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

Young adults are increasingly vulnerable to depression and anxiety due to educational, socialand 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 screeningtool that integrates the validated Patient Health Questionnaire-9 (PHQ-9) and GeneralizedAnxiety Disorder-7 (GAD-7) scales. Using a cross-sectional experimental design, data werecollected from young adults aged 18–35 interacted with the natural language chatbot. A totalof 950 young adults participated in this study, contributing conversational text and standardizedscores. Preprocessed responses were analyzed using a support vector machine (SVM) classifierto distinguish between crisis (+1) and non-crisis (-1) expressions. The model achieved aprecision of 84.07%, recall of 83.62%, and F1 score of 83.84%, confirming its reliability inidentifying emotional distress. Participants reported greater comfort and openness whencommunicating with chatbots compared to traditional self-report formats, indicating increasedengagement and reduced stigma. The findings highlight the potential of conversational AI toimprove early mental health screening and provide accessible, non-judgmental support. Thestudy recommends ethical integration of AI-powered screeners into consultation andtelemedicine systems to expand scalable, privacy-preserving mental health care for youngadults.