Harnessing AI Agents to Advance Research on Refugee Child Mental Health
Journal:
arXiv
Published Date:
Jun 30, 2025
Abstract
The international refugee crisis deepens, exposing millions of dis placed
children to extreme psychological trauma. This research suggests a com pact,
AI-based framework for processing unstructured refugee health data and
distilling knowledge on child mental health. We compare two Retrieval-Aug
mented Generation (RAG) pipelines, Zephyr-7B-beta and DeepSeek R1-7B, to
determine how well they process challenging humanitarian datasets while avoid
ing hallucination hazards. By combining cutting-edge AI methods with migration
research and child psychology, this study presents a scalable strategy to
assist policymakers, mental health practitioners, and humanitarian agencies to
better assist displaced children and recognize their mental wellbeing. In
total, both the models worked properly but significantly Deepseek R1 is
superior to Zephyr with an accuracy of answer relevance 0.91
