Deep learning-based personalized learning path planning and optimization model for music education

Music education is an art that emphasizes the perception, expression, and personalized cultivation of musical skills. Traditional approaches often fail to address individual differences in students’ learning pace, style, engagement, and domain-specific skill mastery. To overcome these limitations, this research proposes a Personalized Learning Path Planning and Optimization Model for Music Education. The model integrates the Artificial Lizard Search-driven Enriched deep neural networks with Attention mechanism (ALS-EDNN-ATT) framework to dynamically recommend personalized learning paths. Experiments were conducted on a structured music learning dataset comprising prior knowledge attributes, vocal and instrumental performance accuracy, learning preferences, practice behavior, and engagement indicators collected across novice, intermediate, and advanced learners. Data were normalized using min–max scaling.The EDNN serves as the predictive backbone, while the attention mechanism emphasizes domain-relevant features to improve learning behavior prediction. Learning outcomes are framed as a multiclass classification problem, where vocal learners are categorized as novice (pre-test 75), and instrumental learners as novice ( 5 years). The ALS optimization continuously evaluates progress, engagement, and feedback to adapt learning modules, task difficulty, and practice frequency. The experiment was implemented by using Python 3.11 and hypothesis analysis SPSS 27.0. A falsification-based hypothesis framework was developed and validated using one-way and two-way ANOVA. ANOVA validation shows ALS-EDNN-ATT significantly improves learning: prior knowledge F = 15.42, engagement F = 12.11, skill F = 11.34, sequencing F = 13.22, difficulty F = 10.18, feedback F = 11.76. Achieving an accuracy of 0.984, the model enables adaptive, efficient, and scalable personalized music education.