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Artificial Intelligence Adoption Amongst Digitally Proficient Trainee Teachers: A Structural Equation Modelling Approach

Author

Listed:
  • María Belén Morales-Cevallos

    (Faculty of Marketing and Communication, Universidad Ecotec, Via Principal Campus Ecotec Km 13.5, Samborondón 092302, Ecuador)

  • Santiago Alonso-García

    (Departament of Didactics and School Organization, Universidad de Granada, 18011 Granada, Spain)

  • Alejandro Martínez-Menéndez

    (Departament of Didactics and School Organization, Universidad de Granada, 18011 Granada, Spain)

  • Juan José Victoria-Maldonado

    (Departament of Didactics and School Organization, Universidad de Granada, 18011 Granada, Spain)

Abstract

The present study examines how pre-service teachers’ digital competence influences their acceptance and use of Artificial Intelligence (AI) in educational settings. Employing a quantitative approach via Structural Equation Modelling (SEM), the authors analyzed self-reported data from Early Childhood and Elementary Education students in Andalusian (Spain) universities. The findings indicate that professional engagement is associated with a critical assessment of AI, focusing on pedagogical and ethical considerations, whereas digital content creation skills promote a more positive and proactive attitude toward AI adoption. These results underscore the importance of teacher education programs that combine technical skills with critical thinking to foster responsible AI integration. This study acknowledges limitations, including its regional scope and cross-sectional design and recommends future longitudinal and comparative research to validate and expand these insights. By addressing these gaps, future studies could enhance our understanding of AI adoption in diverse educational contexts.

Suggested Citation

  • María Belén Morales-Cevallos & Santiago Alonso-García & Alejandro Martínez-Menéndez & Juan José Victoria-Maldonado, 2025. "Artificial Intelligence Adoption Amongst Digitally Proficient Trainee Teachers: A Structural Equation Modelling Approach," Social Sciences, MDPI, vol. 14(6), pages 1-23, June.
    • Handle: RePEc:gam:jscscx:v:14:y:2025:i:6:p:355-:d:1671151
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    References listed on IDEAS

    as
    1. Marko Sarstedt & Christian M. Ringle & Joseph F. Hair, 2022. "Partial Least Squares Structural Equation Modeling," Springer Books, in: Christian Homburg & Martin Klarmann & Arnd Vomberg (ed.), Handbook of Market Research, pages 587-632, Springer.
    2. repec:aoj:jeelre:v:11:y:2024:i:4:p:698-707:id:6097 is not listed on IDEAS
    3. Gerbing, David W & Anderson, James C, 1984. "On the Meaning of Within-Factor Correlated Measurement Errors," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 11(1), pages 572-580, June.
    4. William Black & Barry J. Babin, 2019. "Multivariate Data Analysis: Its Approach, Evolution, and Impact," Springer Books, in: Barry J. Babin & Marko Sarstedt (ed.), The Great Facilitator, pages 121-130, Springer.
    Full references (including those not matched with items on IDEAS)

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