Trust in AI: scale development and validation for online distance learners

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dc.contributor.authorUstun, Aysin Gaye
dc.contributor.authorYavuz, Mehmet
dc.contributor.authorKayali, Bunyami
dc.contributor.authorUcar, Hasan
dc.contributor.authorErdogdu, Erdem
dc.contributor.authorBozkurt, Aras
dc.date.accessioned2026-09-01T15:52:35Z
dc.date.available2026-09-01T15:52:35Z
dc.date.issued2026
dc.departmentBayburt Üniversitesi
dc.description.abstractTrust in artificial intelligence (AI) has emerged as a critical factor influencing students' interactions with AI-supported learning environments. However, validated instruments for measuring student trust in AI within educational contexts remain limited. This study developed and validated a multidimensional scale to assess student trust in AI systems in online distance education. Scale development followed a sequential multi-stage design, including item generation based on literature review, expert evaluation for content validity, and psychometric validation through exploratory and confirmatory factor analyses. A total of 837 distance learning students participated in the study (EFA = 412; CFA = 307; validation sample = 118). Exploratory factor analysis supported a five-factor structure explaining 63.20% of the total variance. Confirmatory factor analysis with an independent sample indicated good model fit (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\chi <^>{2}$$\end{document}/df = 2.17; CFI = .95; TLI = .94; RMSEA = .06). The scale demonstrated high internal consistency (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document} = .94; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\omega $$\end{document} = .94). Multi-group confirmatory factor analysis supported metric invariance across gender, indicating comparable measurement across groups. No significant gender differences were observed, and trust in AI was not significantly associated with grade point average or system engagement frequency. These results suggest that trust represents a distinct perception that may influence how students engage with AI-supported learning environments. The resulting 21-item scale provides a reliable instrument for investigating student trust in AI and offers practical implications for the design and implementation of AI-supported learning in higher education.
dc.identifier.doi10.1186/s40359-026-04679-z
dc.identifier.issn2050-7283
dc.identifier.issue1
dc.identifier.orcid0000-0001-6419-9088
dc.identifier.pmid42098882
dc.identifier.scopus2-s2.0-105043554940
dc.identifier.scopusqualityQ2
dc.identifier.urihttp://dx.doi.org/10.1186/s40359-026-04679-z
dc.identifier.urihttps://hdl.handle.net/20.500.12403/8477
dc.identifier.volume14
dc.identifier.wosWOS:001808357300003
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringernature
dc.relation.ispartofBmc Psychology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260820
dc.subjectArtificial Intelligence
dc.subjectTrust
dc.subjectTechnology Acceptance
dc.subjectOnline Distance Education
dc.subjectHigher Education
dc.subjectScale Development
dc.titleTrust in AI: scale development and validation for online distance learners
dc.typeArticle

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