Vision-based skill assessment and personalized guidance for modern apprenticeship

Modern apprenticeship training faces critical scalability challenges due to limited expert instructor availability and subjective assessment inconsistencies, hindering skilled labor development amid global workforce shortages. This paper presents a vision-based skill assessment and personalized guidance system integrating multi-view Vision Transformer architectures with reinforcement learning for adaptive vocational training. The system employs a multi-view fusion encoder with cross-attention mechanisms that dynamically integrates information from spatially distributed cameras, coupled with hierarchical temporal action segmentation networks and multi-task assessment heads evaluating posture correctness, motion quality, tool manipulation proficiency, and outcome quality simultaneously. A Deep Q-Network trained on historical apprenticeship data optimizes personalized learning paths respecting prerequisite dependencies while maximizing skill acquisition efficiency. Experimental validation on Assembly101 dataset and our Vocational Skill Assessment Dataset (VSAD) containing 1,450 recordings from 78 apprentices across precision machining, welding, and carpentry demonstrates that the system achieves 91.2% action recognition accuracy and 5.4 mean absolute error in skill assessment, substantially outperforming traditional machine learning (Random Forest: 68.5%, 15.2 MAE) and deep learning methods (3D-CNN: 85.3%, 8.3 MAE). Ablation studies confirm multi-view fusion with cross-attention contributes 8.5 percentage points improvement over single-view baselines. The system maintains robust performance across skill levels from beginners (93.8%) to advanced practitioners (88.6%), providing scalable expert-level assessment and personalized instruction for modern apprenticeship training.