Harnessing Artificial Intelligence in universities: motivation, intention, efficacy, and creativity

Komal Khalid; Juri Alsini

doi:10.46661/ijeri.12533

Autores/as

Komal Khalid King Abdulaziz University https://orcid.org/0000-0001-7220-6962
Juri Alsini King Abdulaziz University https://orcid.org/0009-0000-1172-4221

DOI:

https://doi.org/10.46661/ijeri.12533

Palabras clave:

Motivación para el aprendizaje de la Inteligencia Artificial, Eficacia en el aprendizaje de la Inteligencia Artificial, Intención de aprendizaje de la Inteligencia Artificial, Creatividad estudiantil, Teoría de la autoeficacia

Resumen

Esta investigación examina las complejas relaciones entre la motivación para el aprendizaje de la inteligencia artificial (AILM), la intención (AILI), la eficacia (AILE) y la creatividad estudiantil (SC) entre estudiantes de licenciatura y posgrado en Arabia Saudita. Se empleó un enfoque cuantitativo, recopilando datos a través de cuestionarios bilingües aplicados a 466 estudiantes de cinco universidades saudíes, utilizando escalas validadas para AILM (intrínseca/extrínseca), AILI, AILE y SC. Se utilizó el modelado de ecuaciones estructurales de mínimos cuadrados parciales (PLS-SEM) para investigar tanto los efectos directos como indirectos. El análisis confirmó las nueve hipótesis, demostrando que la AILM predice significativamente la AILI y la AILE, las cuales, a su vez, potencian la SC. La AILE y la AILI son mediadores entre la AILM y la SC, siendo la ruta secuencial (AILM→AILI→AILE→SC) el mediador más fuerte. La investigación recomienda la necesidad de reformas educativas en Arabia Saudita, en consonancia con la Visión 2030, que promuevan la motivación intrínseca y extrínseca, la traducción de la intención en conductas de aprendizaje persistentes, y la construcción de la eficacia en IA para liberar la creatividad.

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Citas

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