Inteligencia artificial generativa y comprensión lectora en estudiantes universitarios
DOI:
https://doi.org/10.5281/zenodo.22852031Resumen
El objetivo de este trabajo fue determinar la relación entre la interacción académica con inteligencia artificial generativa (IAG) y la comprensión lectora en estudiantes universitarios. Se realizó un estudio cuantitativo, observacional, transversal y correlacional con 400 estudiantes mayores de 18 años de una universidad pública peruana, seleccionados mediante muestreo intencional por accesibilidad. Se aplicaron una escala de 20 ítems sobre interacción académica con IAG y una prueba de comprensión lectora con 10 indicadores dicotómicos. La estructura interna de ambas puntuaciones globales fue examinada mediante análisis factorial exploratorio y análisis paralelo. La consistencia interna fue adecuada para IAG (α = .858) y lectura (KR-20 = .787). Se identificó una asociación positiva y fuerte entre las puntuaciones globales, rₛ = .782, IC 95 % [.737, .819], p < .001. La relación fue mayor con la comprensión inferencial (rₛ = .720) que con la literal (rₛ = .609) y la crítica (rₛ = .571). Los seis dominios de IAG también presentaron asociaciones positivas con la lectura total. Se concluyó que una interacción académica más informada, frecuente y reflexiva con la IAG se relacionó con un mejor desempeño lector. El diseño transversal, el muestreo no probabilístico y las limitaciones de medición impiden establecer causalidad y restringen la generalización.
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