Ricardo Villarroel-Molina

Artificial intelligence (AI) has emerged as a transformative axis in the educational field, promoting new forms of personalized learning, institutional management, and automated assessment. This study aims to systematically and critically analyze the main technological advances, ethical challenges, and pedagogical perspectives surrounding the implementation of AI in education. A structured literature review was conducted following the PRISMA 2020 guidelines, covering 43 articles published between 2021 and 2025, selected from renowned academic databases. The findings reveal an increasing use of adaptive platforms, intelligent tutoring systems, and predictive analytics, as well as concerns related to privacy, algorithmic bias, and the redefinition of the teacher's role. The conclusions highlight the need for regulatory frameworks and human-centered pedagogical approaches that promote a critical, inclusive, and ethical integration of AI in educational settings.

The relationship between organizational climate, which encompasses shared attitudes and values in an organization, and job satisfaction, which reflects employees' perceptions of their work, was examined in the Public Mobility Company of the Commonwealth of Cotopaxi. Using a quantitative approach and a descriptive correlational methodology, questionnaires from Araujo et al. (2011) to evaluate the organizational climate and Sotomayor, F. (2013) for job satisfaction of 73 employees. The results revealed a moderate relationship (Spearman coefficient of 0.603) between the variables, both with high scores (4/5), indicating a positive environment and satisfied employees. Se examinó la relación entre clima organizacional, que abarca actitudes y valores compartidos en una organización, y la satisfacción laboral, que refleja las percepciones de los empleados sobre su trabajo en la Empresa Pública de Movilidad de la Mancomunidad de Cotopaxi. Utilizando un enfoque cuantitativo y una metodología descriptiva correlacional, se aplicaron cuestionarios de Araujo et al. (2011) para evaluar el clima organizacional y de Sotomayor, F. (2013) para satisfacción laboral a 73 colaboradores. Los resultados revelaron una relación moderada (coeficiente de Spearman de 0.603) entre las variables, ambas con altas puntuaciones (4/5), indicando un ambiente positivo y empleados satisfechos.

Cybersecurity analysis using data science and machine learning plays a crucial role in today's digital era, where information security has become imperative for organizations. This study focuses on the intersection between cybersecurity and data science, using machine learning methods and text analytics to understand and strengthen defenses against threats. The importance of TF-IDF (Term-Inverse Document Frequency) is highlighted as a tool to evaluate the relevance of terms in documents and its application in vulnerability classification.The Multinomial Naive Bayes classifier is presented as an efficient tool in text classification, calculating probabilities of belonging to specific classes based on the frequency of terms. The essential formulas used in this algorithm, such as conditional probability and multinomial distribution, are detailed.The KDD (Knowledge Discovery in Databases) methodology guides the process, from datacollection on platforms like Kaggle to data selection, cleaning and transformation. The use of `TfidfVectorizer` facilitates the discretization of text data, and the `GridSearchCV` method optimizes the model's hyperparameters, achieving an accuracy of 97.36%. Finally, the confusion matrix reveals good overall performance, although areas for improvement are identified, especially in the 'High' class.

Este libro trata sobre conceptos elementales junto con scripts cortos de código basado en R Project para hacer análisis inteligente de datos. La relación entre la teoría y la práctica es fundamental en la comprensión de una disciplina, así la aplicación de procedimientos y funciones específicas en tareas elementales es el propósito de este texto. La idea central del texto tiene origen en la asignatura denominada “Análisis Inteligente de Datos”, una cátedra en la que el profesor aporta con elementos fundamentales basados en conceptos y ejercicios prácticos usando R Project. Hoy en día, la disponibilidad de herramientas para la minería de datos es sin duda muy grande. Usuarios con conocimientos básicos pueden aprovechar de utilitarios intuitivos implementados en poderosos entornos de desarrollo. Nosotros hemos querido dar un enfoque al texto hacia una audiencia con mayor relación a la programación y software. Específicamente que constituya una guía básica para estudiantes que inician en el campo de la Inteligencia de datos.