Artificial Intelligence-Based Smart Dance Resources for a Quality Education System

Abstract

The field of dance education at college and university levels has not yet fully embraced the use of artificial intelligence (AI). Recent years have seen widespread use of AI in many fields, including academia. The use of AI-based smart resources in dance education has the potential to significantly improve the quality of learning experiences, given the importance of dance to both cultural expression and physical exercise. This study is to investigate the feasibility of incorporating AI technology into dance education to develop intelligent dance resources that can accommodate a wide range of students, and enhance teaching strategies. The purpose of this study is to address these issues by defining them, analysing potential solutions for using AI in college dance instruction, and providing empirical evidence of AI's positive impact on this field using Hybrid Dwarf Mongoose Optimised Improved Fuzzy Neural Network (HDMO-IFNN). To create a 3D model library that is relevant to dance teaching material, the study may use AI to gather data such as the skills, movements, and more of the dance instructors, digitize it, and then construct the library. The gathered data is then pre-processed using Principle Component Analysis (PCA) to extract the features. For an excellent education program, the study settled on using a HDMO-IFNN. The findings of this study provide current knowledge into the integration of novel methods in higher dance education. Results demonstration that the proposed method provides higher classification than existing methods like accuracy of 98%, precision of 97%, effort of 98, and F1-Score of 96%.

Keywords: Education system, smart dance resources, Principal component analysis, Hybrid Dwarf Mongoose Optimised Improved Fuzzy Neural Network (HDMO-IFNN), university