Being Songwriter and Singer Using Suno as AI Music Generator: Creating English Songs for Young Learners in Teaching Vocabularies
Abstract
This research simulates the use of Suno AI (https://suno.com/) in creating English songs for young learners in teaching vocabulary. This research is descriptive qualitative. The analysis shows that Suno offers AI-driven solutions encompassing song creation, sound processing, and data analysis, known for efficiently producing lifelike songs blending vocals with instruments or entirely instrumental compositions. Teachers utilize Suno AI to create English songs aimed at teaching children vocabulary, benefiting from its user-friendly tools regardless of musical expertise. This approach enhances vocabulary acquisition in engaging classroom settings, fostering creativity and emotional connections to learning materials. Teachers can customize songs to suit students' preferences and educational needs, promoting motivation and creative skills development. Using Suno AI involves visiting their website, registering, selecting a music genre and vocabulary theme (e.g., colors, numbers), and customizing generated melodies and lyrics to accommodate varying proficiency levels. The platform supports easy song creation, editing, and sharing, facilitating effective vocabulary learning through enjoyable musical experiences integrated into daily classroom activities. This innovative use of technology supports educators in enhancing language acquisition through engaging and accessible musical content, exemplified by the creation of an English children's song titled "Colors of the Rainbow." This song vividly teaches color vocabulary through descriptive lyrics and lively melodies, complemented by a "children's lively" music style that enhances its cheerful and educational impact on young learners. With Suno AI, English teachers can assume the roles of songwriters and musicians in education by utilizing its capabilities to create engaging songs tailored for teaching basic English vocabulary. Suno AI allows teachers to organize lyrics around new words, phrases, or language concepts they aim to teach, fostering a dynamic learning experience that seamlessly integrates language acquisition with musical expression. This innovative use of technology enhances educational engagement through enjoyable and effective teaching methods.
Keywords: AI Music Generator, English song, Suno AI, vocabularies
Full Text:
PDFReferences
Agwan, M., Nemade, M., Roy, S., & Sinha, U. (2023). The Fusion of AI and Music Generation: A Comprehensive Review. 2023 6th International Conference on Advances in Science and Technology (ICAST), 90–94. https://doi.org/10.1109/ICAST59062.2023.10454942
Bian, W., Song, Y., Gu, N., Chan, T. Y., Lo, T. T., Li, T. S., Wong, K. C., Xue, W., & Trillo, R. A. (2023). MoMusic: A Motion-Driven Human-AI Collaborative Music Composition and Performing System. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), Article 13. https://doi.org/10.1609/aaai.v37i13.26907
Casini, L., Marfia, G., & Roccetti, M. (2018). Some Reflections on the Potential and Limitations of Deep Learning for Automated Music Generation. 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 27–31. https://doi.org/10.1109/PIMRC.2018.8581038
Chu, H., Kim, J., Kim, S., Lim, H., Lee, H., Jin, S., Lee, J., Kim, T., & Ko, S. (2022). An Empirical Study on How People Perceive AI-generated Music. Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 304–314. https://doi.org/10.1145/3511808.3557235
Civit, M., Civit-Masot, J., Cuadrado, F., & Escalona, M. J. (2022). A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends. Expert Systems with Applications, 209, 118190. https://doi.org/10.1016/j.eswa.2022.118190
Dash, A., & Agres, K. (2024). AI-Based Affective Music Generation Systems: A Review of Methods and Challenges. ACM Comput. Surv., 56(11), 287:1-287:34. https://doi.org/10.1145/3672554
Fitria, T. N. (2021). Artificial Intelligence (AI) in Education: Using AI Tools for Teaching and Learning Process. Prosiding Seminar Nasional & Call for Paper STIE AAS, 4(1), 134–147.
Fitria, T. N. (2023a). Artificial intelligence (AI) technology in OpenAI ChatGPT application: A review of ChatGPT in writing English essay. ELT Forum: Journal of English Language Teaching, 12(1), 44–58. https://doi.org/10.15294/elt.v12i1.64069
Fitria, T. N. (2023b). The Use of Artificial Intelligence in Education (AIEd): Can AI Replace the Teacher’s Role? EPIGRAM (e-Journal), 20(2), 165–187. https://doi.org/10.32722/epi.v20i2.5711
Fitria, T. N. (2023c). Using Nursery Rhymes in Teaching English for Young Learners at Childhood Education. Athena: Journal of Social, Culture and Society, 1(2), 58–66. https://doi.org/10.58905/athena.v1i2.28
Gupta, S., Marwah, S., & Briskilal, J. (2022). AI Music Generator. Journal of Pharmaceutical Negative Results, 67–71. https://doi.org/10.47750/pnr.2022.13.S03.012
Hernandez-Olivan, C., & Beltrán, J. R. (2023). Music Composition with Deep Learning: A Review. In A. Biswas, E. Wennekes, A. Wieczorkowska, & R. H. Laskar (Eds.), Advances in Speech and Music Technology: Computational Aspects and Applications (pp. 25–50). Springer International Publishing. https://doi.org/10.1007/978-3-031-18444-4_2
