Algorithmic feedback and multilingual identity: Translanguaging practices in Jordanian EFL academic writing

Abstract

This study examines how multilingual EFL students and instructors in a Jordanian university respond to AI-mediated writing support using translanguaging practices. Through a qualitative multi-case design, the research draws on interviews, classroom observations, writing samples, AI-generated feedback, and instructor journals. The findings show that students do not simply follow AI suggestions; instead, they assess them critically, adjusting or discarding them to maintain their intended meaning, personal voice, and cultural references. Translanguaging is shown to be more than a learning strategy, it serves as a form of agency and resistance against automated feedback systems that often reflect monolingual norms. The study highlights how students use both Arabic and English resources to shape their texts, asserting ownership over their writing in the face of algorithmic correction. By combining translanguaging theory with critical AI literacy, the paper offers a new perspective on how language practices are shaped in digital educational settings. It also underscores the importance of building AI-informed writing pedagogies that recognize multilingualism as a resource rather than a barrier, especially in underrepresented Arabic-speaking contexts.

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