Messages in a Digital Bottle: A Youth-Coauthored Perspective on LLM Chatbots and Adolescent Loneliness

TL;DR

This paper explores how LLM-powered chatbots impact adolescent loneliness, emphasizing youth perspectives and proposing design implications tailored to diverse adolescent subgroups.

Contribution

It foregrounds youth-authored insights on chatbot effects on loneliness and offers population-sensitive design recommendations based on interdisciplinary analysis.

Findings

Chatbots can both reduce and deepen loneliness depending on context.

Different adolescent subgroups experience varied effects from chatbot interactions.

Three design implications are proposed for more sensitive chatbot development.

Abstract

Adolescent loneliness is a growing concern in digitally mediated social environments. This work-in-progress presents a youth-authored critical synthesis on chatbots powered by Large Language Model (LLM) and adolescent loneliness. The first author is a 16-year-old Chinese student who recently migrated to the UK. She wrote the first draft of this paper from her lived experience, supervised by the second author. Rather than treating the youth perspective as one data point among many, we foreground it as the primary interpretive lens, grounded in interdisciplinary literature from social computing, developmental psychology, and Human-Computer Interaction (HCI). We examine how chatbots shape experiences of loneliness differently across adolescent subgroups, including those with anxiety or depression, neurodivergent youth, and immigrant adolescents, and identify both conditions under which they may temporarily reduce isolation and breakdowns that risk deepening it. We derive three population-sensitive design implications. The next phase of this work will expand the youth authorship model to a panel of adolescents across these subgroups, empirically validating the framework presented here.