Advancing Workforce Development: AI-Driven Personalized Virtual Job Training with IoT Integration and SVM-Based User Profiling

In today's fast-changing digital world, talented and adaptive workers are needed more than ever. Traditional job training fails to meet workers' demands and learning styles. This paper offers a novel framework for individualized virtual job training that uses AI, IoT, and SVM-based user profiles to close this gap. The suggested system provides a personalized, immersive learning experience that adjusts to workers' requirements and career goals in real-time. AI-enhanced IoT avatars assist workers via interactive training courses to address skill gaps and professional aspirations. The system may modify training material and delivery methods by analyzing user data using an SVM-based user profile to forecast learning preferences and requirements. User engagement indicators show a considerable increase in training length and frequency, suggesting increased employee attention to professional growth. Biometric data like cognitive load and stress levels increase, indicating improved learning and less anxiety throughout training. The system's capacity to provide meaningful and engaging learning experiences is confirmed by high user satisfaction ratings that increase with time. Employees' self-reported work performance and confidence improvements demonstrate the system's effectiveness in professional growth and skill development. The SVM model's ability to anticipate user demands improves with time, enabling better training material and delivery personalization. Future research and development will improve the system's predictive powers, broaden its accessibility across sectors, and investigate new uses in specialized job training and career promotion. This research shows how individualized digital job training solutions may suit today's workforce demands.