Use of AI for supporting non-native language speakers in higher education: navigating equity, ethics, and pedagogical practice

The increasing internationalization of higher education has resulted in a growing population of non-native language speakers (NNLS) who face systemic linguistic barriers and stigmatization in the classroom. While artificial intelligence (AI) offers significant potential to mitigate these challenges through translation and literacy support, the academic community’s response has largely been one of restriction, driven by concerns over academic integrity. This critical theoretical analysis argues that blanket AI prohibitions inadvertently exacerbate educational inequities, imposing an “invisible linguistic tax” on students learning in a non-native language. Drawing on the frameworks of literacy as liberation, culturally sustaining pedagogy, and critical pedagogy, this paper reframes AI policy from a technical question of cheating to a fundamental issue of linguistic justice. Through a review of both theoretical and empirical literature and a narrative vignette, we illustrate how restrictive policies perpetuate the marginalization of NNLS. The paper concludes by offering equity-centered guidelines for faculty to design AI policies that distinguish between academic dishonesty and necessary linguistic access, thereby fostering a more inclusive pedagogical environment.