Concordancia entre procesos de codificación cualitativa humana y codificación cualitativa automatizada basada en Inteligencia Artificial.
DOI:
https://doi.org/10.47058/joa12.8Palabras clave:
Investigación Cualitativa, codificación, inteligencia artificial, modelos de lenguajeResumen
La inteligencia artificial ofrece mejoras a los métodos tradicionales de investigación cualitativa, en lo que se refiere a la dificultad de trabajar con grandes volúmenes de datos y la fiabilidad de sus resultados. Esta investigación explora el potencial del análisis cualitativo automatizado impulsado por inteligencia artificial, mediante la comparación de dos procesos de codificación paralelos de datos no estructurados compuestos por respuestas textuales abiertas: uno automatizado mediante inteligencia artificial y otro tradicional mediante cognición humana. Se aplicó un cuestionario abierto a una muestra de 263 fanáticos de Disney para comprender sus percepciones sobre lo que representa la marca para ellos mismos, mediante una pregunta de libre respuesta. En el proceso de codificación automatizado se utilizó Python y un modelo de lenguaje denominado Llama 3.2-1b-Instruct. Los resultados mostraron que las codificaciones fueron muy similares en el conjunto de casos, pero de concordancia moderada en lo particular del caso a caso. Se concluye que la inteligencia artificial demuestra potencialidad en la eficiencia del análisis y la escalabilidad, pero evidenció sus límites al exponer inconsistencias en sus resultados, introduciendo redundancias en el proceso de codificación y destacando la necesidad de supervisión mediante procesos cognitivos humanos.
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Derechos de autor 2025 David Alejandro Álvarez Maldonado, Anna Milano-Meneses
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
