Unpacking EFL teachers’ perceived opportunities and challenges of multimodal AI-driven language testing

While Artificial Intelligence (AI) has significantly transformed L2 testing and assessment, empirical research into how EFL practitioners perceive the specific opportunities and challenges of these tools remains limited. To address this gap, this phenomenological qualitative study investigated experienced English as a foreign language (EFL) teachers’ perceptions regarding the opportunities and challenges of multimodal AI-driven testing. Grounded in Expectancy-Value Theory (EVT), data were collected via an online focus group interview with ten EFL teachers. Thematic analysis identified four perceived opportunities: fostering the provision of personalized assessment, offering instant feedback, facilitating the assessment of productive skills, and improving efficiency, consistency, and reliability. Conversely, participants identified four significant challenges, specifically concerning the complication of construct validity, the potential for algorithmic bias, difficulties in score interpretation, and the necessity for robust technical infrastructure. The findings offer implications for EFL teachers, language testers, teacher educators, and policymakers, highlighting the bright and dark sides of integrating multimodal AI tools into L2 testing and assessment practices.