Designing a Human-in-the-Loop LLM Chatbot for Interactive Depression Screening

Abstract

Depression remains a major global mental health challenge, and early screening still depends largely on self-report questionnaires, despite their vulnerability to response bias and limited insight into the response process. This study examined the feasibility of a human-in-the-loop, LLM-based chatbot for depression screening under real-time supervision by a licensed psychologist. Twenty-one participants completed the DASS-21 in two digital formats: a static form and a conversational chatbot interface. In the chatbot condition, LLM-generated follow-up questions were reviewed by the psychologist before delivery. Interaction data were collected in both conditions using a digital phenotyping screening tool. The results suggest that the chatbot is a usable alternative for administering screening instruments and that process-level behavioral traces may add contextual information beyond final questionnaire responses. These findings support the potential of clinician-supervised chatbot workflows for enriching depression screening, while highlighting the need for further validation of the clinical relevance of such behavioral signals.