AI as Relational Translator: Rethinking Belonging and Mutual Legibility in Cross-Cultural Contexts

TL;DR

This paper proposes a paradigm shift in AI design from simulating human relationships to facilitating human-to-human connections across cultural divides, emphasizing relational scaffolding and structural justice.

Contribution

It introduces Relational AI Translation, a novel framework positioning AI as cultural-relational infrastructure to support cross-cultural human relationships.

Findings

Multi-agent architecture for emotion decoding, reframing, and scaffolding

Design provocations for measurement and safety in relational AI

Success defined as fostering human-to-human support, not system engagement

Abstract

Against rising global loneliness, AI companions promise connection, yet accumulating evidence suggests that, for some users and contexts, intensive companion-style use can correlate with increased loneliness and reduced offline socialisation. This position paper challenges the dominant "AI as companion" paradigm by proposing a shift: from AI that simulates relationships with humans to AI that supports relationships between humans. We introduce Relational AI Translation, positioning AI as cultural-relational infrastructure that scaffolds human connection across cultural, generational, and geographical divides. Using first-generation East Asian migrants as a theoretically productive critical case, we outline a multi-agent architecture instantiating three translation operations: emotion-intent decoding, contextual reframing, and relational scaffolding. We articulate design provocations around measurement, safety architecture, and the tension between technological intervention and structural justice, and explicitly frame success as graduation toward renewed human-to-human support rather than sustained engagement with the system.