CAITIZEN extendido: estudio PLS-SEM de la innovación en ciudadanía sostenible asistida por IA
DOI:
https://doi.org/10.55965/setp.6.11.a4Palabras clave:
ciudadanía sostenible asistida por ia, innovación para el desarrollo sostenible, alfabetización crítica en inteligencia artificial, pls-sem, educación superiorResumen
Contexto. La inteligencia artificial transforma la educación superior al reconfigurar el aprendizaje, la creatividad, la toma de decisiones y la participación cívica. Este estudio examina CAITIZEN —Ciudadanía Asistida por Inteligencia Artificial para una Formación Sostenible, Ética y en Red— como modelo extendido para validar la ciudadanía sostenible asistida por IA como innovación para el desarrollo sostenible, alineada con el ODS 4 y el ODS 9.
Problema. Aunque el modelo CAITIZEN original fue fundamentado cualitativamente como marco ético–cognitivo–social, su capacidad explicativa y predictiva no había sido probada empíricamente. La educación en IA aún prioriza eficiencia, automatización y adopción técnica, con evidencia limitada sobre cómo alfabetización crítica en IA, ética, justicia de datos, colaboración humano–IA y metacognición en prompts contribuyen a dicha ciudadanía.
Propósito. Este estudio valida el modelo CAITIZEN extendido mediante PLS-SEM, examinando cómo CAIL habilita EAR, AFDJ, HAIC y MTPP, y cómo estas capacidades predicen CAITIZEN.
Metodología. Este estudio parte de una investigación cualitativa previa realizada en Guadalajara, Jalisco, México, durante julio–diciembre de 2025, y lo complementa con un diseño cuantitativo explicativo-predictivo mediante SmartPLS 4.1.1.8 para evaluar constructos reflectivos y relevancia predictiva mediante PLSpredict.
Hallazgos teóricos y prácticos. Los resultados muestran que CAIL predice significativamente EAR, AFDJ, HAIC y MTPP, confirmando su papel como antecedente fundacional. AFDJ, HAIC y MTPP predicen significativamente CAITIZEN, mientras EAR no muestra efecto directo. La relevancia predictiva se confirma porque todos los valores Q²_predict son positivos y las diferencias PLS-LM RMSE favorecen a PLS-SEM.
Originalidad. El estudio transforma el modelo cualitativo CAITIZEN en una estructura explicativa-predictiva validada empíricamente.
Conclusiones y limitaciones. El modelo CAITIZEN extendido ofrece un marco medible para educación responsable en IA e innovación sostenible. Sus limitaciones incluyen muestreo no probabilístico, diseño transversal y muestra estudiantil.
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