI literacy (AIL) in the university context, supported by a multi-method approach that integrates critical review and hermeneutic triangulation. Existing conceptual frameworks, such as those from UNESCO and Ng et al., are analyzed, which converge on key dimensions: technical knowledge, application, critical evaluation, and ethics. A critical gap is evident between the high dependence on self-report instruments, which may be biased by individual overconfidence, and the emerging need for performance-based assessments that measure actual competence. Validated tools such as the AILQ (Artificial Intelligence Literacy Questionnaire) and the GLAT (Generative AI Literacy Assessment Test) performance test are highlighted, analyzing their structure and applicability. Institutional responses are explored, from "AI Across the Curriculum" curricular integration to the development of ethical policies. The findings indicate that while there is consensus on the dimensions of AIL, challenges persist in assessment standardization, academic integrity, and algorithmic bias. The conclusion underscores the urgency of adopting a multimodal approach that combines self-reports with performance tests to obtain a reliable measure of AIL, ensuring that graduates are truly prepared for an AI-driven labor market.

Finding relevant procurement opportunities in Chile's Mercado Público platform is genuinely difficult for small suppliers. The platform processed over 2 million purchase orders in 2024, but its category taxonomy is inconsistently applied, descriptive fields vary in quality, and there is no automated filtering tailored to provider needs. This paper asks whether structured administrative variables alone, without any processing of tender text, can support accurate automatic classification of public tenders into procurement categories. We work with 204,770 tender records from 2024 and focus on the 10 most frequent combinations, covering 56,555 records. Principal Component Analysis (PCA) reduces 14 structured variables to 10 components retaining 89.82% of variance; six classifiers are then trained and compared: Random Forest, XGBoost, Decision Tree, Logistic Regression, SVM, and k-Nearest Neighbors. Random Forest reaches 91.17% accuracy and an F1-macro score of 0.9112. A single Decision Tree comes within 0.06 percentage points of that figure, which has practical implications for deployments where audit trails matter. The full pipeline was implemented independently in both R and Python, with under 1% accuracy difference between environments, confirming reproducibility. The gap between tree-based methods and Logistic Regression exceeds 51 percentage points, confirming that the classification boundaries are non-linear and cannot be captured by simpler models. These results suggest that structured-variable classification is a viable foundation for SME-facing tender recommendation systems in procurement platforms with heterogeneous or incomplete text data.

The objective of this research was to propose a framework based on predictive modeling with Artificial Intelligence (AI) to mitigate systemic instability, operational variability and cost overruns in the management of public infrastructure in the Andean region. The study was based on a quantitative-deductive approach methodology and explanatory level, analyzing a representative sample of 88 Peruvian state projects through a non-experimental cross-sectional design. The reliability of the data collection instrument was validated with a Cronbach's alpha coefficient of 0.840, certifying a high internal consistency. The results reveal a critical gap in the sector: 76% of the works ignore sustainability criteria and 88% lack predictive technological tools, which generates a state of critical risk and operational uncertainty. A significant correlation (rs=0.78) was demonstrated between the absence of machine learning models and the uncontrolled variability of costs and deadlines. It is concluded that the integration of AI is imperative to move towards precision engineering that prioritizes environmental protection and climate resilience under the framework of SINAGERD 2050. The main contribution lies in a decision-making support system capable of optimising resources, reducing the carbon footprint and ensuring compliance with international standards (ISO), consolidating technology as the axis of a symbiosis between industrial productivity and the preservation of ecosystems.

Water management at a global level presents a challenge due to high demand driven by population growth and the effects of climate change. In arid to semi-arid zones with seasonal rainfall, the use of Nature-based Solutions (NbS) is common, drawing on ancestral knowledge. In areas dominated by dry forests and high humidity, meteorological phenomena such as coastal fog (kamanchaka), which result from the interaction of ocean currents, temperature, and topography, help maintain equilibrium and provide water for the survival of flora and fauna. In this context, this study aims to contextualize the role of the kamanchaka meteorological phenomenon by integrating analyses of geodiversity and biodiversity, as well as its potential as an alternative water resource, to define a water management strategy across the five subsystems of sustainability and innovation. The research was structured in three phases: i) geographical and geological characterization, ii) water management using Nature-based Solutions (NbS), and iii) a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis for developing sustainable water management strategies. The results show the potential of the kamanchaka (a type of fog-harvesting system) for integration as an NbS that promotes the conservation of the protected ecosystem and the campus's water resilience. The participatory analysis suggests implementing fog harvesting as an unconventional water source that needs to be integrated into institutional management plans and the continuous learning environment, which serves as a demonstration site for ecohydrology. This study represents a replicable NbS model for coastal areas with similar conditions.