Development of an AI-Powered Virtual Lab for Pharmaceutical Fermentation Education

Aim/Background: This study aims to develop an AI-enhanced virtual lab framework. It addresses the high costs and operational risks of traditional biopharmaceutical fermentation engineering education. The proposed solution integrates virtual simulations, real-time parameter optimization, and adaptive learning tools to provide scalable vocational training. Materials and Methods: A quasi-experimental study was conducted with 100 participants, comparing an AI-powered virtual training group against a conventional training control group. The framework incorporated dynamic simulation models, AI-driven process optimization, and real-time performance feedback. Results: The AI group demonstrated significant improvements, including a 35.4% increase in product yield (88% vs. 65%) and a 57.3% reduction in fault resolution time (3.5) 3.5 n, and real-time performance. The framework reduced reliance on physical equipment while enhancing training efficiency through immersive simulations and real-time skill assessment. Conclusion: The AI-powered virtual lab effectively bridges the gap between technical education and industrial biomanufacturing demands, offering a cost-effective, scalable, and adaptive solution for vocational skill development in fermentation engineering.