The potential of music to activate the brain and enhance learning is widely recognized (Arleo, 2019; Hidayati, 2020), yet its application in English education remains underexplored. Grammatical errors in popular songs can mislead students, and creating educational songs requires specialized knowledge, posing challenges for teachers (Liu, 2023). AI music generation technology offers a convenient solution by tailoring songs for educational purposes. While its benefits in English education have been demonstrated (Takano, 2024; Liu, 2024), its effectiveness in pronunciation instruction requires further research. This study conducted a two-week investigation involving 24 university students in China, divided into experimental and control groups of 12 students each. The experimental group used AI-generated English songs, while the control group followed traditional methods. Results indicated significant improvements in pronunciation accuracy within the experimental group, leading to more stable and enduring learning outcomes. There was a significant difference in scores between the experimental and control groups (p < 0.05). The study confirms the potential of AI-generated songs to enhance English pronunciation learning, particularly by increasing student engagement and motivation. These findings suggest that AI-generated music could complement traditional language learning methods, leading to more effective and enjoyable educational experiences. Further research with larger samples and in diverse contexts is recommended to fully realize AI’s benefits in language education and explore its broader applications.

Authors:
Lingling Liu, Prooproo Co., Ltd., Japan

About the Presenter(s)
Lingling Liu is the CEO of ProoProo Co., Ltd. Lingling Liu's main research areas include music generation AI, language education, educational method development, and teaching material development. hobbies include playing the koto, guitar, and piano.

Additional website of interest
https://researchmap.jp/liulingling/books_etc?lang=en

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