This presentation introduces The Trans-Babelism Paradox: a conceptual framework that examines how AI transcends linguistic barriers yet fails to convey cultural depth. For decades, English has functioned as the global lingua franca, centralizing access to knowledge and power. AI-driven translation now promises to dissolve this monopoly, creating a post-lingua-franca world where all languages can coexist through technological mediation. However, beneath this equality lies a paradox: while AI enables lexical comprehension across languages, it often erases the cultural, emotional, and contextual nuances that give language its human texture. Drawing on sociolinguistics, cognitive semiotics, and AI ethics, this paper proposes a two-layer model of translation: (a) the lexical-syntactic layer effectively handled by AI; and (b) the cultural-cognitive layer that remains irreducibly human. The study argues that true communication in the AI era will depend not on achieving universal intelligibility but on preserving cultural particularity within automated translation systems. The Trans-Babelism Paradox challenges the optimism surrounding AI multilingualism by asking: if machines can translate every word but misunderstand every world, have we truly transcended Babel or merely rebuilt it in code?

Authors:
Hoang-Nam Tran, Tokushima University, Japan

About the Presenter(s)
Hoang-Nam Tran, M.D., Ph.D. is an Associate Professor at Research Center for Higher Education, Tokushima University, Japan.

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