The Complementary Role of Artificial Intelligence to Traditional Teaching Methods in Music Education and Its Educational Effectiveness

In the field of music education, the application of artificial intelligence technology is gradually changing the traditional teaching mode, providing new opportunities and challenges for music education. In this paper, we use artificial intelligence technology to build a smart classroom for music teaching and combine it with a user-based collaborative filtering recommendation algorithm to provide students with personalized music learning materials. Moreover, a treble feature extraction model is integrated into the smart classroom, and the DTW improvement algorithm is used to match the students’ treble features, and the student’s mastery of music skills in the smart classroom is evaluated through the sight-singing scoring technology. Students’ overall satisfaction ratings for the music teaching mode in the smart classroom designed in this paper were 4.35 to 4.60, and only a very few students disliked the teaching mode. The personalised recommendation system built in this paper has a precision rate, recall rate and F-value of 0.50, 0.41 and 0.38, respectively, when the number of recommendations is 50, and it can provide students with personalised music learning materials suitable for them. After the experiment, the average scores of the experimental class on pitch, rhythm, sight-reading ability, music notation, and polyphonic music perception increased by 7.72, 6.37, 7.82, 6.92, and 8.16 points, respectively, compared with the control class. In this paper, the difference between the intelligent scoring system and the teacher’s scores on the “pitch” scores is 0.036~4.903. Artificial intelligence technology provides an effective supplement to traditional music teaching and improves the personalization, efficiency, and quality of teaching.