Conference Agenda

The study was conducted at a transportation company and aimed to determine the influence of Big Data usage on last-mile service management in a transportation company in Lima, 2025. The general hypothesis formulated was that the use of Big Data significantly influences last-mile service management. An applied methodology with a pre-experimental design was adopted. The primary evaluation instrument used was a data observation sheet, complemented by a questionnaire. The study population covered the company's transportation processes and 50 individuals from the warehouse and transportation management areas.The research results demonstrated that the implementation of Big Data had a highly positive impact, improving efficiency in various logistical aspects. The main conclusion of the study was that the application of Big Data has been highly beneficial for optimizing transportation management within the company, strongly supporting the proposed hypothesis and validating its importance in improving last-mile logistics. It was concluded that the implementation of Big Data improved the perception of transportation management, showing a 78.57% improvement (increasing from 2.52 to 4.50), indicating management levels slightly above average and demonstrating a statistically significant difference with a Z = -6.248 and a p-valor = 0.000.

This paper examines the reconfiguration of epistemic agency in contexts shaped by the expansion of artificial intelligence (AI), focusing on its effects on subjectivity, education, and critical thinking. It argues that the growing algorithmic mediation of processes of access, production, and validation of knowledge profoundly transforms individual and collective cognitive practices. From a psychosocial and educational perspective, the study explores the risks associated with the automation of judgment, cognitive dependency, and epistemic delegation, as well as the possibilities for constructing a critical subjectivity capable of reflective interaction with intelligent systems. The paper contends that strengthening epistemic agency is a necessary condition for the development of an ethical, autonomous, and socially responsible technological humanity.

The accelerated integration of artificial intelligence (AI) tools, especially generative AI, is changing how learning takes place in higher education, and more specifically in engineering. These technologies open significant doors to support learning, solve complex problems, personalize instruction, and improve students’ cognitive efficiency. But their widespread and, in many cases, unregulated use has raised new concerns about academic integrity, excessive reliance on technology, the weakening of critical thinking, and ethical dilemmas regarding authorship and responsibility for learning. The article aims to explore the ethical use of AI among engineering students through an integrative review of recent scientific literature and empirical studies addressing student perceptions, practices, ethical frameworks, and institutional policies. An analytical-comparative review methodology is employed to identify usage patterns, benefits, emerging risks, and training needs related to AI literacy. The results show that, although engineering students have a positive attitude toward AI as a teaching support tool, they tend to normalize dangerous practices when no guidelines or ethical training are in place. Furthermore, the ethical use of AI is linked to the development of analytical skills, provided it is accompanied by pedagogical strategies, institutional policies, and an awareness of its limitations.

It is determined that the ethical use of AI in engineering education requires a comprehensive approach that combines regulatory frameworks, ethical and pedagogical literacy, and the redesign of teaching and assessment practices to leverage its benefits and minimize its risks.

Abstract: Given the rapid adoption of Generative Artificial Intelligence (GAI) in higher education, it poses significant risks and opportunities. This study analyzes GAI tools among master's students in Project Management at a university in Honduras. Using an expert-validated instrument and a quantitative approach applied to a sample of 70 students, the benefits, perceived risks, patterns of use, and ethical considerations were explored. The results show almost universal use (95.71%), with frequent weekly use and tools such as ChatGPT, Copilot, and Gemini being the most widely used. Students appreciate GAI primarily for its potential to understand complex concepts and explore new ideas. They also expressed high ethical awareness and low training in the subject. This provides evidence of the urgent need for clear institutional policies coupled with digital literacy programs in GAI.

This systematic review examines the ethical implications of artificial intelligence (AI) implementation in educational environments. Through a comprehensive analysis of peer-reviewed literature published between 2021 and 2026, this study identifies key ethical concerns, including privacy and data protection, algorithmic bias and fairness, transparency and explainability, student autonomy, and the digital divide. The review synthesizes findings from 41 selected studies using PRISMA methodology, revealing that while AI offers significant potential to personalize learning and improve educational outcomes, it also raises critical ethical challenges that require careful consideration. Major concerns include the collection and use of student data, potential discrimination through biased algorithms, lack of transparency in AI decision-making processes, and unequal access to AI-enhanced educational resources. The study proposes a comprehensive ethical framework for AI implementation in education based on principles of beneficence, non-maleficence, autonomy, justice, and explicability. Recommendations are provided for educators, policymakers, and technology developers to ensure responsible and equitable AI integration in educational settings.

The integration of Generative Artificial Intelligence (GenAI) into engineering education raises a critical tension between operational efficiency and the development of deep cognitive competencies. This study examines this dichotomy in 162 Civil Engineering in Computer Science students at a Chilean state university. Using a quantitative descriptive–correlational design, it investigates the relationship between usage patterns and perceptions of academic self-efficacy. The results reveal a “paradox of efficiency”: while 86.4% of students use the tool as conceptual support and 72% value the acceleration of code debugging, 60.9% acknowledge a significant risk of cognitive dependency that threatens their problem-solving autonomy. The analysis suggests that, in contexts with limited personalized tutoring, AI assumes the role of a “Shadow Tutor,” boosting short-term performance while generating ethical and epistemic friction in the long term. The study concludes by highlighting the need to transition toward a “pedagogy of verification,” in which assessment focuses on the critical auditing of AI-generated solutions rather than on syntactic coding alone.

This study aims to analyze the influence of ethical factors on the academic training of students in the Faculty of Industrial Engineering at the José Faustino Sánchez Carrión National University in Huacho during 2024. The factors evaluated include ethical values, principles of professional conduct, soft skills, technical knowledge, and sales management. This analysis seeks to identify how these dimensions contribute to the comprehensive development of future engineers, highlighting the importance of a solid foundation in both technical and ethical aspects of academic and professional life. A quantitative, descriptive, and correlational methodology was used to evaluate the relationships between ethical factors and academic training. The population consisted of students enrolled in 2024, selected using a census sampling method. Data were collected through structured surveys, and the results were processed using statistical tools such as Spearman's rank correlation coefficient and principal component analysis (PCA). Furthermore, structural equation modeling (PLS-SEM) was used with SmartPLS 4 software to validate the proposed model and estimate the influence of the constructs. The results revealed that the dimensions "Level of technical knowledge" (𝛽 = 0.30, p < 0.01) and "Level of ethical principles" (𝛽 = 0.25, p < 0.01) had the greatest positive impact on academic performance, explaining 55% of the variance of this variable (R² = 0.55), which highlights the relevance of these factors in students' academic development.