The rapid integration of artificial intelligence (AI) into language education has heightened the need for foreign language (FL) teachers to develop strong artificial intelligence literacy (AIL) to navigate increasingly complex and diverse digital classrooms. However, current AIL assessment standards tend to be generic, often overlooking the cognitive, linguistic, and pedagogical challenges specific to FL contexts. This gap highlights the need for reliable, context-sensitive frameworks and practical assessment tools. Drawing on an extensive synthesis of peer-reviewed research from major academic databases, this Mini Review synthesizes the literature, using a transparent literature selection process informed by established reporting principles and adapted to the scope of the study. Key study parameters such as research design, participants, and item formats were extracted and organized for comparative analysis. The resulting multidimensional framework, grounded in an expanded TPACK-AI model, comprises six interrelated dimensions: conceptual understanding, technical proficiency, pedagogical integration, adaptive expertise, ethical reasoning, and self-efficacy. In addition, this Mini Review illustrates a set of assessment item types including multiple-choice, matching, ordering, cloze, short answer, and extended response, each mapped to specific AIL competencies. By integrating insights from existing scholarship with a context-specific framework design, this study synthesizes a structured, adaptable approach for defining and organizing the core competencies of FL teachers in AI-integrated language education. The framework can serve as a reference for teacher educators, policymakers, and institutions in developing targeted training initiatives, formulating policy guidelines, and supporting equitable technology integration in language education.
Ren et al. (Mon,) studied this question.