Olger Huamaní Jordan

The aim of this research was to determine the impact of the implementation of an information and communication technologies (ICT) program on strengthening the digital competencies of first-year students in the College of Education at a university in Lima, Peru. The study was conducted according to a quantitative applied approach, with a single-group pre-experimental design that employed a pretest and a posttest. The population consisted of the 156 incoming students in 2024; the sample, selected by non-probabilistic convenience sampling, was made up of 78 students from different majors. Data was collected by means of a structured questionnaire, validated by expert opinion and with an appropriate level of reliability. Descriptive and inferential statistics were used for data analysis, applying a Student’s t-test for related samples, with a level of significance of p < 0.05. The results showed a significant improvement between the pretest and the posttest. In the initial measurement, 38.5% of the students were at the basic level, 30.8% at the intermediate level, 28.2% at the advanced level and 2.6% at the expert level. Following the intervention, the basic level was reduced to 0% and the intermediate level to 1.3%, and accordingly, the advanced level increased to 62.8% and the expert level to 35.9%. Likewise, the t-test showed statistically significant differences between the two measurements. It was concluded that the implementation of the ICT program has a significant effect on the strengthening of the digital competencies, which highlights the importance of systematically incorporating ICT training programs in higher education, primarily in initial teacher training, in order to meet the needs of the current digital educational environment.

This bibliometric study examines artificial intelligence’s impact on sustainable water management through systematic analysis of 424 publications from Scopus, Web of Science, and IEEE Xplore following the 2020 PRISMA guidelines. Four analytical approaches were implemented: descriptive bibliometric characterization, VOSviewer network visualization, principal component analysis with Ward’s hierarchical clustering (86.58% variance explained, cophenetic correlation = 0.951), and qualitative synthesis. The results reveal exponential growth from 4 publications (2018) to 167 (2025) with geographic concentration in China (30.2%), the USA (9.7%), and India (8.0%). Collaboration networks exhibit pronounced fragmentation (density = 0.04, modularity = 0.78) with minimal North–South partnerships (12%). Critically, keyword analysis identifies five thematic clusters dominated by machine learning methodologies, whereas governance and equity dimensions appear fewer than eight times, revealing a fundamental gap wherein technical optimization proceeds without the institutional frameworks necessary for equitable water access. Multivariate analysis suggests that technological infrastructure capacity is a stronger correlate of research output than geographic water stress, based on the observed geographic distribution of high-output nations rather than direct operationalization of scarcity indicators. The qualitative synthesis revealed that 68% of the studies remained pilot-scale studies, 82% were concentrated in developed nations, and 66% cited data quality as the primary constraint. The bibliometric patterns suggest a pronounced orientation toward computational approaches, alongside paradoxical AI infrastructure water consumption that may partially offset conservation benefits. (Note: 2025 figures reflect early-access articles retrieved before the November 2024 search date and should be interpreted as partial-year estimates.) Achieving sustainable water management requires a reorientation emphasizing measurement infrastructure in data-poor contexts, North–South partnerships, and the integration of socioinstitutional dimensions as constitutive elements within technical development frameworks.

This research examines the relationships among economic value assessment, managerial perspectives, adoption willingness, and long-term AI utilization among organizational leaders in Peru. Using a quantitative cross-sectional design with covariance-based statistical analysis, data were collected from 390 management personnel (58.72% male, 41.28% female) representing diverse enterprises in northern Peru. Four hypotheses were evaluated concerning the effects of price–value on intention to use, attitude toward AI on intention to use, price–value on attitude toward AI, and intention to use on sustainable AI implementation. Results from partial-least-squares structural equation modeling showed significant direct effects of price–value on both intentions to use and attitudes toward AI, with attitudes also significantly influencing intention to use. The model exhibited exceptional explanatory power: price–value and attitudes explained 89.2% of the variance in intention to use, while intention to use accounted for 85.1% of the variance in sustainable AI use. These findings indicate that both economic considerations and psychological factors are critical for advancing sustainable AI adoption among business managers. Consequently, integrating price–value analyses and attitude-development components into AI implementation strategies is supported to enhance technology adoption success in business contexts.

