Exploring Pre-Service Teachers' Intention to Use Artificial Intelligence in Education: A Structural Equation Modeling Approach Based on UTAUT2
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Date
2024
Authors
Juan José Victoria Maldonado, Santiago Alonso García, Alejandro Martínez Menendez and Manuel Enrique Lorenzo Martín
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Abstract
Abstract The growing relevance of Artificial Intelligence (AI) in
education necessitates a better understanding of its acceptance
among future educators. This study investigates the factors
influencing pre-service teachers' intention to use AI, employing
the UTAUT2 model extended with sociodemographic moderators.
A cross-sectional quantitative design was applied to a sample of
908 undergraduate students from Early Childhood and Primary
Education programs in Andalusia, Spain. Structural equation
modeling results reveal that performance expectancy, effort
expectancy, and habitual use are significant predictors of
behavioral intention toward AI use. In contrast, the proposed
moderating effects of gender and academic year were found to be
non-significant. Findings highlight the pivotal role of habitual
engagement with AI while questioning the effectiveness of current
curricular approaches in promoting its pedagogical use, as
academic progression showed no moderating influence. The study
emphasizes the need for targeted training and curricular updates
to foster meaningful AI integration in teacher education.
Index Terms Artificial Intelligence, teacher training, technology
integration, UTAUT2, pre-service teachers, moderating
variables, structural equation modeling.