Reflecting the voices of EFL teachers in the world of Intelligent Computer-Assisted Language Assessment (ICALA): an insight into teacher immunity, reflective teaching, job satisfaction, and L2-teacher grit

Abstract

Abstract Artificial intelligence (AI) and Intelligent Computer-Assisted Language Assessment (ICALA) are transforming the educational landscape by radically changing how lessons are taught and students are evaluated. As the masterminds behind the curriculum, it is critical to consider the emotional and mental health of teachers who applied ICALA as part of their language instruction. To this end, this study’s primary objective was to examine the connections between ICALA, teacher immunity, reflective teaching, job satisfaction, and l2-teacher grit. To achieve this goal, 221 English as a foreign language (EFL) university teachers from Afghanistan participated in the current research. The research outcomes identified and quantified the influences of teacher immunity and reflective teaching on job satisfaction and L2 teacher grit by data screening utilizing confirmatory factor analysis (CFA) and structural equation modeling (SEM). According to the results, TI and RT are vital in implementing AI into language assessment by maintaining an uninterrupted JS and L2TG. The study’s ramifications are further elaborated upon.

Keywords

• Intelligent Computer-Assisted Language Assessment
• EFL University Teacher
• Immunity
• Reflective teaching
• Job satisfaction
• L2-teacher grit