Speaker
Description
Artificial Intelligence (AI) is reshaping language education through personalized and adaptive learning experiences. In Vietnam, the Ministry of Education and Training's Digital Competency Framework for Learners (MOET, 2025) has formally recognized AI related competencies, which places new demands on teachers' professional capacity. However, the current Foreign Language Teacher Competency Framework (FLTC) does not address AI integration, and no contextually appropriate instrument exists for Vietnamese EFL teachers to evaluate their own AI pedagogy competence. This poster presents the construction and validation of a Self-assessment Scale designed to address this gap. The Scale was grounded in Domain 4 (AI Pedagogy) of UNESCO's AI Competency Framework for Teachers (ACFT) and Domain 2 (Teaching) of Vietnam's FLTC. It measures teachers' ability to integrate AI in lesson planning, material development, lesson implementation, and assessment, with items aligned with three proficiency levels: Acquire, Deepen, and Create. The Scale underwent a two-phase validation procedure with EFL secondary school teachers in Hanoi, in which quantitative analyses and follow-up interviews informed item reduction, rewording, and refinement of the Vietnamese version. The finalized Vietnamese version was then administered nationwide through voluntary response sampling, reaching 1528 EFL teachers at primary, lower secondary, upper secondary, and inter-level schools across 31 provincial Departments of Education and Training. Cronbach's alpha coefficients ranging from 0.891 to 0.957 confirmed high reliability of the instrument at scale. The validated Scale can serve both diagnostic and developmental purposes, providing an empirical basis for designing AI related professional development and informing policy on AI integration in English language teaching in Vietnam.
Keywords: AI pedagogy competence, self-assessment scale, teacher professional development
