Confianza docente en sistemas de inteligencia artificial educativa: un modelo explicativo desde la epistemología sociotécnica/Teacher Trust in Artificial Intelligence Systems: an explanatory model from a sociotechnical epistemology
Resumen
Introducción: La incorporación de la inteligencia artificial en la educación superior exige comprender cómo se configura la confianza docente hacia estos sistemas, especialmente en contextos públicos latinoamericanos.
Objetivo: Analizar los factores que influyen en la confianza docente hacia los sistemas de inteligencia artificial educativa.
Métodos: Se desarrolló un estudio cuantitativo, transversal y explicativo con docentes de tres universidades públicas de la Península de Yucatán, México. Se aplicó la Escala de Confianza Epistémica en la Inteligencia Artificial Educativa (ECE-IAE), estructurada en tres dimensiones: instrumental, crítica y reflexiva. Se realizaron análisis factorial exploratorio y confirmatorio, así como modelos de regresión lineal múltiple.
Resultados: Se confirmó la estructura tridimensional del constructo. La confianza instrumental presentó niveles más elevados, mientras que la crítica y reflexiva mostraron mayor dispersión. La competencia digital autopercibida y la formación específica en inteligencia artificial emergieron como predictores significativos de las tres dimensiones.
Conclusiones: La confianza docente en la inteligencia artificial es un fenómeno multidimensional condicionado por factores formativos y contextuales. El modelo validado ofrece bases empíricas para orientar políticas institucionales y programas de desarrollo profesional orientados a una adopción crítica y reflexiva de la inteligencia artificial educativa.
Palabras clave: enseñanza superior; formación; inteligencia artificial; tecnología educacional
ABSTRACT
Introduction: The integration of artificial intelligence into higher education requires an understanding of how teacher trust in these systems is shaped, particularly in Latin American public contexts.
Objective: To analyze the factors influencing teacher trust in educational artificial intelligence systems.
Methods: A quantitative, cross-sectional, explanatory study was conducted with faculty members from three public universities on the Yucatán Peninsula, Mexico. The Epistemic Trust in Educational Artificial Intelligence Scale (ECE-IAE) was administered, structured across three dimensions: instrumental, critical, and reflexive. Exploratory and confirmatory factor analysis were performed, along with multiple linear regression models.
Results: The three-dimensional structure of the construct was confirmed. Instrumental trust showed the highest levels, while critical and reflexive trust exhibited greater dispersion. Self-perceived digital competence and specific training in artificial intelligence emerged as significant predictors across all three dimensions.
Conclusions: Teacher trust in artificial intelligence is a multidimensional phenomenon shaped by educational and contextual factors. The validated model provides empirical foundations to guide institutional policies and professional development programs aimed at a critical and reflexive adoption of educational artificial intelligence.
Keywords: artificial intelligence; educational technology; higher education; training
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Derechos de autor 2026 Victor del Carmen Avendaño Porras
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
