Rasch Validation of the 5C Digital Competence Scale for Pre Service Teachers in the AI Era

Rasch Validation of the 5C Digital Competence Scale for Pre Service Teachers in the AI Era | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Rasch Validation of the 5C Digital Competence Scale for Pre Service Teachers in the AI Era Ruslina Irianty, Dinni Devi Triana, Ari Saptono This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9040969/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background The accelerated adoption of Generative Artificial Intelligence (GenAI) in education is reshaping the competence required of future teachers, who must move beyond basic operational skills toward critical, ethical, and creative engagement with AI tools. Purpose This study addresses the need for a robust measurement tool by validating the Digital and AI Literacy Scale for Educators (DAIL-SE), an instrument designed to measure AI-era digital competence through the 5C Framework: Critical Thinking, Creativity, Collaboration, Communication, and Citizenship. Methods Using a quantitative survey design, data were collected from 99 Indonesian pre-service teachers and analyses with R (version 4.3.2) using the Rasch Partial Credit Model (PCM) to examine item fit, reliability, and dimensionality. Key Results The DAIL-SE demonstrated strong psychometric properties, with an EAP/PV person reliability of 0.94 and item difficulties ranging from − 3.00 to + 1.50 logits. The person–item map revealed an asymmetrical competence profile: participants displayed high proficiency in Communication and basic information verification, but showed marked weaknesses in data privacy management, algorithmic understanding, and creative AI integration, patterns conceptualized as a “Digital Freeze.” Implications These findings suggest that current teacher education programmed risk overestimating pre-service teachers’ readiness for AI-rich classrooms if they rely solely on surface-level digital indicators. The validated DAIL-SE offers a diagnostic tool for identifying specific competence gaps across the 5C dimensions and supports the design of curricula that priorities data protection, algorithmic literacy, and ethically grounded AI use. AI Literacy Digital Competence Pre-service Teachers Rasch Model 5C Framework Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 02 Apr, 2026 Editor invited by journal 15 Mar, 2026 Editor assigned by journal 13 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 11 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9040969","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617981929,"identity":"5f256c8b-2664-413f-bd68-6b9d1ff14bcc","order_by":0,"name":"Ruslina 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