AI as Relational Translator: Rethinking Belonging and Mutual Legibility in Cross-Cultural Contexts
This addresses the problem of loneliness and reduced socialisation in cross-cultural contexts by rethinking AI's role, though it is a conceptual shift rather than an incremental improvement.
The paper challenges the 'AI as companion' paradigm by proposing Relational AI Translation, which shifts AI's role from simulating human relationships to supporting relationships between humans, using a multi-agent architecture for cross-cultural contexts like first-generation East Asian migrants.
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.