Development and Validation of AI Acceptance Scale (AI-AS) for Learners in Higher Education

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Özet

The use of artificial intelligence (AI) applications in educational activities is increasing day by day. This situation highlights the need for valid and reliable measurement tools to determine students' levels of acceptance of this technology. This study aims to develop a standard measurement tool to determine students' levels of acceptance of AI. To this end, the systematic scale development steps proposed by DeVellis and Thorpe (2021) were followed. Exploratory factor analysis conducted with 560 participant responses and confirmatory factor analysis conducted with 950 participant responses resulted in a 26-item, 5-factor structure. The factors included in the scale were listed as "usability", "ease of use", "intention and attitude", "facilitating conditions" and "trust and data privacy". The findings revealed that the scale had high internal consistency and exhibited satisfactory model fit indices. The results indicate that the scale is a valid and reliable measurement tool for determining students' levels of AI acceptance. The study offers practical implications for researchers and practitioners and highlights directions for future work on AI adoption in higher education environments.

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https://rdlopen.rawdatalibrary.net/r/bayburt/11128Resmi atıflarda kullanılan kalıcı adres
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https://doi.org/10.1007/s11528-026-01209-xYayıncı resmi DOI adresi
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AlanDeğerDil
dc.contributor.authorKayalı, Bünyami
dc.contributor.authorYavuz, Mehmet
dc.contributor.authorÜstün, Ayşin Gaye
dc.contributor.authorUçar, Hasan
dc.contributor.authorErdoğdu, Erdem
dc.contributor.authorBozkurt, Aras
dc.date.accessioned2026-10-29T19:08:04.551055225Z
dc.date.available2026-10-29T19:08:04.551055225Z
dc.date.issued2026
dc.description.abstractThe use of artificial intelligence (AI) applications in educational activities is increasing day by day. This situation highlights the need for valid and reliable measurement tools to determine students' levels of acceptance of this technology. This study aims to develop a standard measurement tool to determine students' levels of acceptance of AI. To this end, the systematic scale development steps proposed by DeVellis and Thorpe (2021) were followed. Exploratory factor analysis conducted with 560 participant responses and confirmatory factor analysis conducted with 950 participant responses resulted in a 26-item, 5-factor structure. The factors included in the scale were listed as "usability", "ease of use", "intention and attitude", "facilitating conditions" and "trust and data privacy". The findings revealed that the scale had high internal consistency and exhibited satisfactory model fit indices. The results indicate that the scale is a valid and reliable measurement tool for determining students' levels of AI acceptance. The study offers practical implications for researchers and practitioners and highlights directions for future work on AI adoption in higher education environments.
dc.identifier.doi10.1007/s11528-026-01209-x
dc.identifier.eissn1559-7075
dc.identifier.endpage997
dc.identifier.issn8756-3894
dc.identifier.issue4
dc.identifier.scopus2-s2.0-105044322070
dc.identifier.startpage983
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/bayburt/11128
dc.identifier.volume70
dc.identifier.wosWOS:001816364700001
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofTechTrends
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAcceptance
dc.subjectAI
dc.subjectArtificial intelligence
dc.subjectHigher education
dc.subjectScale development
dc.subject.classificationEducation & Educational Research
dc.subject.otherUSER ACCEPTANCE
dc.subject.otherFIT INDEXES
dc.subject.otherTECHNOLOGY
dc.subject.otherMODEL
dc.titleDevelopment and Validation of AI Acceptance Scale (AI-AS) for Learners in Higher Education
dc.typeArticle
rdl.citation.openalex0
rdl.citation.scopus0
rdl.citation.wos0
rdl.oa.statusclosed
rdl.scopus.quartileQ1
rdl.sdgSDG04
rdl.source.indexSCOPUS
rdl.source.indexWOS
rdl.wos.indexEmerging Sources Citation Index (ESCI)