An ICL–GraphRAG decision-support framework for context-aware stress detection and intervention recommendation: a vessel traffic service case study
Year: 2026
Type: article
Abstract: 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 (GraphRAG) module for explainable stress detection and personalized intervention. The ICL component enables few-shot, context-aware stress detection by fusing physiological signals with multi-contextual information, and the GraphRAG module grounds explanations and intervention recommendations in structured domain knowledge, reducing hallucination in large language models (LLM). The framework is validated through a Vessel Traffic Service (VTS) case study, using multimodal data collected from real-world operations over a five-month on-site deployment. Without task-specific training, the ICL-based predictor achieves an F1-score of 88.37% with only 256 examples, surpassing traditional machine learning baselines. GraphRAG constructs a domain-specific knowledge graph linking physiological indicators, operational contexts, stress states, and intervention strategies, enabling multi-hop reasoning for evidence-grounded, traceable recommendations. Expert evaluations confirm that GraphRAG-enhanced outputs significantly improve accuracy, usability, and trustworthiness over LLM-only baselines. This work advances occupational stress management by transitioning from passive monitoring toward a trustworthy, human-centred decision-support paradigm with strong potential for real-world deployment in safety-critical environments.
Source: Journal of Engineering Design
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7160200545