A reinforcement learning-based adaptive evaluation framework for personalized music education

Evaluation of student performance in music education is recognized as a persistent challenge. The current methods of assessment, such as peer feedback, instructor observations, and rubric-based grading, are subjective and fail to deliver prompt and personalized feedback. Previous approaches have largely relied on static evaluation schemes like standardized grading rubrics and subjective instructor judgments like qualitative assessments without real-time feedback, but they failed to accommodate individual learning differences and dynamic adjustment of instructional strategies based on a student’s evolving abilities. To address these limitations, this study demonstrates a reinforcement learning-based adaptive evaluation framework designed for personalized music education. To optimize teaching interventions and customize learning experiences, the framework incorporates a task-selecting evaluation agent, a dynamic student model, and a continuous feedback mechanism. A Deep Q-Network (DQN) agent processes performance metrics like technical proficiency, expressiveness, sight-reading ability, and interpretative skills in real-time to suggest appropriate tasks and provide personalized feedback. A simulated dataset of students was used to train and test the model with different hyperparameters. These parameters are optimized through grid search and validation techniques. The results demonstrate that the proposed framework significantly outperforms baseline models, which include Q-learning, Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), Trust Region Policy Optimization (TRPO), Deep Deterministic Policy Gradient (DDPG), in terms of cumulative rewards, learning efficiency, and evaluation accuracy. Furthermore, the proposed framework exhibits superior adaptability by continuously updating student models and task recommendations based on ongoing performance data. This work offers a practical and scalable solution for personalized music education, contributing a step towards artificial intelligence (AI)-driven adaptive teaching systems in skill-based learning environments.