Exploring a digital music teaching model integrated with recurrent neural networks under artificial intelligence.

This study proposes an intelligent digital music teaching model based on Artificial Intelligence (AI) and Long Short-Term Memory (LSTM) networks to enhance personalized assessment and feedback in music education. Within this teaching model, the music evaluation module employs a three-layer Bidirectional LSTM (Bi-LSTM) combined with an attention mechanism, effectively capturing the long-term sequential features of Musical Instrument Digital Interface (MIDI) music to support the assessment of students' musical performances. In comparative experiments with multiple models, the three-layer Bi-LSTM achieved a final accuracy of 91.9%, significantly outperforming other models and validating the advantages of deep network structures in complex tasks. Further comparisons of precision, recall, and F1-score demonstrated that the average values for the three-layer Bi-LSTM model reached 0.87, 0.854, and 0.86, respectively, showcasing superior classification accuracy and stability. A usability survey indicated that both piano teachers and students rated the overall satisfaction, teaching effectiveness feedback, user experience, teaching engagement, and ease of use of the model above 4.0, highlighting its excellent applicability and potential for broader adoption in teaching practice. This study provides a novel approach and practical reference for AI-driven personalized music education.