A Theory-Informed Framework for Selecting AI Tools in Language Teaching

The revolutionary progress of Artificial Intelligence (AI) is redefining educational technology, enabling innovative approaches to education. However, the absence of a theory-informed support for selecting AI tools has raised concerns about instructional consistency and quality in language teaching. To address this gap, this study proposes an AI Tool Selection (ATS) Framework to guide educators in choosing AI tools for effective language teaching. To ensure theoretical rigor, the proposed framework synthesizes insights from nine established theories across three interrelated components: Pedagogical Alignment, informed by CLT, CALL, and SLA; Technological Integration, drawing on SAMR, TPACK, and HCI; and Adoption and Usability, grounded in TAM, Sociocultural Theory and DOI. Each component is defined by three clear indicators and guiding questions that prompt informed, context-sensitive decisions in AI tool selection. Overall, the conceived ATS Framework advances AI tool selection in language teaching by offering operational practicality, theoretical depth, and ethical-contextual sensitivity, ensuring that decisions are actionable, conceptually grounded, and culturally responsible. Future research should empirically validate and refine the framework across diverse educational and cultural contexts.