Conference Agenda

This study aims to diagnose the level of knowledge, self-perception, and attitudes toward sustainability and the Sustainable Development Goals (SDGs) among first-year students in five engineering programs at a Chilean private university, considering the academic program and gender as analysis variables. A quantitative approach was adopted, using a non-experimental, cross-sectional, and descriptive-comparative design. A questionnaire was employed to assess knowledge of the SDGs, the source of that knowledge, the students' professional and personal involvement with the SDGs, and the sustainability training received within the university context. The results show that students possess a moderate understanding of the SDGs, highlighting formal education as their primary source of information. The specific major constitutes a significant differentiating factor in both professional involvement with the SDGs and the university training received. In contrast, personal involvement showed no significant differences between programs. No significant differences were detected by gender, indicating an equitable perception between men and women. This research underlines the importance of diagnosing these competencies to train professionals capable of addressing global social and environmental challenges.

The present study is guided by the following scientific question: How can the evolution of knowledge over time be modeled through differential equations by deriving predictive metrics within learning analytics? Based on this inquiry, the general objective is to model the evolution of knowledge over time using differential equations in order to derive predictive metrics in learning analytics. The study follows a quantitative approach with a non-experimental, longitudinal design. The sample consisted of a cohort of 59 students enrolled in the course Linear Algebra and Analytic Geometry. A total of 22 assessment activities recorded in Moodle were normalized to the [0,1] scale and organized into six weekly blocks to construct a temporal series of academic performance. Using these data, a differential learning–forgetting model was fitted, integrating a learning rate and a forgetting rate. The estimated parameters (a = 0.42; b = 0.15) allowed the projection of an equilibrium level consistent with the final observed performance (0.73) and the derivation of predictive metrics, such as convergence half-life and the projected time horizon for crossing academic proficiency thresholds. The findings confirm the dynamic nature of learning and demonstrate the relevance of the model as a tool to support data-informed pedagogical decision-making.

Accelerated urbanization and associated phenomena—such as inequality, poverty, overpopulation, habitat degradation, deforestation, biodiversity loss, pollution, and depletion of natural resources—continue to hinder progress toward sustainable development. Although the urgency of these challenges is evident, a marked social indifference persists, also reflected among university students, who often display disinterest, rote learning, and limited ethical awareness. This cognitive and attitudinal disconnection hinders the construction of robust knowledge and the development of critical competencies needed to understand and mitigate complex socio-environmental challenges.

This study presents the results of integrating Artificial Intelligence (AI) tools into a university educational environment to strengthen critical reasoning, ethical decision-making, and interdisciplinary understanding related to climate change and sustainable development. AI was employed to analyze urban data, model scenarios, and generate strategies aimed at improving community sustainability, positioning students as active agents in the analysis of socio-ecological systems.

Findings show that pedagogical integration of AI significantly increases student interest, participation, and the development of both disciplinary and transversal competencies by providing dynamic, contextualized learning experiences aligned with the Sustainable Development Goals. The adoption of emerging technologies in higher education not only enhances the understanding of present environmental challenges but also empowers students as active contributors in constructing sustainable solutions for their communities.

This article presents LxC Engineering (Life per Carbon), a regenerative and participatory engineering approach aimed at transforming degradative processes into sustainable, functional, and multi-capital systems. Derived from the IP.IDEAS.S framework, it is grounded in the CRFC (Carbon Regenerative Physicochemical Cycle), whose operative equation states: L = C + 6.5 kWh/kg, where L is regenerated life, C is captured carbon (kg CO₂ eq), and 6.5 kWh/kg is the solar energy required per kg of useful biomass.

Its methodology integrates tools that support the co-design, implementation, and evaluation of regenerative and participatory processes:

• DPAV (Decide, Plan, Act, Value): strategic governance cycle for making informed decisions under uncertainty.
• CUPA (Calculate, Understand, Plan, Act): adaptive operational core structured around technical, creative, and social engagement.
• ToC (Theory of Constraints): prioritization and measurement logic through five iterative steps to focus action.
• ADFI (Analyze, Define, Formulate, Implement): participatory engineering methodology that involves stakeholders in every phase from contextual analysis to implemented improvements.
• 9SP (Nine Strategic Pillars): participatory strategic framework articulating Purpose, Problematic, Potential, People, Proposal, Process, Project, Budget, and Benefit.

VxC quantifies regeneration through indicators such as ROP (Return on Process), BM (Marginal Benefit), CM (Marginal Consumption), and PTF (Total Factor Productivity). Its territorial validation is based on the ATA case (Amazon Tambo Hamlet), applying Markov chain simulations and input-output matrices. Results demonstrate the feasibility of reconfiguring degraded territorial processes toward regenerative and sustainable futures through participatory governance and value co-creation.