Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems demand reliable hazard identification. While large language models (LLMs) have shown promise in automating safety analysis tasks, single-turn, monolithic inference is brittle: it lacks the self-correction, deliberation, and contextual refinement that safety engineers apply iteratively. In this paper, we introduce HAZDIAL, a framework that investigates whether structured agentic dialogue (multi-agent, multi-turn interactions) improves the quality of NLP-based hazard identification over single-pass baselines. We systematically compare two dialogue modalities: adversarial debate and constructive discussion, and propose an genetic algorithm-based agentic interaction optimization. We evaluate all configurations against a curated golden dataset using standard classification metrics (accuracy, precision, recall, F1) and a novel dialogue metrics. This work advances the intersection of dialogue systems, multi-agent reasoning, and AI safety, providing empirical evidence for dialogue-driven hazard analysis.

Current smoke opacity certification methods are environmentally harmful and logistically inefficient, requiring real emissions, travel, and time off work. In this study, we present a Virtual Reality (VR) training and assessment system that simulates black and white smoke emissions in an immersive, controlled environment, designed to mirror real-world training and testing standards. Participants in the VR group completed two training sessions and a standardized test adapted from an official certification exam. Their performance was compared to a control group that received traditional lecture-based training and paper-based testing. Certification required a test score below 37 for both smoke colors. The VR group achieved mean scores of 33.44 (black smoke) and 57.75 (white smoke), outperforming the control group, which scored 46.00 and 72.00 respectively. The VR group significantly outperformed the control on black smoke$(p<0.05)$achieving passing scores on nearly all tested opacities ($<3$points (15%) deviation per reading). Subjectively, VR participants reported feeling more confident, better prepared, and found the application enjoyable. These results suggest VR is an effective, scalable alternative to traditional training, enhancing performance, increasing user satisfaction, and reducing environmental impact.