Programa del congreso

Employability and the perception of competency development among graduates constitute key inputs for accreditation and quality improvement in engineering programs. This study analyzes the results of the Employability and Labor Market Insertion Survey administered to graduates of Industrial Engineering (n = 256) and Electrical Engineering (n = 226) from a Colombian public university, who graduated between 2015 and 2024. The results show a high labor market insertion rate (between 91% and 96% depending on the program), with most graduates employed in areas related to their academic training and obtaining their first job within six months after graduation. Additionally, graduates report a positive assessment of the development of fundamental competencies, particularly in mathematics, technical knowledge, and engineering problem-solving. However, gaps are identified in the strengthening of advanced analytical methods, applied use of digital technologies, second-language communication, teamwork in multidisciplinary environments, and the integration of economic, social, environmental, and sustainability approaches—more pronounced in Electrical Engineering. Overall, the findings provide empirical evidence to support EUR-ACE accreditation and offer strategic inputs for curricular improvement and the enhancement of quality in engineering education.

The accelerated integration of artificial intelligence (AI) in higher education is reshaping academic practices, particularly within engineering and technology-oriented environments. Despite this transformation, empirical evidence regarding generational differences in the academic adoption of AI remains limited. This study examined generational patterns in the academic integration of AI among 301 university students, assessing three dimensions: academic use of AI, trust in AI systems, and ethical awareness. Using a validated three-dimensional instrument (CFI = .974; RMSEA = .051), results indicated moderate levels of academic use (M = 2.92), slightly higher levels of trust (M = 3.05), and comparatively lower levels of ethical awareness (M = 2.73). Pearson correlations revealed significant positive associations among all dimensions (p < .001). No statistically significant differences were found by gender. However, Welch’s ANOVA showed a significant age effect on academic AI use (F = 7.02, p < .001), with participants aged 43 years or older reporting significantly lower utilization. No generational differences were observed in trust or ethical awareness. These findings suggest that generational disparities in AI integration are primarily behavioral rather than attitudinal, posing important implications for university AI governance, curriculum design, and the implementation of differentiated training strategies within digitally transforming educational ecosystems.

In the Latin American context, quality assurance systems face the challenge of moving from formal compliance to continuous improvement, supported by evidence. In Peru, the institutional licensing in charge of SUNEDU verifies Basic Quality Conditions to authorize the operation of the educational service, while the accreditation (under the guidance of SINEACE) evaluates standards oriented to performance and continuous improvement. In this framework, the present study designs and operationally validates a management plan for the reaccreditation of a nursing program in a private university in Arequipa, addressing gaps in knowledge of standards, low teacher participation, and delays in the delivery and validation of evidence. An applied, mixed (qualitative-quantitative), non-experimental and cross-sectional design was adopted. The process combined documentary review, observation of the flow of evidence and semi-structured interviews; in addition, improvement tools (Lean), causal analysis (Ishikawa), alignment (Hoshin Kanri) and governance of responsibilities (RACI) were used. The proposed intervention operationalizes the 34 standards of the accreditation model through quarterly OKR (Objectives and Key Results) cycles, an approach originated in Intel and widely disseminated after its adoption by Google. The results of the diagnosis establish baseline and estimated goals: closing gaps in compliance with standards, reducing the time it takes to deliver evidence, and increasing teacher involvement, conditioned by the discipline of execution and periodic monitoring. It is concluded that an OKR scheme, integrated with repositories and digital dashboards, improves traceability, inter-area coordination and the timeliness of evidence, strengthening the operational sustainability of quality assurance without relying on "last-minute" efforts.

This review examines how business management in Latin America has evolved between 2020 and 2025 through the incorporation of environmental sustainability, using a systematic review of the literature under the PRISMA protocol and the PICO framework. The articles analyzed were extracted from reliable databases such as Scopus, WOS, Scielo, and Google Scholar. After applying inclusion and exclusion criteria related to thematic relevance, timeliness, and methodological rigor, 15 articles were selected that adequately responded to the objective of this research. The findings reveal that companies that apply sustainable practices manage to improve their operational efficiency, optimize resources, strengthen their corporate image, and increase their competitiveness. Conversely, organizations that do not adopt these practices face limitations arising from legal requirements, market changes, and social pressure. Likewise, the positive role of public policies and environmental regulations in promoting sustainable initiatives is highlighted. In conclusion, the need for a comprehensive vision that combines technological, regulatory, and organizational dimensions to achieve effective business sustainability and generate sustainable competitive advantages is emphasized.

In Peru, university quality assurance is moving from a compliance-focused approach to a results-oriented one after the approval of the Accreditation Model for University Higher Education Study Programs (CONEAU, 2025). This study analyzes the degree of normative convergence of the model with international engineering accreditation frameworks and identifies structural gaps that limit the demonstration of substantial equivalence in training terms. A qualitative approach of comparative documentary and normative analysis was used, using a correspondence matrix to map dimensions of the national model with international references and derive a taxonomy of gaps. The findings show that the 2025 Model incorporates indicators and means of verification, defines differentiated periods of accreditation validity and reinforces evidence-based management; in addition, it integrates University Social Responsibility as an assessable axis. However, six structural gaps are identified: (i) disconnection between data and decisions, (ii) tension between virtuality and experimental practices, (iii) teaching capacities for results-based evaluation, (iv) industry-curriculum feedback, (v) operationalization of sustainability and sociotechnical competencies, and (vi) terminological precision to support substantial equivalence. Overall, the results suggest that regulatory convergence is significant, but insufficient without strengthening systems for assessment, traceability and governance of quality assurance, especially in a context where distance learning can cover all loans under regulated conditions.

This research aims to analyze university entrance scores using linear regression models from both explanatory and predictive perspectives. A quantitative cross-sectional design was used with 545 university students, considering socioeconomic, academic, motivational, and demographic variables. An exploratory data analysis was conducted, revealing weak but statistically significant associations between entrance scores and variables such as income and years of study. Subsequently, an explanatory model was created using classical linear regression (OLS), incorporating a process for selecting the most significant variables. The final model yielded an adjusted R² of 0.126, indicating limited explanatory power. The variables that showed statistically significant effects were years of study, type of high school attended, and the mother's educational level. Next, a predictive approach based on linear regression was implemented under the machine learning paradigm, using validation with training and test data, as well as automatic variable selection via LASSO regression. The performance metrics showed low predictive capacity, reflecting limitations in the model's generalizability. The final results of the study show that linear regression is useful for explaining significant associations but has predictive limitations; therefore, both approaches complement each other to offer a comprehensive perspective on university admissions.