Perceptions of artificial intelligence in nursing students: A qualitative meta-synthesis based on the UTAUT2 model

Background: Artificial intelligence (AI) is rapidly transforming nursing education and clinical practice. Understanding students’ perceptions is essential for designing effective and equitable AI-enhanced learning strategies. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) offers a...

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Autores: Bonet Augè, Aida, Tort Nasarre, Glòria, Domènech-Sorolla, Jordina, Monistrol Ruano, Olga, Camí Garanto, Carla, Medel, Daniel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:dnet:.___________::a4ab7e1f1c4656697d266bccac1e55d6
Acceso en línea:https://doi.org/10.1016/j.nedt.2026.107119
https://hdl.handle.net/10459.1/469967
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Qualitative research
Meta-synthesis
Nursing students
Perceptions
UTAUT2 model
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spelling Perceptions of artificial intelligence in nursing students: A qualitative meta-synthesis based on the UTAUT2 modelBonet Augè, AidaTort Nasarre, GlòriaDomènech-Sorolla, JordinaMonistrol Ruano, OlgaCamí Garanto, CarlaMedel, DanielArtificial intelligenceQualitative researchMeta-synthesisNursing studentsPerceptionsUTAUT2 modelBackground: Artificial intelligence (AI) is rapidly transforming nursing education and clinical practice. Understanding students’ perceptions is essential for designing effective and equitable AI-enhanced learning strategies. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) offers a robust model for examining factors that shape acceptance and use. Aim: To integrate qualitative evidence on nursing students’ perceptions of incorporating AI into academic education, using the UTAUT2 model to interpret constructs influencing acceptance and use. Methods: A qualitative deductive meta-synthesis was conducted following Dixon-Woods’ framework. A systematic search (October 2024–July 2025) in PubMed, CINAHL, and Web of Science identified qualitative studies and qualitative findings from mixed-method research published between 2020 and 2025. Methodological quality was appraised using ENTREQ guidelines and the Joanna Briggs Institute’s Qualitative Assessment and Review Instrument (QARI). Data were thematically synthesized according to UTAUT2 constructs. Results: Nursing students perceive AI as a tool to enhance learning and efficiency (performance expectancy) yet concerns about the complexity and digital competencies persist (effort expectancy). Faculty support and institutional context emerge as key factors in promoting acceptance (social influence and facilitating conditions). Intention to use relates to interest and prior experience (hedonic motivation). Cost remains a barrier (price value), while accumulated experience strengthens adoption (habit). Conclusion: AI represents a valuable resource in nursing education, offering opportunities for personalized learning, improved access to information, and enhanced competency development. Effective integration requires addressing technical, ethical, equity-related, and training barriers. Importantly, AI should complement, rather than replace, the human dimension of nursing education.Elsevier Ltd.2026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1016/j.nedt.2026.107119https://hdl.handle.net/10459.1/469967reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)InglésReproducció del document publicat a https://doi.org/10.1016/j.nedt.2026.107119Nurse Education Today, 2026, vol. 163cc-by-nc, (c) Bonet et al., 2026Attribution-NonCommercial 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/4.0/oai:dnet:.___________::a4ab7e1f1c4656697d266bccac1e55d62026-06-24T12:42:17Z
dc.title.none.fl_str_mv Perceptions of artificial intelligence in nursing students: A qualitative meta-synthesis based on the UTAUT2 model
title Perceptions of artificial intelligence in nursing students: A qualitative meta-synthesis based on the UTAUT2 model
spellingShingle Perceptions of artificial intelligence in nursing students: A qualitative meta-synthesis based on the UTAUT2 model
Bonet Augè, Aida
Artificial intelligence
Qualitative research
Meta-synthesis
Nursing students
Perceptions
UTAUT2 model
title_short Perceptions of artificial intelligence in nursing students: A qualitative meta-synthesis based on the UTAUT2 model
title_full Perceptions of artificial intelligence in nursing students: A qualitative meta-synthesis based on the UTAUT2 model
title_fullStr Perceptions of artificial intelligence in nursing students: A qualitative meta-synthesis based on the UTAUT2 model
title_full_unstemmed Perceptions of artificial intelligence in nursing students: A qualitative meta-synthesis based on the UTAUT2 model
title_sort Perceptions of artificial intelligence in nursing students: A qualitative meta-synthesis based on the UTAUT2 model
dc.creator.none.fl_str_mv Bonet Augè, Aida
Tort Nasarre, Glòria
Domènech-Sorolla, Jordina
Monistrol Ruano, Olga
Camí Garanto, Carla
Medel, Daniel
author Bonet Augè, Aida
author_facet Bonet Augè, Aida
Tort Nasarre, Glòria
Domènech-Sorolla, Jordina
Monistrol Ruano, Olga
Camí Garanto, Carla
Medel, Daniel
author_role author
author2 Tort Nasarre, Glòria
Domènech-Sorolla, Jordina
Monistrol Ruano, Olga
Camí Garanto, Carla
Medel, Daniel
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Artificial intelligence
Qualitative research
Meta-synthesis
Nursing students
Perceptions
UTAUT2 model
topic Artificial intelligence
Qualitative research
Meta-synthesis
Nursing students
Perceptions
UTAUT2 model
description Background: Artificial intelligence (AI) is rapidly transforming nursing education and clinical practice. Understanding students’ perceptions is essential for designing effective and equitable AI-enhanced learning strategies. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) offers a robust model for examining factors that shape acceptance and use. Aim: To integrate qualitative evidence on nursing students’ perceptions of incorporating AI into academic education, using the UTAUT2 model to interpret constructs influencing acceptance and use. Methods: A qualitative deductive meta-synthesis was conducted following Dixon-Woods’ framework. A systematic search (October 2024–July 2025) in PubMed, CINAHL, and Web of Science identified qualitative studies and qualitative findings from mixed-method research published between 2020 and 2025. Methodological quality was appraised using ENTREQ guidelines and the Joanna Briggs Institute’s Qualitative Assessment and Review Instrument (QARI). Data were thematically synthesized according to UTAUT2 constructs. Results: Nursing students perceive AI as a tool to enhance learning and efficiency (performance expectancy) yet concerns about the complexity and digital competencies persist (effort expectancy). Faculty support and institutional context emerge as key factors in promoting acceptance (social influence and facilitating conditions). Intention to use relates to interest and prior experience (hedonic motivation). Cost remains a barrier (price value), while accumulated experience strengthens adoption (habit). Conclusion: AI represents a valuable resource in nursing education, offering opportunities for personalized learning, improved access to information, and enhanced competency development. Effective integration requires addressing technical, ethical, equity-related, and training barriers. Importantly, AI should complement, rather than replace, the human dimension of nursing education.
publishDate 2026
dc.date.none.fl_str_mv 2026
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://doi.org/10.1016/j.nedt.2026.107119
https://hdl.handle.net/10459.1/469967
url https://doi.org/10.1016/j.nedt.2026.107119
https://hdl.handle.net/10459.1/469967
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a https://doi.org/10.1016/j.nedt.2026.107119
Nurse Education Today, 2026, vol. 163
dc.rights.none.fl_str_mv cc-by-nc, (c) Bonet et al., 2026
Attribution-NonCommercial 4.0 International
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/4.0/
rights_invalid_str_mv cc-by-nc, (c) Bonet et al., 2026
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier Ltd.
publisher.none.fl_str_mv Elsevier Ltd.
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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