Mapeo de la investigación en inteligencia artificial aplicada a la enseñanza-aprendizaje en educación superior: Un análisis bibliométrico
DOI:
https://doi.org/10.62776/rse.v2i1.19Palabras clave:
Inteligencia artificial, educación superior, enseñanza-aprendizaje, análisis bibliométrico, aprendizaje personalizadoResumen
Este artículo presenta un análisis bibliométrico exhaustivo de la investigación en inteligencia artificial (IA) aplicada a la enseñanza-aprendizaje en educación superior, con el objetivo de mapear los principales enfoques, tendencias y áreas emergentes. Entre estas, destacan la ética en el uso de IA, la personalización del aprendizaje mediante algoritmos adaptativos y la adopción de tecnologías inmersivas como la realidad aumentada. Basado en 2,440 publicaciones indexadas en Scopus hasta julio de 2024, el estudio identifica un crecimiento sostenido en la producción científica desde 2010, con un acelerado interés desde 2015, lo que refleja su creciente relevancia en el ámbito académico. Los resultados muestran una concentración de la investigación en un número reducido de autores y países, indicando una alta especialización e interdisciplinariedad. Temas como "machine learning", "e-learning" y "deep learning" emergen como motores principales que impulsan la innovación educativa. Sin embargo, el análisis también revela limitaciones, como la dependencia exclusiva de Scopus y la ausencia de estudios cualitativos complementarios. A pesar de los avances, persisten desafíos relacionados con la integración efectiva de la IA en contextos educativos diversos. Este estudio contribuye a una comprensión integral del estado actual de la investigación en IA en educación superior y señala direcciones futuras para fortalecer esta línea de investigación.
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