In a context where artificial intelligence (AI) is transforming higher education, this study analyzes how students’ perceptions of AI influence their academic integrity (INA), with a focus on sustainable education. Through a correlational-explanatory analysis based on Structural Equation Models (SEMs) applied to a sample of 659 students from 13 universities in Chile and Peru, it is observed that AI has a significant and direct impact on academic integrity in both countries (β = 0.44). In Peru, the most influential dimension is trust in education (λ = 0.86), followed by social, economic, security, and risk implications (λ = 0.78), while attitudes towards AI also have a direct impact on integrity factors (β = 0.15). In Chile, the dimensions of trust in education (λ = 0.83) and social and economic impact (λ = 0.79) are most relevant, and the relationships between the dimensions of academic integrity such as justice, respect, and responsibility (λ = 0.71) are stronger. The study highlights the importance of incorporating AI literacy into educational curricula and developing regulatory frameworks that promote its ethical use, linking these actions to sustainable education. The findings highlight the need for sustainable educational approaches that enhance understanding of AI and ensure that its use in academia is beneficial, ethical, and contributes to sustainable development.

As Latin American universities pursue digitally and environmentally sustainable teaching models, understanding why students adopt generative AI is essential. We analyzed data from undergraduate students (n = 792) across five Latin American countries (Peru, Chile, Bolivia, Argentina, and Colombia). Grounded in the diffusion of innovations theory, the study evaluated the effects of relative advantage, compatibility, complexity, trialability, and observability on attitudes towards ChatGPT and examined the effect of attitude on intention to use among higher education students in the region. The reliability and validity of the measurement scale were confirmed, and structural relationships were tested using partial least squares structural equation modeling (PLS-SEM). The model explained 58.1% of the variance in attitude: relative advantage (β = 0.247) and compatibility (β = 0.246) exerted the largest effects, followed by trialability (β = 0.223) and observability (β = 0.167); complexity showed a weaker yet significant effect (β = 0.118). Attitude strongly predicted the intention to use ChatGPT (β = 0.777), accounting for 60.4% of its variance. All paths were significant (p < 0.001), and psychometric indicators exceeded recommended thresholds. These findings indicate that student adoption is driven more by perceived academic benefits and alignment with existing learning routines than by technical ease. Highlighting concrete, ethically delineated use cases and providing guided institutional spaces for experimentation may accelerate the responsible, long-term adoption of generative AI in quality higher education.

En este artículo proponemos el concepto de régimen urbano especulativo para enfrentar las críticas al concepto de informalidad, y para describir las condiciones contemporáneas de producción de espacio urbano en el Perú desde el 2000. Para tal fin, se realizó un estudio comparativo de tres áreas de expansión en Arequipa, Lima y Tacna en el Perú, donde se aplicaron 91 entrevistas semiestructuradas, así como análisis de información secundaria y socioespacial. Los resultados muestran que los modos de producción de espacio urbano en las tres ciudades del Perú están sustentados en la disputa del suelo con fines especulativos. En este proceso participan de manera sinérgica y con acuerdos sociales y legales, el aparato estatal, empresa privada, redes criminales y asociaciones civiles. Los hallazgos permiten discutir el concepto de “régimen urbano especulativo” como una apuesta por explicar la complejidad de las relaciones de poder involucradas en la producción de suelo urbano en los márgenes de las ciudades peruanas.

El estudio analiza las actitudes y comprensión de estudiantes universitarios en Perú hacia la Inteligencia Artificial (IA). A nivel metodológico se siguió la ruta cuantitativa-descriptiva diseñando una encuesta, revela que la mayoría de los estudiantes tienen un conocimiento básico de la IA, pero también expresan preocupaciones sobre su impacto en el empleo y la sociedad. La encuesta fue aplicada a 392 personas siendo validadas para el estudio 379 cuestionarios. Los resultados determinan que los estudiantes de pre y posgrado muestran curiosidad por entender cómo la IA podría influir en sus carreras y en la sociedad en general. Este estudio ofrece una perspectiva valiosa sobre cómo la IA es percibida y entendida por la próxima generación de profesionales peruanos, señalando la importancia de promover la educación y el debate sobre este tema en el ámbito universitario.

The aim of this article was to provide a comprehensive review of the factors that influence the risk of pests in olive cultivation on a global scale. Two different analytical methods were used: a bibliometric review and a systematic review. The first method, based on the Scopus database and supported by analytical tools such as VOSviewer and Bibliometrix, allowed for the identification and analysis of 1060 publications, with a particular focus on the 445 most cited from 2014 to 2023. This bibliometric exploration highlighted the main contributions and trends in research on pest management in olive growing. On the other hand, the systematic review was carried out through a triangulation of databases, including Scopus, EBSCO, and Web of Science, following the PRISMA protocol to ensure a rigorous methodological process. A total of 27 scientific studies were selected that provided a detailed and relevant view of pest control practices. The results of this systematic analysis show the critical importance of the controlled and judicious use of pesticides in olive cultivation to prevent adverse effects on human health, biodiversity, and the environment. They also highlight the need to promote sustainable cultivation practices that reduce dependence on agrochemicals, thereby improving soil health and the quality of the harvested product.