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

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.