<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2215-3470</journal-id>
<journal-title><![CDATA[Uniciencia]]></journal-title>
<abbrev-journal-title><![CDATA[Uniciencia]]></abbrev-journal-title>
<issn>2215-3470</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional, Costa Rica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2215-34702025000100032</article-id>
<article-id pub-id-type="doi">10.15359/ru.39-1.3</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Desarrollo de competencias: inteligencia artificial y aprendizaje automático en prácticas supervisadas de estudiantes en computación]]></article-title>
<article-title xml:lang="en"><![CDATA[Development of competencies: artificial intelligence and machine learning in supervised internships of computer science students]]></article-title>
<article-title xml:lang="pt"><![CDATA[Desenvolvimento de competências: inteligência artificial e aprendizagem automática em estágios supervisionados de estudantes em ciências da computação.]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Villalobos-Murillo]]></surname>
<given-names><![CDATA[Johnny]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Garita-González]]></surname>
<given-names><![CDATA[Gabriela]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Alfaro Ramírez]]></surname>
<given-names><![CDATA[Byron Jesús]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional Escuela de Informática y Computación Laboratorio de Imágenes]]></institution>
<addr-line><![CDATA[Heredia ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional Escuela de Informática y Computación Laboratorio de Imágenes]]></institution>
<addr-line><![CDATA[Heredia ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional Escuela de Informática y Computación Laboratorio de Imágenes]]></institution>
<addr-line><![CDATA[Heredia ]]></addr-line>
<country>Costa Rica</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<volume>39</volume>
<numero>1</numero>
<fpage>32</fpage>
<lpage>50</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S2215-34702025000100032&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S2215-34702025000100032&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S2215-34702025000100032&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  (Objetivo)  El objetivo de esta investigación se centra en el desarrollo de competencias en inteligencia artificial y aprendizaje automático para el estudiantado que desee experimentar en la creación de modelos de aprendizaje automático.  (Metodología)  El estudio se realizó en el Laboratorio de Procesamiento de Imágenes Digitales de la Universidad Nacional de Costa Rica, durante el I ciclo del 2023 por personas académicas y estudiantes que cursan la asignatura Práctica Profesional; se enmarca en una investigación aplicada y exploratoria, en la cual se creó y utilizó una metodología que muestra el paso a paso para clasificar mamografías en benignas y malignas, desarrollando un modelo de aprendizaje automático. Se utilizó un conjunto de datos compuesto por 118 imágenes de mamografías, previamente diagnosticadas.  (Resultados)  Los resultados se dividen en dos áreas. La primera es la sistematización del proceso de enseñanza y aprendizaje basado en competencias, resultados de aprendizaje y la rúbrica para la evaluación de los aprendizajes, en las áreas de inteligencia artificial, aprendizaje automático y redes neuronales. La segunda involucra los productos generados al construir una aplicación informática Modelo de clasificación de mamografías, el cual integra una red neuronal de convolución que incluye la transformación de imágenes, la creación y el entrenamiento.  (Conclusiones)  Se concluye que, desde la academia, se pueden innovar en modelos de aprendizajes centrados en el estudiantado, para fortalecer habilidades que mejoren su perfil profesional. Desde el campo médico, la clasificación de mamografías es una oportunidad para el desarrollo de competencias en un entorno real.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  (Objective)  The objective of this research focuses on developing competencies in artificial intelligence and machine learning for students wishing to experiment in the creation of machine learning models.  (Methodology)  The study was conducted in the Digital Image Processing Laboratory of Universidad Nacional de Costa Rica, during the first cycle of 2023 by faculty and students doing their internships. For this exploratory-applied research, a step-by-step methodology was created and used to classify benign and malignant mammograms by developing a machine-learning model. A data set comprised of 118 previously diagnosed mammogram images was used.  (Results)  Results are divided into two areas. The first area is the systematization of the teaching and learning process based on competencies, learning results, and the rubric to evaluate learning in artificial intelligence, machine learning, and neural networks. The second area involves the products generated by developing the computer application Mammography Classification Model, which integrates a convolution neural network that includes image transformation, creation, and training.  (Conclusions)  It is concluded that, in the academic field, student-centered learning models can be innovated to strengthen skills that enhance their professional profile. In the medical field, the classification of mammograms is an opportunity to develop competencies in a real environment.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo  (Objetivo)  O objetivo desta pesquisa centra-se no desenvolvimento de competências em inteligencia artificial e aprendizagem automática para estudantes que desejam experimentar a criação de modelos de aprendizagem automática.  (Metodologia)  O estudo foi realizado no Laboratório de Processamento Digital de Imagens da Universidade Nacional da Costa Rica, durante o I ciclo de 2023, por acadêmicos e estudantes da disciplina Prática Profissional; faz parte de uma pesquisa aplicada e exploratória, na qual foi criada e utilizada uma metodologia que mostra o passo a passo para classificar mamografias em benignas e malignas, desenvolvendo um modelo de aprendizagem automática. Foi utilizado um conjunto de dados composto por 118 imagens de mamografia previamente diagnosticadas.  (Resultados)  Os resultados estão divididos em duas áreas. A primeira é a sistematização do processo de ensino e aprendizagem com base nas competências, nos resultados de aprendizagem e na rubrica de avaliação da aprendizagem, nas áreas de inteligência artificial, aprendizagem automática e redes neurais. A segunda envolve os produtosgerados pela construção de um aplicativo computacional Mammography Classification Model, que integra uma rede neural de convolução que inclui a criação, o treinamento e a transformação de imagens.  (Conclusões)  Concluise que, a partir da instituição, modelos de aprendizagem voltados para os alunos podem ser inovados, para fortalecer competências que melhorem seu perfil profissional. Na área médica, a classificação das mamografias é uma oportunidade para desenvolver habilidades em ambiente real.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales de convolución]]></kwd>
<kwd lng="es"><![CDATA[conocimiento]]></kwd>
<kwd lng="es"><![CDATA[computación e informática]]></kwd>
<kwd lng="en"><![CDATA[Artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[convolution neural networks]]></kwd>
<kwd lng="en"><![CDATA[knowledge]]></kwd>
<kwd lng="en"><![CDATA[Computer Science and Information Technology]]></kwd>
<kwd lng="pt"><![CDATA[inteligência artificial]]></kwd>
<kwd lng="pt"><![CDATA[aprendizagem automática]]></kwd>
<kwd lng="pt"><![CDATA[redes neurais de convolução]]></kwd>
<kwd lng="pt"><![CDATA[conhecimento]]></kwd>
<kwd lng="pt"><![CDATA[computação e informática]]></kwd>
</kwd-group>
</article-meta>
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