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<p>Personalised music education necessitates adaptive instruction that responds to each learner’s evolving skill set, emotional state, and stylistic preferences. Yet, most existing intelligent tutoring systems and AI-driven platforms are limited in their ability to address the sequential, expressive, and affective complexity inherent in music learning. This paper presents a novel Deep Reinforcement Learning (DRL) framework designed to overcome these limitations through real-time multimodal learner modelling, affect-cognitive reward optimisation, and policy adaptation via ensemble methods and curriculum-aware exploration. The proposed approach formalises the instructional process as a Markov Decision Process (MDP), utilising a rich, multidimensional learner state that encompasses musical proficiency, practice behaviour, affective signals (including engagement and frustration), and individual learning style. Notably, the framework intro-duces a dual-reward mechanism that balances musical task performance with emotional engagement, thereby promoting sustained learner motivation. Policy learning is further enhanced by employing an ensemble of Actor–Critic agents, coordinated by a curriculum-based scheduler to facilitate smoother skill progres-sion and improved generalisation across diverse learner profiles. Empirical results from both simulated environments and controlled pilot studies indicate that the proposed method significantly outperforms baseline DRL and conventional adap-tive systems in skill retention, time-to-proficiency, and learner satisfaction. This research contributes to ongoing efforts toward emotionally aware and learner-centred music tutoring systems.</p>

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Deep Reinforcement Learning for Personalised Music Learning Pathways

  • 期刊论文

  • 发表日期：

    2026年3月27日

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  • Ni Li,
  • Zixiang Hong

摘要

Personalised music education necessitates adaptive instruction that responds to each learner’s evolving skill set, emotional state, and stylistic preferences. Yet, most existing intelligent tutoring systems and AI-driven platforms are limited in their ability to address the sequential, expressive, and affective complexity inherent in music learning. This paper presents a novel Deep Reinforcement Learning (DRL) framework designed to overcome these limitations through real-time multimodal learner modelling, affect-cognitive reward optimisation, and policy adaptation via ensemble methods and curriculum-aware exploration. The proposed approach formalises the instructional process as a Markov Decision Process (MDP), utilising a rich, multidimensional learner state that encompasses musical proficiency, practice behaviour, affective signals (including engagement and frustration), and individual learning style. Notably, the framework intro-duces a dual-reward mechanism that balances musical task performance with emotional engagement, thereby promoting sustained learner motivation. Policy learning is further enhanced by employing an ensemble of Actor–Critic agents, coordinated by a curriculum-based scheduler to facilitate smoother skill progres-sion and improved generalisation across diverse learner profiles. Empirical results from both simulated environments and controlled pilot studies indicate that the proposed method significantly outperforms baseline DRL and conventional adap-tive systems in skill retention, time-to-proficiency, and learner satisfaction. This research contributes to ongoing efforts toward emotionally aware and learner-centred music tutoring systems.