3 papers
cs.HC2026
Engagement Phenotypes for a Sample of 102,684 AI Mental Health Chatbot Users and Dose-Response Associations with Clinical Outcomes
Emma C. Wolfe, Ting Su, Olivier Tieleman +3
Background: Conversational AI chatbots are emerging as scalable mental health tools, but little is known about real world engagement or its relationship to clinical outcomes. Objec…
cs.HC2026
Functional outcomes and naturalistic engagement with a purpose-built conversational AI for mental health (Ash)
Kristen M. Van Swearingen, Thomas D. Hull, Karthik V. Sarma +1
Background: Conversational AI chatbots designed for mental health may offer an accessible, scalable avenue for supporting psychological well-being, yet prior evaluations have large…
cs.CL2026
Fine-tuning LLMs for Passive Depression Severity Estimation from AI Mental Health Dialogue
Olivier Tieleman, Ziyi Zhu, Ting Su +3
Depression is the leading cause of disability worldwide, and early detection of symptom change is essential for timely intervention. Validated instruments such as the Patient Healt…