Speaker
Description
The rapid integration of artificial intelligence (AI) into language education has created an urgent demand for professional development (PD) that builds teacher AI competence rather than mere tool familiarity. Although empirical studies on AI-focused teacher training have grown considerably since 2022, the evidence remains fragmented across contexts, delivery formats, and outcome measures, leaving program designers without consolidated guidance. This study systematically reviews empirical research on AI-focused PD for language teachers, guided by two questions: (1) What program designs and instructional strategies characterise AI-focused PD for language teachers? (2) What factors influence the effectiveness of such programs? Following the PRISMA-ScR protocol, peer-reviewed studies published between 2019 and 2026 were retrieved from Scopus, ERIC, and Web of Science, screened against predefined eligibility criteria, and synthesised through reflexive thematic analysis. The review maps program designs across five delivery approaches: course-based learning, co-design, communities of practice, lesson trial and observation, and action research. Effectiveness factors are organised into a three-level typology covering program-level conditions (duration, contextual alignment, practice-first sequencing), individual-level conditions (self-efficacy, prior digital experience), and institutional-level conditions (leadership support, policy alignment). Findings indicate that sustained, blended, and contextually localised programs outperform short generic workshops, and that self-efficacy is the strongest individual predictor of sustained AI integration. The review concludes with design implications for AI teacher training in EFL contexts, particularly policy-driven settings such as Vietnam, where new national digital competence requirements intensify the need for evidence-based PD models.
Keywords: AI competence, teacher professional development, language teachers
