基于ICL和图增强检索生成的决策支持框架

In safety-critical environments characterized by volatility, uncertainty, and complexity, operators are persistently exposed to elevated stress levels, posing significant threats to both operational safety and operator well-being. Existing AI-driven stress detection approaches rely on task-specific supervised models with limited scalability and lack the explainability and reliability required for high-stakes applications. This paper introduces a trustworthy hybrid human–agentic AI framework integrating In-Context Learning (ICL) with a Knowledge Graph-enhanced Retrieval-Augmented Generation (Gr