Who Regulates AI Better? Exploring AI Self-Regulated Learning Among Novice and Experienced Vietnamese EFL Teachers

Artificial intelligence (AI) has increasingly transformed teacher professional learning in English as a foreign language (EFL) education. However, relatively limited research has examined how teachers strategically regulate and sustain their AI-related learning processes, particularly across different stages of professional experience. This study investigated AI self-regulated learning (AI-SRL) among novice and experienced Vietnamese in-service EFL teachers. An explanatory sequential mixed methods design was employed. Quantitative data were collected from 1,108 Vietnamese in-service EFL teachers using the AI-SRL Scale, followed by semi-structured interviews with 10 teachers. The quantitative findings revealed that novice teachers demonstrated significantly higher levels of AI-SRL than experienced teachers, particularly in task planning, self-monitoring, and self-evaluation. Notably, no statistically significant differences were identified in help seeking and institutional support. Interestingly, the qualitative findings showed that teachers’ AI-SRL was shaped not only by technological factors, but also by professional identity, emotional responses toward AI learning, and contextual conditions surrounding AI integration. Novice teachers demonstrated remarkably stronger exploratory orientations toward AI learning, whereas experienced teachers expressed greater concerns regarding pedagogical stability and professional accountability. Overall, the study extends previous AI-related educational research by offering a more process-oriented and psychologically nuanced understanding of teacher professional learning in AI-mediated educational environments. The findings also provide valuable pedagogical implications for AI-related teacher professional development in EFL contexts.