A RAG-Based Decision Support System for Occupational Exposure Assessment of VOCs: Design, Implementation, and Evaluation

Abstract

Introduction: Assessing exposure to volatile organic compounds (VOCs) in workplaces requires timely access to accurate, standardized guidance on sampling and analysis. This study aimed to develop and evaluate a decision-support chatbot, based on the Retrieval-Augmented Generation (RAG) architecture, to assist occupational health professionals in selecting appropriate sampling and analysis methods for VOCs in accordance with NIOSH guidelines. Material and Methods: This research was of an applied-developmental type. In this study, relevant NIOSH methods for sampling and analysis of VOCs were extracted, structured, and organized into a knowledge base. User needs were identified through semi-structured interviews with occupational health professionals. Based on this data, a generative AI-based chatbot was designed and implemented using the RAG architecture. The system performance was evaluated using expert evaluation and a chatbot usability questionnaire. Results: The developed chatbot received an overall user-friendliness score of 3.88 out of 5, indicating good user acceptance. Participants noted improved access speed for standard technical information and the high transparency of the system’s responses. Expert evaluation also confirmed the scientific validity of the retrieved content and its compliance with NIOSH standards. Conclusion: The proposed RAG-based decision-making chatbot provides an effective and innovative tool to support occupational exposure assessment to VOCs. Integrating generative AI with standard occupational health guidelines can improve the efficiency of decision-making and access to expert information in practice.