La dependencia de la inteligencia artificial como predictor de la autoeficacia académica en estudiantes de ingeniería de una universidad pública peruana
Artificial intelligence dependency as a predictor of academic self-efficacy among engineering students at a peruvian public universityContenido principal del artículo
Introducción: La integración de la inteligencia artificial en la educación superior ha transformado los procesos de aprendizaje, generando preocupaciones sobre sus posibles efectos en la autonomía y el desempeño de los estudiantes. Objetivo: Determinar la capacidad predictiva de la dependencia de la inteligencia artificial sobre la autoeficacia académica en estudiantes de ingeniería de una universidad peruana. Metodología: Se realizó un estudio cuantitativo con diseño predictivo transversal. La muestra estuvo conformada por 134 estudiantes de dos facultades de ingeniería de una universidad pública. Se aplicaron la Escala de Dependencia a la Inteligencia Artificial y un instrumento de Autoeficacia Académica, ambos con adecuados niveles de validez y confiabilidad. Resultados: La dependencia de la inteligencia artificial predice de manera significativa la autoeficacia académica con un efecto negativo moderado (β = −0.457; p < .001). El modelo explicó el 20.9 % de la varianza de la autoeficacia (R² = 0.209), evidenciando que un mayor nivel de dependencia se asocia con menor percepción de autoeficacia académica. Conclusiones: La dependencia de la inteligencia artificial es un predictor significativo y negativo de la autoeficacia académica en estudiantes de ingeniería. Estos hallazgos sugieren la necesidad de promover estrategias pedagógicas que fortalezcan la autonomía, el pensamiento crítico y la confianza en las propias capacidades en contextos educativos mediados por IA.
Introduction: The integration of artificial intelligence (AI) into higher education has transformed learning processes, raising concerns regarding its potential effects on student autonomy and performance. Objective: To determine the predictive power of artificial intelligence dependency on academic self-efficacy among engineering students at a Peruvian public university. Methodology: A quantitative study was conducted using a cross-sectional predictive design. The sample consisted of 134 students from two engineering faculties at a public university. The Artificial Intelligence Dependency Scale and an Academic Self-Efficacy instrument were administered, both of which demonstrated adequate levels of validity and reliability. Results: Artificial intelligence dependency significantly predicts academic self-efficacy with a moderate negative effect (β = −.457; p < .001). The model accounted for 20.9% of the variance in self-efficacy (R² = .209), indicating that higher levels of dependency are associated with lower perceived academic self-efficacy. Conclusions: Artificial intelligence dependency is a significant negative predictor of academic self-efficacy in engineering students. These findings suggest the need to foster pedagogical strategies that strengthen autonomy, critical thinking, and confidence in individual capabilities within AI-mediated educational environments.
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