College music education and teaching based on AI techniques

Abstract

Primary or secondary music directors, college or collaborative music directors, and music educators are trained in music education. Students in music education research and develop new methods for teaching and learning music. Through peer-reviewed publications and instruction at the University of Music for music teachers, students in the field of music conduct their research. Using artificial intelligence in the classroom is a challenge because music is an available domain that requires students and teachers to innovate and solve problems. In this paper, Music Education and Teaching based on AI (MET-AI) techniques are increasingly comprehensive with modern science and technology, enhancing music education. The use of artificial intelligence in music education breached the conventional paradigm of music education, particularly electronic music and innovative music software in private colleges, which has significantly enhanced the standard of teaching music and the teaching model for music education. The experimental results show us that the network teaching platform Music Major should be continuously improved based on artificial intelligence technologies. AI can make more optimized environments and professional music classes so that teachers and students can make the most of this and ensure smooth improvement in the network's teaching model. The MET-AI scores students’ learning outcome rate of 95.2%, an efficiency ratio of 98.1%, the mean square error rate of 17.9%, accuracy ratio of 95.3%, teaching performance analysis ratio of 90.7%, the false-positive rate of 18.6%, true positive rate 95.7% and flexibility ratio of 92,1%.