Abstract For a long time, dance education in Chinese universities has relied on teachers watching students and students practicing over and over again. This method often makes it hard to give objective feedback, correct mistakes quickly, and give personalized feedback, especially in big or diverse classes. In these circumstances, it is challenging to detect and rectify subtle biomechanical and rhythmic deviations using traditional teaching methods. Recent developments in artificial intelligence (AI) and wearable sensor technologies provide an alternative by facilitating continuous motion capture, quantitative movement analysis, and data-driven instructional support. This project creates an AI-based Dance Movement Teaching Support System (DM-TSS) that aims to improve the accuracy of motion acquisition, the reliability of feedback, and the effectiveness of instruction in higher education dance training. The system combines wearable inertial sensors with deep learning and reinforcement learning models to analyze dance movements that involve more than one joint in real time and give personalized feedback. We present a new framework called Namib Beetle Optimization–Twi
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