Hong, J.-W., Fischer, K., Ha, Y., & Zeng, Y. (2022). Human, I wrote a song for you: An experiment testing the influence of machines’ attributes on the AI-composed music evaluation. Computers in Human Behavior, 131, 107239. https://doi.org/10.1016/j.chb.2022.107239
Kaliakatsos-Papakostas, M., Floros, A., & Vrahatis, M. N. (2020). Chapter 13 - Artificial intelligence methods for music generation: A review and future perspectives. In X.-S. Yang (Ed.), Nature-Inspired Computation and Swarm Intelligence (pp. 217–245). Academic Press. https://doi.org/10.1016/B978-0-12-819714-1.00024-5
Li, F. (2024). Chord-based music generation using long short-term memory neural networks in the context of artificial intelligence. The Journal of Supercomputing, 80(5), 6068–6092. https://doi.org/10.1007/s11227-023-05704-3
Lopez-Rincon, O., Starostenko, O., & Martín, G. A.-S. (2018). Algoritmic music composition based on artificial intelligence: A survey. 2018 International Conference on Electronics, Communications and Computers (CONIELECOMP), 187–193. https://doi.org/10.1109/CONIELECOMP.2018.8327197
Louie, R., Coenen, A., Huang, C. Z., Terry, M., & Cai, C. J. (2020). Novice-AI Music Co-Creation via AI-Steering Tools for Deep Generative Models. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–13. https://doi.org/10.1145/3313831.3376739
Ma, X., Wang, Y., Kan, M.-Y., & Lee, W. S. (2021). AI-Lyricist: Generating Music and Vocabulary Constrained Lyrics. Proceedings of the 29th ACM International Conference on Multimedia, 1002–1011. https://doi.org/10.1145/3474085.3475502
Mantaras, R. L. de, & Arcos, J. L. (2002). AI and Music: From Composition to Expressive Performance. AI Magazine, 23(3), Article 3. https://doi.org/10.1609/aimag.v23i3.1656
Mysliwiec, D. (2023, July 7). AI-Composed Music for User Preference Using Reinforcement Learning [Info:eu-repo/semantics/bachelorThesis]. University of Twente. https://essay.utwente.nl/96026/
Pathariya, M. J., Basavraj Jalkote, P., Patil, A. M., Ashok Sutar, A., & Ghule, R. L. (2024). Tunes by Technology: A Comprehensive Survey of Music Generation Models. 2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC - ROBINS), 506–512. https://doi.org/10.1109/ICC-ROBINS60238.2024.10534029
Pudjiati, D., Mappapoleonro, A. M., Malik, H. A., & Fitria, T. N. (2024). Pre-Service Teachers’ Perspectives on the Implementation of Song-Based Instruction in Teaching English to Early Childhood Learners. Education and Human Development Journal, 9(3), 272–285. https://doi.org/10.33086/ehdj.v9i3.6249
Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., & Zurada, J. M. (2019, May 27). Artificial Intelligence and Soft Computing: 18th International Conference, ICAISC 2019, Zakopane, Poland.
Sabry, F. (2023). Artificial Intelligence Music: Fundamentals and Applications. One Billion Knowledgeable.
Siphocly, N. N. J., El-Horbaty, E.-S. M., & Salem, A.-B. M. (2021). Top 10 Artificial Intelligence Algorithms in Computer Music Composition. International Journal of Computing and Digital Systems, 10(01), 373–394. https://doi.org/10.12785/ijcds/100138
Soegiono, A. S., Utami, Y. N. T., Prasetya, L. A., Angeline, E. O., Sudiredjo, E. F., Almers, V., Saraswati, D., Amaya, Y. L. S., Purnomo, A. A., & Santoso, T. V. (2023). AI Music Generator: Mengenal Lebih Dalam. SIEGA Publisher.
Tan, X., & Li, X. (2021). A Tutorial on AI Music Composition. Proceedings of the 29th ACM International Conference on Multimedia, 5678–5680. https://doi.org/10.1145/3474085.3478875
Tang, H., Gu, Y., & Yang, X. (2022). Music Generation with AI technology: Is It Possible? 2022 IEEE 5th International Conference on Electronics Technology (ICET), 1265–1272. https://doi.org/10.1109/ICET55676.2022.9824149
Tokui, N. (2020). Towards democratizing music production with AI-Design of Variational Autoencoder-based Rhythm Generator as a DAW plugin (No. arXiv:2004.01525). arXiv. https://doi.org/10.48550/arXiv.2004.01525
Will, J. (2024). Rage against the Machine: Copyright Infringement in AI-Generated Music. Journal of Intellectual Property Law, 31(2), 378.
Williams, D., Hodge, V. J., Gega, L., Murphy, D., Cowling, P. I., & Drachen, A. (2019, March 17). AI and Automatic Music Generation for Mindfulness. 2019 AES International Conference on Immersive and Interactive Audio: Creating the Next Dimension of Sound Experience. 2019 AES International Conference on Immersive and Interactive Audio: Creating the Next Dimension of Sound Experience, GBR. https://eprints.whiterose.ac.uk/141387/
Yu, J., Wu, S., Lu, G., Li, Z., Zhou, L., & Zhang, K. (2024). Suno: Potential, prospects, and trends. Frontiers of Information Technology & Electronic Engineering. https://doi.org/10.1631/FITEE.2400299
DOI: https://doi.org/10.29040/ijcis.v6i3.230
Article Metrics
Abstract view : 6 timesPDF - 2 times
Refbacks
- There are currently no refbacks.

This work is licensed under a Creative Commons Attribution 4.0 International License
















