Conference Agenda

This study examines the statistical association between digital procurement intensity and budget execution performance in Peruvian public education at the subnational level during fiscal years 2023–2025. A quantitative, non-experimental, and correlational design was adopted using secondary data from two official open-data platforms: the Ministry of Economy and Finance and the Supervisory Agency for Public Procurement. The dataset integrated 2,136 entity-year procurement records with aggregated budget execution data from local governments. Pearson correlation and ordinary least squares regression were applied at the annual aggregate level (n = 3). Results reveal a strong and statistically significant positive correlation between the financial volume channeled through digital procurement and budget execution (r = 0.999, p = 0.026), whereas the number of procurement procedures showed no linear association. Regression estimates indicate that each additional billion soles managed through digital procurement is associated with a 9.23 percentage point increase in budget execution (R² = 0.998). The enactment of Law No. 32027 in 2025 introduced streamlined procurement modalities without altering aggregate financial volumes. Although the limited degrees of freedom and correlational design restrict causal inference, the findings provide quantitative evidence on an underexplored issue in Latin America and suggest that scaling financial volume—rather than increasing procedural frequency—may represent a relevant pathway for improving subnational educational spending efficiency in Peru.

This research addresses the adoption of immersive technologies in Higher Education, highlighting how Virtual Reality (VR) generates positive effects on engineering learning by enabling safe and accessible interaction with complex environments. The study is situated within the Environmental Management and Sustainable Development course of Industrial Civil Engineering at the Catholic University of Temuco, seeking to close gaps in competency assessment through measurements taken before and after the intervention. The methodology consisted of characterizing a student sample of 27 using Likert scale surveys of digital skills and knowledge before and after the VR activity. The students used the OpLab virtual laboratory and head-mounted displays (HMDs) to tour a production plant, where they had to identify environmental aspects and impacts, recognize stakeholders, and propose environmental management tools. The results revealed a significant positive impact: the ability to detect environmental aspects increased by 29%, while knowledge of management tools grew by 59%. Furthermore, confidence in proposing sustainable solutions doubled after the experience. It is concluded that VR not only facilitates the understanding of industrial systems that are difficult to visit due to cost or logistical challenges, but also promotes meaningful and reflective learning, better preparing future professionals for decision-making in complex scenarios.

This investigation examines the transformative potential of an innovative educational mobile application integrating augmented reality (AR) and artificial intelligence-powered learning analytics for enhancing secondary students' comprehension of nutritional science and physiological systems. Through a rigorous quasi-experimental design with 64 second-year secondary students from a Cajamarca educational institution in 2025, we implemented a four-week intervention using a validated 11-item assessment instrument (Cronbach's α = 0.768). Statistical analysis revealed substantial learning gains in the experimental group (M = 14.34, SD = 1.83) compared to traditional instruction (M = 10.19, SD = 1.60), with significant differences confirmed by Mann-Whitney U test (U = 53.00, p < 0.001). The analysis of covariance demonstrated a robust treatment effect (F = 56.87, p < 0.001) with a large effect size (η² = 0.48), indicating that 48% of the variance in learning outcomes was attributable to the technological intervention. Notably, the experimental cohort achieved a 5.21-point learning advancement, significantly outperforming the control group's 0.94-point gain. These findings provide compelling evidence that AI-enhanced AR applications represent a powerful pedagogical tool for complex scientific concepts, particularly in resource-constrained educational environments. The study makes a significant contribution to educational technology literature by demonstrating how integrated learning analytics with immersive technologies can substantially enhance conceptual understanding and learning outcomes in secondary science education.

The rapid adoption of artificial intelligence (AI) in higher education has transformed teaching and learning processes. However, the effective integration of AI into learning activities depends on multiple technological, individual, and institutional factors that have not yet been sufficiently explored in emerging educational contexts. The objective of this study is to analyze the factors that influence the effective integration of artificial intelligence in higher education learning and its impact on student engagement.

A quantitative, cross-sectional research design was employed using a structured questionnaire administered to a sample of 150 higher education students enrolled in engineering programs and business-related fields. The conceptual model integrates constructs derived from the Technology Acceptance Model (TAM), digital competence, institutional support, effective AI integration, and learning engagement. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the WarpPLS 7.0 software.

The results show that perceived ease of use and institutional support have a positive and statistically significant effect on the effective integration of artificial intelligence in learning, while digital competence does not exhibit a significant direct effect. In addition, effective AI integration positively and significantly influences student engagement. The model demonstrates acceptable predictive capability and an adequate overall fit for an exploratory study.

These findings contribute to the understanding of artificial intelligence adoption in higher education and offer practical implications for the design of institutional strategies aimed at promoting the meaningful and responsible use of AI in higher education.

This paper presents the design, implementation, and evaluation of a humanistic administrative chatbot developed for the Universidad de Ciencias y Humanidades (UCH) in Lima, Peru. While most AI-driven administrative systems in higher education prioritize efficiency, scalability, and response accuracy, this study proposes a complementary paradigm centered on relational accompaniment, ethical mediation, and institutional identity. Grounded in contemporary discussions on AI governance, large language models, and human-centered education, the chatbot was configured using Chatbase with a structured prompt engineering strategy aligned with UCH’ s educational principles. A quasi- experimental evaluation was conducted across two academic semesters (2024-2 and 2025-1), involving 85 students interacting with simulated administrative queries. Results indicate improvements in response time and user satisfaction, alongside high perceived warmth, clarity, and institutional alignment. The findings suggest that administrative AI systems can transcend purely functional roles and become instruments of formative mediation when designed under explicit humanistic principles. This model contributes to the discussion on responsible AI deployment in higher education, particularly in institutions committed to integral formation and social responsibility.

In recent years, generative language models such as ChatGPT, Bing Chat, Gemini, Claude and Monica have been integrated into the academic environment of universities. These technologies are commonly used in academic writing, text writing and data exploration.
Their constant presence in coursework has raised concerns about their true effect on fostering autonomous learning. This skill, which allows students to manage knowledge autonomously and issue their own criteria, is crucial in university education. However, whether these tools
favor or hinder this process remains a matter of debate. Faced with this gap, this study suggests an exhaustive analysis that gathers the most relevant advances in this area. To achieve this, a systematic search method is suggested that relies on recognized academic databases and precise selection criteria. In addition to collecting existing information, the main goal is to examine it in depth in order to understand the ways in which the scientific community discusses this topic. This research, in addition to establishing a solid foundation for future research, seeks to guide educators and institutional leaders who are determining the most effective way to employ. By better understanding how these tools influence student autonomy, it will be possible to propose more responsible and effective ways of integrating them into the educational process. This review will make it possible to recognize the true scope of generative models in university education and to propose strategies that strengthen autonomy without relying exclusively on these technological tools.