Enhancing Mental Health Support for Young Adults Through Conversational AI-Based Screening Tools

Abstract

Young adults are increasingly vulnerable to depression and anxiety due to educational, social
and economic pressures, but stigma and limited access to care often prevent early intervention.
This study evaluates the acceptability and performance of a conversational AI-based screening
tool that integrates the validated Patient Health Questionnaire-9 (PHQ-9) and Generalized
Anxiety Disorder-7 (GAD-7) scales. Using a cross-sectional experimental design, data were
collected from young adults aged 18–35 interacted with the natural language chatbot. A total
of 950 young adults participated in this study, contributing conversational text and standardized
scores. Preprocessed responses were analyzed using a support vector machine (SVM) classifier
to distinguish between crisis (+1) and non-crisis (-1) expressions. The model achieved a
precision of 84.07%, recall of 83.62%, and F1 score of 83.84%, confirming its reliability in
identifying emotional distress. Participants reported greater comfort and openness when
communicating with chatbots compared to traditional self-report formats, indicating increased
engagement and reduced stigma. The findings highlight the potential of conversational AI to
improve early mental health screening and provide accessible, non-judgmental support. The
study recommends ethical integration of AI-powered screeners into consultation and
telemedicine systems to expand scalable, privacy-preserving mental health care for young
adults.