Applications of Large Language Models in Industrial Internet of Things Education

This paper explores the potential of Large Language Models (LLMs) to enhance educational experiences in the Industrial Internet of Things (IIoT), focusing on the domain of smart steel manufacturing. We address the critical shortage of authentic industrial datasets for academic research by presenting an industry-validated IIoT educational dataset derived from three months of operational data from a steel plant, enriched with domain-specific annotations. We propose a comprehensive IIoT framework specifically designed for smart steel manufacturing, integrating LLMs with IIoT infrastructure. This novel architecture unifies real-time process monitoring, predictive analytics, and adaptive learning, simulating a smart factory ecosystem. It facilitates humanmachine collaboration and replicates quality control workflows, thereby bridging the gap between educational theory and industrial practice. A key component of our framework is an interactive decision-making dashboard powered by LLMs. This AI-driven tool contextualizes IIoT data, offering dynamic visualizations of critical parameters such as metallization levels and H2/CO ratios. It further enables scenario-based simulations for energy optimization in a risk-free environment, empowering learners to experiment and understand complex industrial processes. Our study demonstrates the transformative potential of LLMs in HoT education, offering a practical and engaging learning environment that directly aligns with industrial realities.