Algorithmically Mediated Audience Design: Voice, Pragmatic Force, and Linguistic Normativity in Generative-AI Rewriting

Abstract

Generative AI is increasingly inserted between multilingual writers and their audiences, yet its linguistic consequences are often discussed as matters of accuracy, fluency, or efficiency. This study reconceptualizes AI-assisted rewriting as algorithmically mediated audience design: a process in which a generative system can redistribute pragmatic force, epistemic commitment, register, authorial visibility, and the indexical meanings through which writers position themselves. A controlled qualitative elicitation corpus was constructed from 12 researcher-designed English utterances representing recurrent applied-linguistic phenomena, including directness, stance, culturally situated formulaicity, pluricentric lexis, code-meshing, self-mention, and relational deference. Each item was contrasted across generic “improvement” and audience/identity-aware revision conditions. Analysis combined functional linguistic coding with deviant-case analysis and an explicit audit trail across lexicogrammatical, pragmatic, discourse, and sociolinguistic levels. Five recurrent processes were identified: sociolinguistic normalization, pragmatic mitigation, epistemic recalibration, redistribution of authorial voice, and selective retention of multilingual indexicality. Audience-explicit prompting constrained some homogenizing tendencies but did not eliminate normative mediation. The findings support a model of algorithmically mediated audience design in which prompting, model output, and human uptake jointly shape communicative meaning. The study argues that applied-linguistic evaluation of GenAI should move beyond error reduction toward consequential changes in voice, identity, normativity, and communicative agency.