Large Language Models and the Evolution of Online Help-Seeking for Mental Health

Abstract

Large language models (LLMs) are rapidly becoming embedded in everyday mental health help-seeking practices, particularly among young people who already turn to digital platforms as gateways to mental health support. While LLMs offer unprecedented immediacy and accessibility, their integration into help-seeking ecosystems raises important questions for digital health research. This opinion paper argues that LLMs fundamentally reshape the developmental processes underpinning online help-seeking. Traditional digital help-seeking requires active exploration, searching, comparing sources and reflecting on lived experience, processes that contribute to mental health literacy and resilience. In contrast, LLMs collapse informational plurality into singular, authoritative-sounding responses, potentially shifting users from active exploration toward passive consumption. We discuss the risks of sycophancy, and over-reliance on immediacy, and consider how these dynamics may alter developmental trajectories of coping and help-seeking agency. We argue that preserving agency, connectedness, and reflective engagement must be central to the design of conversational AI in health contexts.