<?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>2518-8283</journal-id>
<journal-title><![CDATA[Educación Superior]]></journal-title>
<abbrev-journal-title><![CDATA[Edu. Sup. Rev. Cient. Cepies]]></abbrev-journal-title>
<issn>2518-8283</issn>
<publisher>
<publisher-name><![CDATA[Centro Psicopedagógico y de Investigación  en Educación Superior CEPIES-UMSA]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2518-82832025000100070</article-id>
<article-id pub-id-type="doi">10.53287/undf7848pz65o</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[La evaluación educativa en la era de la inteligencia artificial]]></article-title>
<article-title xml:lang="en"><![CDATA[Educational Assessment in the Age of Artificial Intelligence]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Llanos Torrico]]></surname>
<given-names><![CDATA[Boris Adolfo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,aff1  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2025</year>
</pub-date>
<volume>12</volume>
<numero>1</numero>
<fpage>70</fpage>
<lpage>85</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.bo/scielo.php?script=sci_arttext&amp;pid=S2518-82832025000100070&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.bo/scielo.php?script=sci_abstract&amp;pid=S2518-82832025000100070&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.bo/scielo.php?script=sci_pdf&amp;pid=S2518-82832025000100070&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El ensayo examina el impacto de la inteligencia artificial (IA) en la evaluación educativa en la Educación Superior, destacando su potencial paramejorar los procesos evaluativos mediante la precisión y personalización de la retroalimentación. A través de sistemas de evaluación automatizada, la IA facilita la corrección de exámenes y ensayos, optimizando la gestión de grandes volúmenes de trabajo académico. Este enfoque se relaciona con los principios del conexionismo (McClelland y Rumelhart, 1986), que explican el procesamiento de patrones complejos de datos educativos, y la teoría del conectivismo (Siemens, 2005), que analiza redes de datos para mapear el rendimiento estudiantil. Mientras el conexionismo explica cómo las redes neuronales procesan datos educativos, el conectivismo analiza cómo el aprendizaje se construye en redes digitales. Estudios como los de Balfour (2013) en entornos MOOC respaldan esta interrelación al proporcionar información valiosa que puede guiar estrategias pedagógicas más efectivas. La personalización del aprendizaje se resalta gracias a plataformas adaptativas que ajustan el contenido a las capacidades de los estudiantes. Sin embargo, se reconocen limitaciones, como la incapacidad de la IA para evaluar la creatividad y el pensamiento crítico, lo que plantea interrogantes sobre la validez de los resultados obtenidos automáticamente. Además, se abordan consideraciones éticas, como la privacidad de los datos y el consentimiento informado, subrayando la necesidad de un marco ético que proteja los derechos de los estudiantes. La interacción humana sigue siendo fundamental en la educación, sugiriendo que la IA debe complementar el rol de los profesores en la evaluación del aprendizaje.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The research examines the impact of artificial intelligence (AI) on educational assessment in Higher Education, highlighting its potential to improve assessment processes through the accuracy and personalization of feedback. Through automated assessment systems, AI facilitates the correction of exams and essays, optimizing the management of large volumes of academic work. This approach is related to the principles of connectionism (McClelland and Rumelhart, 1986), which explains the processing of complex patterns of educational data, and the theory of connectionism (Siemens, 2005), which analyzes data networks to map student performance. While connectionism explains how neural networks process educational data, connectionism analyzes how learning is constructed in digital networks. Studies such as those by Balfour (2013) in MOOC environments support this interrelationship by providing valuable information that can guide more effective pedagogical strategies. Personalization of learning is highlighted by adaptive platforms that match content to learners&#8217; abilities. However, limitations are acknowledged, such as the inability of AI to assess creativity and critical thinking, which raises questions about the validity of automatically obtained results. In addition, ethical considerations, such as data privacy and informed consent, are addressed, underscoring the need for an ethical framework that protects the rights of learners. Human interaction remains fundamental in education, suggesting that AI should complement the role of teachers in learning assessment.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Aprendizaje adaptativo]]></kwd>
<kwd lng="es"><![CDATA[ética en la educación]]></kwd>
<kwd lng="es"><![CDATA[evaluación educativa]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[personalización del aprendizaje]]></kwd>
<kwd lng="en"><![CDATA[Adaptive learning]]></kwd>
<kwd lng="en"><![CDATA[ethics in education]]></kwd>
<kwd lng="en"><![CDATA[educational assessment]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[personalization of learning]]></kwd>
</kwd-group>
</article-meta>
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