Functional outcomes and naturalistic engagement with a purpose-built conversational AI for mental health (Ash)
2026-06-26 • Human-Computer Interaction
Human-Computer Interaction
AI summaryⓘ
The authors studied a mental health chatbot called Ash to see if using it could improve everyday feelings like life satisfaction, relationship quality, and sleep over four weeks. They found that people who used Ash reported better mental functioning and stronger connections with the chatbot, without increases in overconfidence or inflated self-views. More frequent and longer use of the chatbot was linked to better outcomes, but simply sending more messages was not. This study suggests that chatbots might help with general psychological well-being, not just reduce symptoms.
conversational AImental health chatbotpsychological functioninglife satisfactionworking alliancegrandiositybehavioral activationsingle-arm observational studyengagement metrics
Authors
Kristen M. Van Swearingen, Thomas D. Hull, Karthik V. Sarma, Caitlin A. Stamatis
Abstract
Background: Conversational AI chatbots designed for mental health may offer an accessible, scalable avenue for supporting psychological well-being, yet prior evaluations have largely focused on clinical symptom reduction rather than broader indicators of day-to-day functioning, and have rarely monitored for potential harms such as inflated self-perception. Objective: We examined within-person change in psychological functioning indicators among real-world users of Ash, a purpose-built conversational AI for mental health support, over the first four weeks of use, and whether these changes were associated with engagement metrics. Methods: In this single-arm observational cohort study, new users (n = 1,284) completed in-app single-item measures of psychological functioning (life satisfaction, relationship satisfaction, sleep quality, behavioral activation), working alliance, and grandiosity (inflated self-perception), at baseline and Week 4. Paired-sample t-tests examined within-person change; ANCOVAs tested engagement-outcome associations at Week 4, controlling for baseline. Results: At baseline, participants reported below-average life satisfaction and fair sleep quality. Significant within-person improvements emerged across all functioning indicators and working alliance (ps < .001; d = 0.14-0.26), with no change in grandiosity. Active days, total sessions, and total minutes consistently predicted Week 4 psychological functioning and working alliance (ps <= .006; partial R^2 range: 0.58-2.15%; controlling for baseline), whereas user message volume did not. Conclusion: Findings provide preliminary data for the potential of evidence-based conversational AI to extend mental health support for broad psychological functioning, extending the existing literature beyond symptom-based outcomes.