Design and Evaluation of a Culturally Adapted Multimodal Virtual Agent for PTSD Screening

2026-04-20

AI Summary

This work addresses the high prevalence yet severe underreporting of post-traumatic stress disorder (PTSD) among combat-exposed military personnel by proposing Molhim, an automated PTSD screening platform tailored for military healthcare settings. Molhim employs a high-fidelity virtual avatar to conduct multi-turn, structured clinical interviews, integrating a large language model–driven dialogue engine, real-time speech recognition and synthesis, and visual input understanding within a multimodal framework. The system rigorously adheres to DSM-5 criteria by administering the PCL-5 assessment protocol. Innovatively, it embeds cultural adaptability into the virtual agent’s design, introduces a configurable, goal-oriented dialogue pipeline, and pioneers a socially collaborative human–AI paradigm for mental health screening. Empirical evaluation demonstrates Molhim’s feasibility in delivering reliable and efficient automated PTSD screening in military environments.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Post-traumatic stress disorder (PTSD) is highly prevalent yet chronically underreported among combat-exposed military personnel. This paper presents Molhim, a culturally adapted multimodal conversational AI platform that supports purpose-specific interactions through a configurable conversational pipeline consisting of session setup, real-time dialogue with a high-fidelity virtual avatar, and post-session analysis and feedback. In this work, we examine the PTSD screening configuration of the Molhim platform in a military healthcare context. The system employs a conversational avatar driven by a large language model, integrating real-time speech recognition, visual understanding of user input, text-to-speech synthesis, and a high-fidelity human avatar to support structured multi-turn dialogue and automated post-session analysis, including administration of the PTSD Checklist for DSM-5 (PCL-5). These findings suggest the feasibility of Molhim as a conversational platform for PTSD screening and highlight design considerations for socially cooperative human-AI systems in clinical environments.

Problem

• PTSD screening
• military personnel
• underreporting
• culturally adapted
• virtual agent

Innovation

• multimodal virtual agent
• culturally adapted AI
• PTSD screening
• large language model
• conversational AI