Designing Mental-Health Chatbots for Indian Adolescents: Mixed-Methods Evidence, a Boundary-Object Lens, and a Design-Tensions Framework

TL;DR

This study explores how Indian adolescents interact with mental health chatbots, revealing cultural preferences and design gaps, and introduces frameworks to improve culturally sensitive digital mental health support.

Contribution

It presents a novel Design-Tensions framework, an artifact-level probe, and a boundary-objects account for designing culturally relevant mental health chatbots.

Findings

Adolescents prefer text-based interactions over voice.

Low utilization of mental health apps despite high smartphone access.

Existing chatbots lack personalization and cultural relevance.

Abstract

Mental health challenges among Indian adolescents are shaped by unique cultural and systemic barriers, including high social stigma and limited professional support. We report a mixed-methods study of Indian adolescents (survey n=362; interviews n=14) examining how they navigate mental-health challenges and engage with digital tools. Quantitative results highlight low self-stigma but significant social stigma, a preference for text over voice interactions, and low utilization of mental health apps but high smartphone access. Our qualitative findings reveal that while adolescents value privacy, emotional support, and localized content in mental health tools, existing chatbots lack personalization and cultural relevance. We contribute (1) a Design-Tensions framework; (2) an artifact-level probe; and (3) a boundary-objects account that specifies how chatbots mediate adolescents, peers, families, and services. This work advances culturally sensitive chatbot design by centering on underrepresented populations, addressing critical gaps in accessibility and support for adolescents in India.