AI-ASSISTED NOTATION SYSTEMS IN MUSIC PEDAGOGY - Panigra, Abhijeet; Korwar, Ganesh; R P S Chauhan; Aswitha V; Sahoo, Somanath; Nagargoje, Pooja (ShodhKosh: Journal of Visual and Performing Arts, 2026)
In this paper, an AI-assisted notation system is suggested to assist beginners, intermediate, and advanced musicians in the creation of scores with the help of intelligent score generation, a real-time experience, and an intelligent, pedagogically adjusted approach. The paper discusses the drawbacks of traditional teaching notation such as a slow response, fixed display, and and heavy workload among beginners. The proposed method uses a combination of signal processing with pitch, rhythm, and timbre determination and deep learning models such as convolutional and recurrent models and transformer-based models to attain accurate results in automatic music transcription and expressive analysis. MIDI and MusicXML are symbolic representations that are used to ensure educational readability and interoperability. Training of the models is done using multi-instrument data using stratified validation in various levels of learner proficiency. Assessment is based on the accuracy of transcription, precision of rhythm, comprehensibility of the notation, and pedagogical convenience using structured tasks of learners. The experimental data prove that an AI-assisted system has a mean pitch accuracy of 91.6, a rhythmic accuracy of 88.3, and a learning error based on the notation is much higher (27 percent in comparison with traditional training). Students with adaptive notation have an increase in their speed of sight-reading of 22 percent and an increase in their engagement scores, especially with beginners.