AI Chatbots For Teen Mental Health: Augmenting India’s Counselling Services – Analysis – Eurasia Review
A Journal of Analysis and News
By Observer Research Foundation
By Srishti Sinha
Adolescent mental health has emerged as a critical public health challenge in India. Mental health disorders account for a significant share of disease burden among young people, yet limited resources and inadequate early intervention systems continue to compound the crisis.
Suicide is the fourth leading causeof death among adolescents aged 15-19 in India, underscoring the unmet need for early, accessible support and reliable pathways to counselling services as part of a broader continuum of care.
The shortage of adolescent mental health professionals compounds the problem. India has fewer than 50 child and adolescent psychiatrists nationwide, translating to less than 0.02 psychiatrists per 100,000 adolescents. With so few specialists, core preventive functions such as school-based screening, psychoeducation, and early identification remain under-delivered, while adolescents who seek help face long waits and referral delays.
Government telehealth initiatives such as Tele-MANAS and e-Sanjeevaniwere launched in recognition of the mental health burden and the shortage of qualified professionals. Their tiered networks route users to counsellors and psychiatrists, easing scarcity and distance barriers, yet coverage remains insufficient. These platforms are well-positioned to integrate AI chatbots that can widen access, provided deployment is sensitive to context and culture. With clear safety guardrails, age-appropriate consent, and inclusive language design, chatbots can supplement counsellors by offering empathetic listening and coping support. AI-enabled chatbots are emerging as a low-threshold support mechanism, offering immediate, affordable, and approachable entry points to care.
Adolescents in India face multiple barriers to mental health care. Stigma, financial costs, geographic inequities, and limited ability to seek services independently often delay help-seeking until a crisis emerges. Generative AI chatbots that create free-form replies are increasingly used for emotional support and self-discovery, with users often describing them as offering an emotional sanctuary, providing insightful guidance, and a sense of connection. These tools can provide early support and complement existing services such as helplines or school counselling.
Research on conversational agents indicates measurable reductions in distress among adolescents with early or mild symptoms. Wysa, a global mental health chatbot that has already served over half a million users in India, has been shown to foster a therapeutic alliance within just five days, with users reporting feelings of being liked, respected, and cared for.
Evidence from India echoes this trajectory, with a Youth Pulse survey finding that 88 percent of school students had turned to AI tools during periods of stress, and anonymity was cited as a key reason adolescents were more willing to participate than with formal services. Together, these dynamics highlight chatbots’ ability to extend support to populations that might otherwise delay or forgo help-seeking.
The foremost challenge is to ensure AI chatbots provide context-appropriate support. A practical pathway is pre-deployment testing aligned with WHO mhGAP for self-harm detection and escalation, and adherence to the 2023 ICMR AI-in-Health principles on safety, oversight, fairness, and inclusion.
After launch, stress-testing and periodic evaluations can surface real-world failures such as unsafe reassurance, bias, data leakage, or shifts in system behaviour that reduce reliability. A national coordinator, such as the IndiaAI Safety Institute, can standardise tests, accredit evaluators, and provide national benchmarks for AI safety across health contexts.
On privacy, deployments must be anchored in the Digital Personal Data Protection Act and the Ayushman Bharat Digital Mission’s consent framework. In practice, this means collecting only what is necessary, enforcing limited retention, separating identifiers from content, and ensuring secure handling with audit trails and need-to-know access.
Use of chat data for improvement or evaluation should require explicit, revocable opt-in, independent oversight, and minimal data. Emergency disclosure should be a narrow exception for imminent harm, with documented and reviewed escalation. Together, these measures safeguard adolescents’ privacy and ensure system safety, while enabling responsible scale.
Linguistic and cultural diversity is a major barrier to equitable adolescent mental health support. Expressions of distress often surface in regional languages, dialects, or colloquialisms that mainstream datasets rarely capture, creating the risk of excluding precisely the adolescents who are most vulnerable. India’s Bhashini initiative offers an opportunity to build multilingual models capable of recognising distress cues across this diversity. To strengthen such efforts, developing lexicons of adolescent distress markers, validated through usability testing, would help improve detection accuracy and reduce misclassification.
Equally important is the co-design of these systems with adolescent users across different cultural and language groups, ensuring participation is age-appropriate and meaningful. UNICEF’s Engaged and Heard!and Safer Chatbots initiatives provide practical guidance for this process, emphasising the involvement of young people in pilot testing, refining phrasing, and shaping responses so that they feel authentic, empathetic, and accessible.
The effectiveness of AI chatbots depends on the strength of the human response system. Tele-MANAS, launched in 2022, has handled 2.4 million calls till July 2025. However, a 40 percent budget cut and a workforce of only 1,900 counsellors leave it under-resourced to respond promptly to high-risk cases. Ensuring credible escalation requires counsellors trained in both clinical practice and cultural nuances.
At the same time, automation can enhance scale by using risk triage algorithms to prioritise urgent cases and call-routing systems to distribute workloads more efficiently, which reduces manual overhead and allows counsellors to focus on service delivery that improves timely detection and referral. More broadly, staffing optimisation should be data-driven, drawing on statistical patterns such as historical demand trends, and regional and seasonal call spikes to anticipate pressure points and allocate resources effectively. Embedding these measures would reinforce the reliability of escalation pathways and lead to timely and competent care.
Robust oversight is essential to safeguard adolescents and maintain public accountability in AI-enabled mental health services. NIMHANS, the nodal centre for tele-mental health, is well placed to conduct sector-specific audits of chatbot pilots, focusing on clinical quality, escalation accuracy, data protection compliance, and user outcomes. These audits should be published transparently and complemented by independent expert review panels and feedback loops from adolescents and counsellors to capture lived experiences.
Integrating these oversight mechanisms within the IndiaAI “safe and trusted AI” frameworkwould establish national benchmarks, ensure consistency across states, and link chatbot governance to India’s broader AI safety agenda. Such measures would create a continuous cycle of oversight and improvement, ensuring that AI chatbots remain accountable tools that support human-led care and protect adolescent well-being.
Adolescent mental health needs in India continue to outpace traditional services, creating a persistent gap that existing approaches cannot close. As AI becomes part of everyday tools and public services, integrating adolescent-facing chatbots within mental health programmes offers a feasible and forward-looking way to expand coverage. These tools are not a substitute for counsellors, but when designed with safety, privacy, and inclusivity at their core, they can extend the reach of scarce professionals and create earlier touchpoints for support. Their value will depend on how effectively India aligns technical innovation with human capacity, governance, and trust, ensuring that chatbots act as responsible bridges that help more young people find timely, reliable care.
ORF was established on 5 September 1990 as a private, not for profit, ’think tank’ to influence public policy formulation. The Foundation brought together, for the first time, leading Indian economists and policymakers to present An Agenda for Economic Reforms in India. The idea was to help develop a consensus in favour of economic reforms.
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