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

TL;DR

The paper investigates mental health challenges among Indian adolescents, highlighting stigma, limited access to care, and the potential of chatbots as culturally tuned boundary objects. Using a two-phase mixed-methods approach (survey of 362 and interviews with 14), it reveals low self-stigma but high social stigma, a preference for text-based interactions, and limited use of MH apps despite widespread smartphone access. It introduces a Design-Tensions framework and a boundary-objects perspective to guide culturally sensitive chatbot design, plus an infrastructure-aware roadmap aligned with Tele-MANAS and DPDP 2023 for scalable deployment in India. The work advances HCI for the Global South by centering underrepresented populations, outlining practical design tensions, localization needs, and governance approaches to create accessible, trustworthy, and contextually relevant digital mental health support for adolescents in India.

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.

Figures (1)

• Figure 1: The chatbot interface. Interviewees could access the chatbot on their phone, tablet, or laptop and interacted via text. The chatbot was programmed to always give short responses and ask one question per turn.