A conversational agent framework for mental health screening
Design, implementation, and usability
Bibliographic Data
| ID | 21452069 |
|---|---|
| Authors | Rares Boian (0000-0002-0993-8842, Babeș-Bolyai University), Ana-Maria Bucur (0000-0003-2433-8877, University of Bucharest), Diana Todea (University of Bucharest), Andreea Luca (University of Bucharest), Andreea Roxana Luca (0000-0002-0645-4228, University of Bucharest), Traian Rebedea (0000-0002-7255-5537, Universitatea Națională de Știință și Tehnologie Politehnica București), Ioana R Podina (0000-0002-0075-4908, University of Bucharest, corresponding author) |
| Year | 2025 |
| Volume | 44 |
| Issue | 10 |
| Pages | 2364-2378 |
| Publication date | 2025-06-15 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Behaviour and Information Technology (JOURNAL) |
| Journal identifiers | ISSN: 0144-929X • E-ISSN: 1362-3001 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/0144929x.2024.2332934 |
| OpenAlex | W4393222804 |
| Language | EN |
| Citations received | 1 |
| References cited | 37 |
While chatbots show promise for large-scale mental health screening, few offer interactive, free-text conversations, limiting their appeal for self-administered screening and impeding the timely detection of mental health issues. This study introduces an AI-based chatbot that allows users to respond to validated screening surveys for mental disorders (PHQ-9, GAD-7, and PCL-5) in a natural, free-text conversation manner with real-time feedback. The study's objectives include evaluating the chatbot's usability and reducing the frequency of response clarifications while accurately interpreting users’ responses. The system was assessed running in hybrid NLU mode (Phase 2; N = 587; Mage = 21.56, SD = 5.56, 67.8% women) after being trained on data collected while running in rule-based mode (Phase 1; N = 274; Mage = 21.86, SD = 5.50). During user-chatbot interactions, the chatbot required clarification only 4.64% of the time. Using the AI NLU model, the chatbot could understand user responses in 85.65% of cases and interpret free-text similarly to human annotators. In terms of usability, the chatbot in hybrid NLU mode was perceived as more engaging, friendly, and easier to use than in the rule-based NLU mode, which may be indirectly attributed to the enhanced autonomy provided by the AI NLU model
Cognitive science · Human–computer interaction · Knowledge management · Mental health · Mental model · Psychotherapist · Usability · Computer Science · Digital Mental Health Interventions · Mental Health Research Topics · Mental Health via Writing · Psychology
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| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |