Blockchain-enabled assessment of dance education outcomes with transparent credentialing and AI-based performance analytics

Abstract

This framework is a hybrid combination of AI and blockchain for dance performance assessment. The model it uses, the hybrid CNN/LSTM model, evaluates posture accuracy and timing for the posture, while style recognition requires a Transformer-based encoder to learn long-range dependencies. The Expressiveness Index measures the expressiveness and dynamics shown by a performer through a regression head. A blockchain ensures that the performance results can secure and verifiable credentials without forging them. Real-time feedback helps dancers to improve faster and more effectively. The model is trained on a variety of dance styles to ensure that it is flexible and applicable to different forms of dance training. The evaluation achieved high-performance metrics, showing the strength of the system for objective performance measurements, accuracy of 95.52%, Precision 94.56%, Recall 94.87%, and F1-Score of 94.71%. In the exciting angle of enhancing the integration of AI and blockchain into the performing arts and their visual presentation, TransCNN + DSSS signifies a secure, transparent, and scalable means of providing dance education.