En el ámbito de la gestión empresarial, la integración de tecnologías emergentes juega un rol importante en las actividades administrativas y operativas. La innovación, por otro lado, se convierte en un factor clave para optimizar los procesos en estas organizaciones. El propósito de esta investigación es examinar el papel de las tecnologías emergentes como elemento clave de innovación y competitividad. Se lleva a cabo una revisión sistemática de literatura, empleando un enfoque cualitativo- descriptivo, con el fin de investigar el impacto de la implementación de tecnologías emergentes en la competitividad empresarial. Se examina tanto el proceso productivo como las acciones de marketing correspondientes. Además, se presentan casos exitosos de empresas que adoptan tecnologías emergentes y se utiliza la metodología de triangulación para analizar estos casos, este artículo revela los factores más relevantes que influyen directamente en la posición de la marca en el mercado tras la adopción de estas tecnologías emergentes. Estos hallazgos proporcionan aportes empíricos valiosos y contribuyen a la comprensión de cómo las tecnologías emergentes pueden impulsar la innovación y la competitividad en el ámbito empresarial.

Objective: The objective of this study is to investigate the role of computational thinking in teacher training, in order to identify the main practices and trends in this field and examine their implications for technological education. Theoretical Framework: This study presents a review of the key concepts related to computational thinking in the educational field. Existing definitions and approaches are highlighted, as well as models and theories that support the integration of computational thinking in teacher training. Method: The methodology adopted included an exhaustive search in five academic databases to identify relevant studies published in the last five years. Inclusion and exclusion criteria were applied to select the relevant articles, and a systematic analysis was carried out to synthesize the findings. Results and Discussion: The results revealed a variety of experiences and practices related to computational thinking in teacher training. In the discussion section, these results were contextualized in the light of the theoretical framework, highlighting the implications for educational practice and identifying areas of future research. Implications of the research: The practical and theoretical implications of this study are discussed, providing information on how the results can influence the teaching and learning of computational thinking in the context of teacher training. These implications could include the design of professional development programs and the development of technology-focused educational curricula. Originality/Value: This study contributes to the literature by providing an updated view of the state of computational thinking in teacher training. Its relevance and value are evidenced in its ability to inform the design of effective educational interventions in the field of technological education.

In this article, we present a systematic review of the literature that explores the impact of Machine Learning as a teaching strategy in the educational field. Machine Learning, a branch of artificial intelligence, has gained relevance in teaching and learning due to its ability to personalize education and improve instructional effectiveness. The systematic review focuses on identifying studies investigating how Machine Learning has been used in educational settings. Through a thorough analysis, its impact on various areas related to teaching and learning, including student performance, knowledge retention, and curricular adaptability, is examined. The findings of this review indicate that Machine Learning has proven to be an effective strategy for tailoring instruction to individual student needs. As a result, engagement and academic performance are significantly improved. Furthermore, the review underscores the importance of future research. This future research will enable a deeper understanding of how Machine Learning can optimize education and address current challenges and emerging opportunities in this evolving field. This systematic review provides valuable information for educators, curriculum designers, and educational policymakers. It also emphasizes the continuing need to explore the potential of Machine Learning to enhance teaching and learning in the digital age of the 21st century.

Este artículo describe la integración del pensamiento computacional y el enfoque STEAM (Science, Tecnology, Engineer, Art, Math) con la finalidad de fortalecer las competencias matemáticas que con el paso del tiempo se han debilitado. Teniendo en cuenta el contexto educativo y numerosas investigaciones se evidenció que los estudiantes carecen de las habilidades del pensamiento de orden superior. El estudio se desarrolló en tres fases: diagnóstico de competencias, diseño y aplicación de estrategias y evaluación de competencias. La investigación se realizó bajo el paradigma positivista con enfoque cuantitativo y método empírico analítico mediante un tipo de investigación descriptiva, utilizando un diseño cuasi experimental con grupo experimental y grupo de control con pre y posprueba con la siguiente estructura: G₁ O₁ X O₂ y G₂ O₃ - O₄ donde el objeto de estudio fueron estudiantes de décimo año de educación básica. La recolección de información se realizó mediante rúbricas semánticas, proyecto STEAM y evaluaciones formativas. Los datos se analizaron con técnicas paramétricas mediante la distribución de probabilidad T de Student donde se comprobó que los resultados obtenidos entre el grupo experimental y el de control fue estadísticamente significativa, demostrando que este tipo de propuestas pedagógicas permitieron fortalecer las competencias matemáticas.

Introduction. Educational robotics was consolidated as a key tool in STEM education (Science, Technology, Engineering, and Mathematics), by fostering technical, cognitive, and socio-emotional skills in students.Objectives. This study aimed to conduct a systematic review of approaches and trends in the use of educational robotics within STEM learning contexts, incorporating a perspective focused on educational management.Methods. A structured literature review methodology was applied, using recognized academic databases to identify relevant studies published between 2020 and 2024. The analysis included implementation strategies, technological tools, pedagogical models, reported benefits, and challenges. Results. The findings showed that educational robotics strengthened computational thinking, problem-solving abilities, student motivation, and collaborative learning. However, persistent challenges were identified, such as unequal access to technology, the need for teacher training, and the lack of unified curricular standards, which posed significant issues for institutional management.Conclusions. It was concluded that, in addition to constructivist and inquiry-based learning approaches, emerging trends included the integration of artificial intelligence and adaptive learning environments. The findings provided valuable insights for school leaders, educators, and researchers interested in incorporating educational robotics in STEM contexts from a strategic management perspective.

Generative Artificial Intelligence (GenAI) has attracted growing attention in higher education due to its potential to transform teaching, learning, assessment, and academic production. This article presents a systematic literature review focused on the use of GenAI in university settings, conducted following Kitchenham's methodological model. A total of 41 studies published between 2020 and 2025 were analyzed across major databases such as Scopus, Web of Science, Springer, and IEEE Xplore. The findings reveal a wide range of applications, including learning personalization, automated feedback, academic writing support, content generation, and optimization of teaching and administrative tasks. However, the review also identifies significant ethical, pedagogical, and epistemological challenges, such as academic integrity, algorithmic bias, and the urgent need to strengthen critical digital literacy among both instructors and students. This review systematizes 26 GenAI tools, 21 educational strategies, and 12 ethical implications, providing a comprehensive foundation for institutional policy development and future research. The study ultimately aims to promote the responsible, ethical, and pedagogically meaningful integration of emerging generative AI technologies within higher education.