Using GenAI to Train Mental Health Professionals in Suicide Risk Assessment: Preliminary Findings

Background: Suicide risk assessment is a critical skill for mental health professionals (MHPs), yet traditional training in this area is often limited. This study examined the potential of generative artificial intelligence (GenAI)based simulator to enhance self-efficacy in suicide risk assessment among MHPs. Methods: A quasiexperimental mixed-methods study was conducted. Participants interacted with an AI-based simulator (AIBS) that embodied the role of a patient seeking suicide risk assessment. Each participant conducted a real-time risk assessment interview with the virtual patient and received comprehensive feedback on their assessment approach and performance. Quantitative data were collected through pre- and postintervention questionnaires measuring suicide risk assessment self-efficacy and willingness to treat suicidal patients (using 11-point Likert scales). Qualitative data were gathered through open-ended questions analyzing participants’ experiences, perceived benefits, and concerns regarding the AI simulator. Results: Among the 43 participating MHPs, we found a significant increase in self-efficacy scores from preintervention (mean = 6.0, SD = 2.4) to postintervention (mean = 6.4, SD = 2.1, P< .05). Willingness to treat patients presenting suicide risk increased slightly from (mean = 4.76, SD = 2.64) to (mean = 5.00, SD = 2.50) but did not reach significance. Participants reported positive experiences with the simulator, with high likelihood to recommend to colleagues (mean = 7.63, SD = 2.27). Qualitative feedback indicated that participants found the simulator engaging and valuable for professional development. However, participants raised concerns about overreliance on AI and the need for human supervision during training. Conclusion: This preliminary study suggests that AIBSs show promise for improving MHPs’ self-efficacy in suicide risk assessment. However, further research with larger samples and control groups is needed to confirm these findings and address ethical considerations surrounding AI use in suicide risk assessment training. AI-powered simulation tools may have potential to increase access to training in mental health, potentially contributing to global suicide prevention efforts. However, their implementation should be carefully considered to ensure they complement rather than replace human expertise.