Navigating Generative AI in English Language Education: Competencies of Novice and Experienced Vietnamese EFL Teachers
TL;DR
The qualitative findings revealed that teachers’ GenAI competencies were negotiated through institutional ambiguity, professional identity concerns, informal learning networks, and the hidden evaluative labour associated with AI-generated content.
Abstract
This study has investigated the generative artificial intelligence (GenAI) competencies of Vietnamese in-service EFL teachers across different teaching experience levels. Employing an explanatory sequential mixed-methods design, the study collected quantitative data from 581 novice and experienced EFL teachers using the Teachers’ Generative AI Competencies (T-GAIC) instrument and qualitative data from semi-structured interviews with 14 participants. Quantitative data were analysed using descriptive statistics and independent-sample t-tests, while qualitative data were examined through thematic analysis. The findings indicated that both novice and experienced teachers perceived themselves as having relatively high levels of GenAI competencies, despite the rapid emergence of AI technologies in Vietnamese higher education. Independent-sample t-tests revealed statistically significant but small differences in technological proficiency, with novice teachers reporting higher levels than experienced teachers (t = 3.29, p <.05, d =.27), and in risk and ethical awareness, with experienced teachers reporting higher levels than novice teachers (t = ?3.48, p <.05, d =.30). No significant differences were found in pedagogical compatibility, preparing students for effective GenAI use, or professional development and communication. Overall, the limited magnitude of the group differences suggested that GenAI competencies might extend beyond technological familiarity or teaching experience alone. More importantly, the qualitative findings revealed that teachers’ GenAI competencies were negotiated through institutional ambiguity, professional identity concerns, informal learning networks, and the hidden evaluative labour associated with AI-generated content. The study refines current understandings of teachers’ AI competencies by highlighting their multidimensional, socially situated, and context-dependent nature in AI-mediated language education.
