GenAI-mediated activity theory (GMAT) for L2 teachers

Abstract

This study proposes the GenAI-Mediated Activity Theory (GMAT) as a conceptual framework for understanding how generative AI (GenAI) reshapes second language (L2) teachers’ teaching preparation and practices within complex pedagogical ecosystems. Methodologically, the paper adopts a model-building approach. It recontextualizes and synthesizes conceptual elements from Engeström’s (1999) expanded Activity Theory (AT) to construct the model. Thus, the study illuminates the evolving role of L2 teachers as adaptive, multi-positional agents of teaching. Specifically, teachers may adopt GenAI to refine instructional goals, co-create pedagogical content, negotiate ethical guidelines, and participate in collaborative professional networks. While previous AT-based studies have primarily examined how technologies mediate components within an activity system, the GMAT model contributes this work by theorizing how GenAI contributes to the evolution of sociotechnical and pedagogical ecosystems in L2 education. Furthermore, the study outlines concrete pedagogical implications by examining how specific triadic relationships among the components interact to generate new GenAI-mediated instructional environments. Overall, the GMAT model provides both a theoretical and practical foundation for guiding instructional design and teacher development in the era of GenAI-enhanced education.