Trauma Care in the Age of Conversational Artificial Intelligence: Promises, Pitfalls, and Principles for Trauma-Informed Chatbots

Abstract

People exposed to trauma increasingly turn to general-purpose chatbots and mental health applications for support, often long before they reach a clinician. This shift is happening faster than the evidence, the regulation, and the clinical conversation around it. This guest editorial summarizes current knowledge on digital and conversational artificial intelligence interventions relevant to traumatic stress and evaluates what they may and may not offer trauma survivors. Conversational artificial intelligence promises scalable psychoeducation, early support after disasters, lower stigma, and between-session assistance within hybrid models of care. Yet trauma raises specific risks: uncontrolled activation of traumatic memories, missed crisis signals, sycophantic responses that may reinforce avoidance or distorted appraisals, privacy threats to highly sensitive narratives, and an evidence base that still lacks trauma-specific trials. These issues are discussed with reference to Türkiye and neighboring regions, where post-earthquake needs, displacement, and a limited specialist workforce make low-cost digital tools especially attractive. The editorial proposes five principles for trauma-informed conversational artificial intelligence: stabilization before processing, reliable crisis detection with human escalation, clinician-in-the-loop design, culturally and linguistically validated development, and trauma-specific outcome and harm monitoring. Clinicians and researchers are invited to shape these tools rather than react to them.