Understanding AI adoption among secondary education teachers: A PLS-SEM approach

This study investigates the factors influencing the adoption of Artificial Intelligence (AI) by secondary school teachers in Catalonia. Using a Partial Least Squares Structural Equation Modelling (PLS-SEM) methodology, a conceptual model was analyzed that includes AI perception, AI knowledge, Genera...

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Authors: López Costa, Marta, Donate, Belén, Cabrera, Nati, Maina, Marcelo Fabián
Format: article
Status:Published version
Publication Date:2025
Country:España
Institution:Universitat Oberta de Catalunya (UOC)
Repository:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/153097
Online Access:https://hdl.handle.net/10609/153097
https://doi.org/10.1016/j.caeai.2025.100416
Access Level:Open access
Keyword:partial least squares structural equation modelling (PLS-SEM)
AI adoption
secondary school teachers
data literacy
AI knowledge
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oai_identifier_str oai:openaccess.uoc.edu:10609/153097
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repository_id_str
spelling Understanding AI adoption among secondary education teachers: A PLS-SEM approachLópez Costa, MartaDonate, Belén Cabrera, NatiMaina, Marcelo Fabiánpartial least squares structural equation modelling (PLS-SEM)AI adoptionsecondary school teachersdata literacyAI knowledgeThis study investigates the factors influencing the adoption of Artificial Intelligence (AI) by secondary school teachers in Catalonia. Using a Partial Least Squares Structural Equation Modelling (PLS-SEM) methodology, a conceptual model was analyzed that includes AI perception, AI knowledge, General data use, Applied data use, and STEM training as predictors of AI adoption. The results reveal that AI knowledge (β = .482, p < .001) and General data use (β = .288, p = .001) are the most significant and positive predictors of AI adoption. In contrast, AI perception shows a weak but statistically significant negative relationship (β = -.105, p = .022), while applied data use and STEM training do not present a significant direct effect. The model explains 30.5 % of the variance in AI adoption. These findings suggest that developing specific knowledge on how to use AI for content creation and competence in general data use is crucial to fostering AI adoption among secondary school teachers in the Catalan context. In addition, this explorative work provides the research community with evidence that key Data Literacy competencies significantly shape AI adoption.Elsevier202520252025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/10609/153097https://doi.org/10.1016/j.caeai.2025.100416reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)InglésComputers & Education. Artificial Intelligence, 2025, 8info:eu-repo/grantAgreement/2024/EDU145/23CC BY-NC-NDAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:openaccess.uoc.edu:10609/1530972026-05-28T12:42:01Z
dc.title.none.fl_str_mv Understanding AI adoption among secondary education teachers: A PLS-SEM approach
title Understanding AI adoption among secondary education teachers: A PLS-SEM approach
spellingShingle Understanding AI adoption among secondary education teachers: A PLS-SEM approach
López Costa, Marta
partial least squares structural equation modelling (PLS-SEM)
AI adoption
secondary school teachers
data literacy
AI knowledge
title_short Understanding AI adoption among secondary education teachers: A PLS-SEM approach
title_full Understanding AI adoption among secondary education teachers: A PLS-SEM approach
title_fullStr Understanding AI adoption among secondary education teachers: A PLS-SEM approach
title_full_unstemmed Understanding AI adoption among secondary education teachers: A PLS-SEM approach
title_sort Understanding AI adoption among secondary education teachers: A PLS-SEM approach
dc.creator.none.fl_str_mv López Costa, Marta
Donate, Belén
Cabrera, Nati
Maina, Marcelo Fabián
author López Costa, Marta
author_facet López Costa, Marta
Donate, Belén
Cabrera, Nati
Maina, Marcelo Fabián
author_role author
author2 Donate, Belén
Cabrera, Nati
Maina, Marcelo Fabián
author2_role author
author
author
dc.subject.none.fl_str_mv partial least squares structural equation modelling (PLS-SEM)
AI adoption
secondary school teachers
data literacy
AI knowledge
topic partial least squares structural equation modelling (PLS-SEM)
AI adoption
secondary school teachers
data literacy
AI knowledge
description This study investigates the factors influencing the adoption of Artificial Intelligence (AI) by secondary school teachers in Catalonia. Using a Partial Least Squares Structural Equation Modelling (PLS-SEM) methodology, a conceptual model was analyzed that includes AI perception, AI knowledge, General data use, Applied data use, and STEM training as predictors of AI adoption. The results reveal that AI knowledge (β = .482, p < .001) and General data use (β = .288, p = .001) are the most significant and positive predictors of AI adoption. In contrast, AI perception shows a weak but statistically significant negative relationship (β = -.105, p = .022), while applied data use and STEM training do not present a significant direct effect. The model explains 30.5 % of the variance in AI adoption. These findings suggest that developing specific knowledge on how to use AI for content creation and competence in general data use is crucial to fostering AI adoption among secondary school teachers in the Catalan context. In addition, this explorative work provides the research community with evidence that key Data Literacy competencies significantly shape AI adoption.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10609/153097
https://doi.org/10.1016/j.caeai.2025.100416
url https://hdl.handle.net/10609/153097
https://doi.org/10.1016/j.caeai.2025.100416
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Computers & Education. Artificial Intelligence, 2025, 8
info:eu-repo/grantAgreement/2024/EDU145/23
dc.rights.none.fl_str_mv CC BY-NC-ND
Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv CC BY-NC-ND
Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869414731533516800
score 15,81155

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