Autonomous AI-Driven Skill Assessment for Apprenticeship Training

Abstract

Modern apprenticeship programmes benefit from AI-powered assessment systems that dynamically adjust skill evaluations based on real-time performance metrics. This study presents an autonomous intelligent framework that utilises deep learning models and cognitive algorithms to evaluate apprentices’ competencies in practical, hands-on environments. By analysing multimodal data—such as task completion time, accuracy, and decision-making efficiency—the system provides real-time feedback and customised skill-building recommendations. Results indicate that AI-driven evaluations outperform traditional static assessments, leading to faster skill acquisition and improved apprentice performance outcomes.