Li Wen, Cui Xuerong, Manoharan Pravina, Dai Lu, Liu Ke, Huang Li
School of Teacher Education, Aba Teachers College, Wenchuan, Sichuan, China.
School of the Arts, Universiti Sains Malaysia, Penang, Malaysia.
Front Psychol. 2025 Aug 18;16:1598867. doi: 10.3389/fpsyg.2025.1598867. eCollection 2025.
Metacognition plays a vital role in enhancing learning outcomes and has received increasing attention in recent years. Studies have shown that accomplished musicians typically demonstrate high levels of metacognition, and that reflection and feedback are effective strategies for promoting metacognitive development. This study explores the impact of integrating artificial intelligence (AI) and e-learning tools into vocal music training. It focuses on feedback and reflection interventions aimed at enhancing the metacognitive abilities and singing performance of pre-service teachers.
An experimental design was employed over a six-week training period. Participants were randomly divided into a control group ( = 42), which received conventional singing instruction, and an experimental group ( = 38), which received additional interventions comprising: (a) self-assessment through the use of an audio comparison tool, (b) dialogic feedback through interaction with a large language model (Yuanbao, Tencent's generative AI chatbot), and (c) engagement in self-reflective journal writing. A two-way repeated measures ANOVA was employed to examine the interaction effects between time (pre-test vs. post-test) and group (experimental vs. control). In addition, linear mixed models were used to analyse the relationship between metacognitive abilities and singing performance.
The results demonstrated that AI-assisted training significantly affects the development of metacognitive abilities. While both the experimental and control groups exhibited significant improvements in singing performance following the intervention, no significant interaction effect between the group and time was detected. No correlation was found between metacognition and singing performance.
The significance of this study is its provision of an effective implementation framework for integrating AI and e-learning tools into music instructional practice. These technologies offer high-quality personalized feedback and foster deep reflective engagement, thereby supporting the metacognitive development process in music education contexts.
元认知在提高学习成果方面起着至关重要的作用，近年来受到了越来越多的关注。研究表明，有成就的音乐家通常表现出高水平的元认知，并且反思和反馈是促进元认知发展的有效策略。本研究探讨了将人工智能（AI）和电子学习工具整合到声乐训练中的影响。它侧重于旨在提高职前教师元认知能力和演唱表现的反馈和反思干预措施。
在为期六周的训练期间采用了实验设计。参与者被随机分为对照组（n = 42），接受传统的歌唱指导，以及实验组（n = 38），接受额外的干预措施，包括：（a）通过使用音频比较工具进行自我评估，（b）通过与大语言模型（腾讯的生成式人工智能聊天机器人元宝）互动进行对话式反馈，以及（c）参与自我反思日记写作。采用双向重复测量方差分析来检验时间（前测与后测）和组（实验组与对照组）之间的交互作用。此外，使用线性混合模型来分析元认知能力与演唱表现之间的关系。
结果表明，人工智能辅助训练显著影响元认知能力的发展。虽然实验组和对照组在干预后演唱表现均有显著提高，但未检测到组与时间之间的显著交互作用。未发现元认知与演唱表现之间存在相关性。
本研究的意义在于为将人工智能和电子学习工具整合到音乐教学实践中提供了一个有效的实施框架。这些技术提供高质量的个性化反馈，并促进深入的反思参与，从而支持音乐教育背景下的元认知发展过程。
