Conference Agenda

Generative artificial intelligence (AI), particularly ChatGPT, has reshaped learning processes in higher education by offering new opportunities alongside emerging challenges. Although its use has rapidly expanded, questions remain regarding how students integrate it into their academic practices, what perceptions they hold about its effectiveness, and which ethical implications arise from its application.

This study aimed to analyze university students’ perceptions and experiences regarding the use of generative AI tools, identifying associated benefits, limitations, and ethical considerations. A qualitative, descriptive, and cross-sectional approach was adopted, using focus groups with students from a Honduran university. Data were transcribed, validated, and analyzed through thematic coding based on a predefined category framework. Discourse saturation was visualized through a word cloud, a heat map, and a conceptual network.

The findings revealed that students frequently use ChatGPT as support for content generation, information retrieval, and text refinement. However, they also noted limitations in accuracy, particularly in areas involving mathematics and programming.

The study concludes that AI adoption among students is primarily driven by functional considerations, with ethical reflection remaining at an early stage. These results underscore the need for educational strategies that integrate critical and ethical training into the use of AI

Although Artificial Intelligence (AI) has shown a significant impact in various educational fields, its specific application in enhancing reading comprehension remains limited and fragmented. Most existing studies focus on isolated tools rather than offering an integrated perspective on how AI technologies can support reading processes across formal education levels. To address this gap, the present study aimed to identify and systematize the fundamental AI pillars with potential application in reading comprehension. A scoping review was conducted following the Arksey and O’Malley framework, comprising five stages. A total of 696 documents were retrieved from Scopus and Web of Science, from which 8 empirical studies were ultimately selected based on their practical evidence of AI use in literacy-related educational contexts. Using co-occurrence analysis through VOSviewer and qualitative synthesis, four key pillars were identified: (1) generative models for text simplification, (2) automatic question generation and narrative datasets, (3) personalized adaptive learning platforms, and (4) AI-supported moderation in online discussion environments. These pillars reflect a shift from instrumental to cognitively supportive uses of AI in educational settings. The findings offer both theoretical insights and practical guidance for educators, policymakers, and researchers interested in the ethical, inclusive, and meaningful integration of AI to foster reading comprehension.

This study analyzes the factors associated with entrepreneurial potential in Mexican upper secondary school students through a quantitative approach and an educational intervention based on the Canvas Model. Using an intentional sample of 27 scholarship recipients aged 12 to 16, variables such as entrepreneurial skills, personal experience, family environment, and the influence of the educational institution were evaluated. The results reveal a high intention to undertake among young people, as well as a significant correlation between entrepreneurial self-efficacy and the desire to start a business. However, low exposure to entrepreneurship content in the school environment was identified. The linear regression model explained 49% of the variance in entrepreneurial potential, highlighting the perception of skills as the main predictor. After the educational intervention, an increase in entrepreneurial motivation was observed. It is concluded that strengthening entrepreneurial competencies from an early age, accompanied by active educational strategies, can enhance the entrepreneurial spirit of youth.

This study evaluates the impact of an artificial intelligence (AI) platform for mass expense validation on tax compliance costs for micro and small enterprises (MSEs) in Lima's commercial sector. Combining a mixed-methods approach, we analyze efficiency gains, accuracy improvements, and accessibility enhancements tailored to Peru's unique context. Our methodology integrates a PRISMA systematic review of 85 international studies (2018–2025) on tax applications of AI, the analysis of approximately 5,600 actual SUNAT receipts (2025/01 to 2025/06) to train context-specific algorithms, pilot tests with 17 MSEs in three major commercial districts (Centro de Lima, Lurigancho- Chosica, San Martin, Lince y Los Olivos), and econometric modeling for macroeconomic projections.. Key findings demonstrate a 78% reduction in validation time per receipt (p < 0.01), 65% decrease in declarative errors (95% CI: 62-68%), average annual savings of S/ 6,800 per business in compliance costs, and an 83% adoption rate

facilitated by our bilingual (Spanish-Quechua) interface. The study makes three original contributions: (1) the first AI system specifically designed for Peru's structural informality, (2) a scalable methodological framework for digital tax solutions in emerging economies, and (3) empirical evidence on the cost- benefit ratio of AI implementations for MSEs.

The integration of Information and Communication Technologies (ICT) in higher education plays a crucial role in fostering meaningful learning aligned with current social and educational demands. In this context, the teacher's role as a mediator in the use of digital tools becomes essential. This study aimed to examine the effect of the GeoGebra digital tool on enhancing the learning of numerical sets among students at the Autonomous University of Peru. GeoGebra, widely recognized by mathematics educators, enables dynamic visualization of mathematical structures, facilitating conceptual understanding. The research adopted a quantitative approach with a quasi-experimental design. The population included 235 second-cycle students enrolled in the Mathematical Information Management course during the 2023-II term. A non-equivalent sampling method was used to select 120 participants, divided equally between a control group and an experimental group. A 10-item test was used to measure students’ knowledge of numerical sets before and after the intervention. The findings indicate that the use of GeoGebra significantly improved students’ comprehension and retention of numerical set concepts. The study concludes that integrating GeoGebra into the mathematics curriculum not only strengthens understanding of specific topics but also contributes to a more engaging and effective mathematical learning experience in university settings

This study examines the mechanical behavior of masonry units and prisms built with handcrafted and industrial bricks through experimental testing and numerical modeling using the Finite Element Method (FEM). Axial compression tests were conducted in the laboratory to determine the strength, strain, and elastic modulus of each material type. Experimental results revealed that industrial bricks exhibited significantly higher compressive strength, reaching up to 152.45 kg/cm² in single units and 94.00 kg/cm² in prisms, compared to 97.65 kg/cm² and 24.99 kg/cm² for handcrafted bricks, respectively. Subsequently, numerical models were calibrated in DIANA FEA, achieving a deviation of less than 3.75% from physical tests. The study also identifies common testing errors, such as excessive initial load or unrecorded early deformations. Findings underscore the importance of regional material characterization and constitutive model adjustment. The study concludes that FEM simulations, when properly calibrated, provide an efficient tool for structural analysis of masonry systems, especially in regions like Cajamarca, where the use of handcrafted bricks remains prevalent

This case study explores the educational trajectories of beneficiaries of the Beca 18 program who initially enrolled at IES Avansys and later completed their technical higher education at Cibertec Institute following its institutional integration (2019–2020). The study analyzes enabling and limiting factors that shaped the program's capacity to deliver inclusive, high-quality, and equitable education. Using an exploratory qualitative methodology, the research highlights critical lessons in academic reinforcement, curriculum relevance, intercultural competencies, institutional accompaniment, and labor insertion. Findings show that although the public-private co-management model fosters innovation and adaptation, persistent gaps in academic support systems, digital infrastructure, and coordination mechanisms hinder effective implementation. Participants converge on the need to strengthen monitoring strategies, promote flexible and culturally responsive pedagogies, and reinforce institutional capacities to consolidate educational innovation with equity and sustainability. Furthermore, the study underscores the importance of addressing rural-urban disparities, ensuring universal accessibility, and promoting personalized mentoring strategies to support vulnerable students. These elements are crucial for bridging opportunity gaps and encouraging entrepreneurial potential among scholarship holders, thereby contributing to broader goals of social inclusion and development.