Veteran Language Teachers' Coping in AI-Assisted Learning Environments

Abstract

The integration of AI-assisted environments and AI-based systems in education has introduced both opportunities and challenges for experienced teachers. This article explores the difficulties encountered by veteran educators who would normally feel satisfactorily efficient due to their experience in the profession as they navigate these new technologies, comparing their teacher self-efficacy and management strategies in professional settings. Thirty-two veteran language teachers with professional experience ranging from 25 to 40 years participated in the study. Utilizing a mixed-methods approach, this study employs the Teachers' Sense of Efficacy Scale and Teacher AI Competence Self-Efficacy Scale to quantitatively assess efficacy levels among teachers. Additionally, in-depth interviews with 12 veteran teachers provide qualitative insights into their coping mechanisms and adaptations. The findings indicate high levels of general self-efficacy, consistent with their extensive professional experience; however, this confidence does not necessarily extend to technology-enhanced education, particularly in relation to AI tools and applications. The findings also highlight the complexities faced by experienced educators and offer recommendations for effectively integrating AI technologies in teaching practices, ensuring that the transition supports both educators' professional growth and students' learning outcomes.