Trauma-informed conversational agents for mental health: understanding user perspectives and experiences

AI Summary

Five validated trauma-informed domains for mental health chatbots: Trust and Transparency, Data Safety, Empowerment, Peer Support, and Cultural Sensitivity.
Trust, emotional validation and peer support significantly predict users perceiving chatbots as trauma informed.
Designers must align AI mental health tools with trauma-informed principles to improve therapeutic safety, credibility and inclusivity across diverse users.

AI Summary

While trauma-informed care (TIC) principles, emphasizing safety, trust, empowerment, collaboration, peer support, and cultural sensitivity, are well-established in clinical practice, their application and user interpretation in chatbot contexts remain unexplored.

This study aimed to explore (1) how users conceptualize trauma-informed care in the context of mental health chatbots and (2) what factors can help us predict whether users perceive their chatbot interaction as trauma-informed.

Findings emphasize the necessity of aligning AI-based mental health tools with trauma-informed principles to enhance therapeutic safety, credibility, and inclusivity across diverse user populations.

Basic summary

Front Digit Health. 2026 Jun 10;8:1797681. doi: 10.3389/fdgth.2026.1797681. eCollection 2026.

ABSTRACT

INTRODUCTION: Mental health conversational agents (MHCAs) offer scalable, accessible psychological support yet raise concerns about safety and appropriateness for trauma, exposed users. While trauma-informed care (TIC) principles, emphasizing safety, trust, empowerment, collaboration, peer support, and cultural sensitivity, are well-established in clinical practice, their application and user interpretation in chatbot contexts remain unexplored. This study aimed to explore (1) how users conceptualize trauma-informed care in the context of mental health chatbots and (2) what factors can help us predict whether users perceive their chatbot interaction as trauma-informed.

METHODS: A 59-item web-based survey (REDCap) assessed demographics, technology proficiency, adverse life experiences, chatbot usage, and TIC perceptions among 606 participants recruited via ClickWorker, social media, and MyChart. Exploratory and confirmatory factor analyses identified latent TIC domains. Multivariable logistic regression determined predictors of trauma-informed perception.

RESULTS: High satisfaction (84.5%) and trauma-informed perception (92.9%) were reported. Factor analyses validated a five-domain structure: Trust & Transparency, Data Safety, Empowerment, Peer Support, and Cultural Sensitivity (CFI = 0.978; RMSEA = 0.045). Trust (OR = 3.89, p = .001), Empowerment (OR = 1.97, p = .025), and Peer Support (OR = 1.73, p = .021) significantly predicted trauma-informed perception. Convenience-driven use (OR = 0.33, p = .041) and smartphone proficiency (OR = 0.38, p = .033) showed negative associations.

DISCUSSION: This study provides empirical foundation for measuring trauma-informed design in MHCAs. Trust-building, emotional validation, and peer connection significantly shape user perceptions. Findings emphasize the necessity of aligning AI-based mental health tools with trauma-informed principles to enhance therapeutic safety, credibility, and inclusivity across diverse user populations.

PMID:42359445 | PMC:PMC13291057 | DOI:10.3389/fdgth.2026.1797681

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