Algoritmos de aprendizaje para predecir el uso de la inteligencia artificial generativa en estudiantes universitarios

• José Clemente Mora Rosales https://orcid.org/0000-0002-6414-7947
• Raul Marcelo Benavides-Lara https://orcid.org/0000-0001-5560-845X
• Ana Paulina Alfaro-Rodríguez https://orcid.org/0009-0006-2874-4126
• Lucio Guadalupe Quirino-Rodríguez https://orcid.org/0009-0009-5128-1870

DOI: https://doi.org/10.31876/rcs.v32i.45743

Palabras clave: Inteligencia artificial, aprendizaje automático, educación superior, estudiantes universitarios, predicción

Resumen

El auge de la inteligencia artificial generativa ha transformado los procesos educativos en la educación superior generando oportunidades y retos en la formación universitaria. Este estudio tuvo como objetivo analizar los algoritmos de aprendizaje para predecir el uso de ChatGPT y Gemini en estudiantes de la Escuela Superior Politécnica de Chimborazo en Ecuador. Se empleó un enfoque cuantitativo, de diseño no experimental, transversal y correlacional, con una muestra de 699 estudiantes seleccionados mediante muestreo aleatorio simple. Los datos se recolectaron a través de un cuestionario validado estadísticamente y fueron procesados en SPSS, así como en Orange Data Mining. Los resultados mostraron que las variables sociodemográficas (sexo, edad, religión y semestre académico) no presentaron asociaciones significativas con la preferencia de uso, lo que indica un bajo poder explicativo. En contraste, la frecuencia de uso de inteligencia artificial generativa sí constituyó un predictor relevante, evidenciando la importancia del hábito como determinante de la adopción. Asimismo, los algoritmos Random Forest y AdaBoost obtuvieron los mejores niveles de precisión, confirmando la eficacia de los métodos de ensamble. Se concluye que las condiciones socioeducativas explican mejor el comportamiento estudiantil que los factores demográficos, consolidando a ChatGPT como la herramienta más utilizada.

Citas

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