The integration of artificial intelligence (AI) in higher education presents complex challenges for preservice teachers facing significant academic pressures. This study examines how academic workload affects AI adoption among preservice teachers in Peru and investigates the mediating roles of work stress and performance expectations in this relationship. AI models in this context encompass generative tools for content creation, automated assessment systems, and personalized learning platforms commonly utilized in educational settings. A cross-sectional study was conducted with 876 preservice teachers from 12 Peruvian universities. Data were collected through online questionnaires measuring workload, work stress, performance expectations, and AI usage patterns. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the hypothesized relationships. The findings confirmed that workload has a significant direct positive effect on AI model usage. Work stress significantly mediated the relationship between workload and AI usage. Additionally, work stress and performance expectations operate as serial mediators between workload and AI adoption, demonstrating how psychological mechanisms connect academic pressures to technological behaviors. This study provides novel insights into AI adoption among Peruvian preservice teachers, an underexplored population in educational technology research. The findings reveal the multifaceted relationship between academic workload and AI integration in teacher preparation. Educational institutions should implement stress management interventions including mindfulness training and time management workshops, while establishing realistic performance expectations through hands-on AI literacy programs that address both technical competencies and ethical considerations in technology adoption.

This study examines the determinants of the sustainable use of artificial intelligence (AI) among university professors in Peru. This research adopted a quantitative approach through a cross-sectional empirical–explanatory study, employing a structural equation model. Data were collected from 368 professors from eight Peruvian universities using a structured questionnaire that assessed six main constructs: attitude toward AI, prejudice against AI, facilitating conditions, use of AI, teaching concerns, and ethical perception. While the results reveal significant correlational relationships—with attitude toward AI, facilitating conditions, and prejudice against AI showing a significant association with its sustainable use, and the use of AI showing a significant relationship with professors’ ethical perceptions—the cross-sectional nature of this study precludes causal inferences. No significant relationship was found between the use of AI and teaching concerns. Additionally, demographic variables such as gender and age did not exhibit significant moderating effects. These findings contribute to understanding the factors related to the sustainable adoption of AI in higher education and provide valuable insights for the development of effective institutional strategies in the Latin American context.

The immersion of artificial intelligence (AI) in higher education presents significant challenges and opportunities. This study examines the acceptance of AI as a teaching strategy among university teachers, following the extended UTAUT2 model with the inclusion of the teacher skills and knowledge for technology integration (SKTI) construct. Employing a quantitative cross‐sectional research design, data were collected from 318 university teachers with prior experience using AI as a learning strategy through nonprobabilistic convenience sampling across 10 universities in northern Peru. Participants completed an online survey, and data were analyzed using descriptive statistics, Kruskal–Wallis tests with Dunn’s post hoc comparisons, and partial least squares structural equation modeling (PLS‐SEM). The results showed that performance expectancy ( β = 0.129 ^∗∗ ), hedonic motivation ( β = 0.167 ^∗∗ ), habit ( β = 0.405 ^∗∗∗ ), and SKTI ( β = 0.263 ^∗∗∗ ) had a positive influence on the behavioral intention to adopt AI as a teaching strategy. Additionally, behavioral intention ( β = 0.303 ^∗∗∗ ), facilitating conditions ( β = 0.115 ^∗ ), and habit ( β = 0.464 ^∗∗ ) determine the behavioral use of AI by teachers. The Kruskal–Wallis test revealed significant differences among age groups in the performance expectancy, social influence, habit, and behavioral intention constructs, with the 37‐ to 48‐year‐old age group showing higher average ranks. The discussion highlights that these findings suggest a positive adoption of AI among teachers, driven by individual and contextual factors, and challenges assumptions about the relevance of certain constructs in this specific context. In conclusion, this study represents a significant advancement in understanding the adoption of AI in university teaching and provides valuable guidance for practical implementation efforts.

Purpose: This document seeks to delve into the digital transformation of education during the COVID-19 pandemic, aiming to provide a comprehensive understanding of this evolving phenomenon's purpose and significance. Design/Methodology/Approach: The research approach undertaken is characterized by a non-experimental, documentary, exploratory, and descriptive study methodology, which involves an extensive examination of existing literature and data to gain insights into the digital transformation in education during the pandemic. Findings: The study's key findings revolve around the consensus in existing literature regarding the swift acceleration of the transformation of education from traditional face-to-face classes to virtual learning environments. It also highlights the implications of this transformation, particularly in reshaping teaching models and advocating for a hybrid approach encompassing both face-to-face and virtual learning. Research, Practical & Social implications: The implications of this research extend to informing educational institutions about the need for digital adaptation, guiding policymakers in supporting adaptable learning models, and empowering educators with a deeper understanding of the changing educational landscape. Moreover, it considers the broader societal impact, including equity and access issues in education. Originality/Value: This research is unique in its contribution to understanding the profound impact of the COVID-19 pandemic on education. It emphasizes the significance of adaptability and hybrid learning models while providing a foundation for future educational research and policy development.