From Chatbots to Confidants: A Cross-Cultural Study of LLM Adoption for Emotional Support

2026-04-28

AI Summary

This study addresses the lack of systematic understanding regarding the adoption drivers and user perceptions of large language models (LLMs) for emotional support across different national contexts. Conducting the first cross-cultural comparison across seven countries, it integrates a large-scale survey (N > 7,000) with 731 multilingual, real-world user prompts. Using mixed-effects modeling, the research examines usage patterns, perceptual differences, and sociodemographic influences. Findings reveal LLM adoption rates for emotional support ranging from 20% to 59%, with users in English-speaking countries reporting more positive perceptions. Age, marital status, religious affiliation, and socioeconomic status significantly shape trust and usage, with socioeconomic status exhibiting the strongest effect—highlighting the critical roles of both cultural norms and structural factors in shaping engagement with AI-mediated emotional support.

Abstract

Large Language Models (LLMs) are increasingly used not only for instrumental tasks, but as always-available and non-judgmental confidants for emotional support. Yet what drives adoption and how users perceive emotional support interactions across countries remains unknown. To address this gap, we present the first large-scale cross-cultural study of LLM use for emotional support, surveying 4,641 participants across seven countries (USA, UK, Germany, France, Spain, Italy, and The Netherlands). Our results show that adoption rates vary dramatically across countries (from 20% to 59%). Using mixed models that separate cultural effects from demographic composition, we find that: Being aged 25-44, religious, married, and of higher socioeconomic status are predictors of positive perceptions (trust, usage, perceived benefits), with socioeconomic status being the strongest. English-speaking countries consistently show more positive perceptions than Continental European countries. We further collect a corpus of 731 real multilingual prompts from user interactions, showing that users mainly seek help for loneliness, stress, relationship conflicts, and mental health struggles. Our findings reveal that LLM emotional support use is shaped by a complex sociotechnical landscape and call for a broader research agenda examining how these systems can be developed, deployed, and governed to ensure safe and informed access.

Problem

• Large Language Models
• Emotional Support
• Cross-Cultural Study
• LLM Adoption
• Sociotechnical Landscape

Innovation

• cross-cultural study
• large language models
• emotional support
• sociotechnical factors
• user adoption