The potential of music to activate the brain and enhance learning has been widely recognized (Mogi, 2008), yet its application in English education remains underexplored. Grammatical errors in popular songs can mislead students, and the need for specialized knowledge to create educational songs poses a challenge for teachers (Liu, 2023). AI music generation technology offers a convenient solution by creating songs suitable for educational purposes. While its benefits in adult English education have been demonstrated (Takano, 2024; Liu, 2024), its effectiveness for young students requires further research.

This study conducted a two-month empirical investigation with 90 third-grade students from primary schools in Tianchang City, Anhui Province, China, divided into an experimental group and a control group, each with 45 students. The experimental group was taught using AI-generated English songs, while the control group continued with traditional methods. Results indicated significant improvements in vocabulary retention and comprehension in the experimental group, with more stable and lasting learning outcomes (correlation coefficient: experimental group 0.48, control group 0.34).

The study confirms the substantial potential of AI-generated songs to enhance primary school English learning and significantly increases student interest and engagement in the classroom. We recommend broader adoption of AI-generated music in education to explore more effective teaching models and foster innovation in educational technology.

Authors:
Lingling Liu, ProoProo Co.,Ltd., Japan
Yuanyuan Zhang, Binghui Primary School, China

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.

Connect on Linkedin
https://researchmap.jp/liulingling/books_etc?lang=en

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