Validation of the University of Florida AI Literacy Model for IT Skill Development in Saudi Higher Education.
Background: Artificial intelligence (AI) literacy is increasingly essential in higher education for developing digital competencies, but its impact on IT skill acquisition remains underexplored in Arab contexts. Objectives: To evaluate the effectiveness of the University of Florida (UF) AI Literacy...
| Publicado en: | Journal of Computer Assisted Learning Vol. 42; no. 4; pp. 1 - 19 |
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| Formato: | research tables/charts randomized controlled trial Journal Article |
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Wiley-Blackwell
Aug2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195655218&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195655218 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Aug2026 vid: 42 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 195655218 195655218 195655218 10.1002/jcal.70291 195655218 ppf: 1 ppct: 18 formats: tig: atl: Validation of the University of Florida AI Literacy Model for IT Skill Development in Saudi Higher Education. aug: au: Al‐Garni, Zafer Ahmed affil: Department of Educational Sciences, College of Education, Majmaah University, Al Majmaah, Saudi Arabia sug: subj: Computer Literacy Education Artificial Intelligence Education Educational Technology Educational Measurement Students, College Psychosocial Factors Human Male Female Adolescence Young Adult Saudi Arabia Randomized Controlled Trials Random Assignment Multimethod Studies Curriculum Ethics, Professional Memory Validation Studies Student Attitudes Semi-Structured Interview Audiorecording Thematic Analysis Analysis of Variance Two-Way Analysis of Variance Multivariate Analysis of Variance McNemar's Test Logistic Regression Kaplan-Meier Estimator Log-Rank Test Intraclass Correlation Coefficient Post Hoc Analysis Confidence Intervals Data Analysis Software Descriptive Statistics Funding Source Adolescent: 13-18 years Male Female ab: Background: Artificial intelligence (AI) literacy is increasingly essential in higher education for developing digital competencies, but its impact on IT skill acquisition remains underexplored in Arab contexts. Objectives: To evaluate the effectiveness of the University of Florida (UF) AI Literacy Model in enhancing IT competencies among Saudi university students, aligning with Vision 2030's digital transformation goals, and to examine factors influencing skill retention and practical application. Methods: In a randomized control‐group design, 60 students aged 18–22 were assigned to either the experimental group, which completed a 10‐session AI literacy intervention, or the control group, which followed traditional IT instruction. Pre‐ and post‐intervention achievement tests measured proficiency in navigation, file management, system customization, and troubleshooting. The mixed‐methods approach also incorporated surveys, interviews, and machine learning classifiers to analyse outcomes. Results: The experimental group exhibited significantly greater improvements in IT skills, with large effect sizes. Machine learning analyses identified attendance and regular tool usage as key predictors of success. High‐engagement learners showed strong retention, with performance gains aligning with DigComp 2.2 standards. However, students' ethical awareness remained limited, with only 30% spontaneously raising privacy concerns. Conclusions: This study provides the first empirical validation of the UF AI Literacy Model in an Arab higher‐education context. It offers evidence of both effectiveness and skill retention conditions, providing implementation insights for Gulf state educational contexts. Curricula should include regular AI tool practice and explicitly assess ethics (privacy, bias, and transparency). Institutions should implement graded ethical checkpoints to foster digital fluency and workforce readiness. Practitioner Notes: What is currently known about this topic? ○AI literacy is essential for digital competency but is scarcely explored in Arab higher education contexts.○Saudi Vision 2030 emphasizes digital transformation, yet classroom‐level AI implementation lags behind policy frameworks.○Existing research shows AI‐augmented learning improves technical skills but often overlooks ethical dimensions.What does this paper add? ○Provides the first empirical validation of the UF AI Literacy Model in Middle Eastern higher education.○Demonstrates that AI‐enhanced instruction produces significant IT‐skill gains with large effect sizes and strong retention among engaged learners.○Reveals a critical gap: only 30% of students spontaneously addressed privacy concerns despite substantial technical gains.Implications for practice/policy ○Integrate structured ethics modules with technical AI training in curricula.○Adopt a scalable instructional framework aligned with the Saudi National Qualifications Framework and DigComp 2.2 standards.○Prioritize targeted faculty development to bridge AI implementation gaps and promote responsible, practice‐based AI education in Arab universities. pubtype: Academic Journal doctype: research tables/charts randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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