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

2026-03-19

AI Summary

This study addresses how current AI companion paradigms often exacerbate user loneliness and fail to foster cross-cultural interpersonal connections. It proposes a “relational AI translation” framework that reorients artificial intelligence from a human–AI interaction model toward functioning as cultural relational infrastructure designed to cultivate interpersonal trust. Success is defined by users’ eventual disengagement from the system and reintegration into authentic human support networks. Grounded in multi-agent systems, the framework employs three core mechanisms—emotion-intention decoding, contextual reframing, and relational scaffolding—to facilitate understanding across cultural, generational, and geographic divides. Focusing on first-generation East Asian immigrants, the work advances a theoretical model and design implications, while articulating a forward-looking approach that navigates tensions among measurement metrics, safety architectures, and the interplay of technological intervention with structural justice.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Intelligent Robots: Embodied AI

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

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.

Problem

• AI companions
• loneliness
• cross-cultural contexts
• human-to-human connection
• relational translation

Innovation

• Relational AI Translation
• multi-agent architecture
• emotion-intent decoding
• contextual reframing
• relational scaffolding