Adoption of AI Music Generator by Early Childhood Educators in Digital Communities: A Netnographic Study of Practices, Perceptions, and Dynamics
The rapid advancement of generative artificial intelligence (AI), particularly in AI music generation platforms, presents new opportunities for early childhood educators to produce varied and contextually relevant musical learning materials. Empirical evidence regarding how early childhood education (PAUD) educators in Indonesia adopt, construct perceptions of, and negotiate AI music generators within digital communities remains limited. This study examined three research questions: what forms of practice PAUD educators demonstrate in utilizing AI music generators; how educators construct perceptions, opportunities, and concerns through digital community interactions; and what patterns of role differentiation characterize community dynamics around AI music generator adoption. A netnographic approach was employed, with data collected from Facebook Groups, TikTok, and YouTube communities of PAUD educators over 12 months. Data sources comprised 286 digital archival interactions, online interviews with ten purposively selected informants, and researcher field notes, analyzed through inductive thematic analysis with inter-rater reliability verification (Cohen's kappa = .81). Three dominant practice patterns were identified: thematic song creation via text-to-music prompting (54.2%), adaptation and reimagination of existing children's songs (23.8%), and active distribution of AI-generated musical content (22.0%). Educators' perceptions were polarized, combining positive constructions of efficiency, accessibility, and pedagogical creativity with concerns about overreliance on technology, Western-centric algorithmic bias, and unresolved copyright status. Community dynamics revealed three organically differentiated roles, namely active creators, adopters, and critical curators, with platform algorithms creating a discursive hierarchy that privileges practical-demonstrative content over reflective-critical engagement. These findings confirm that PAUD educators' digital communities function as horizontal professional learning ecosystems in which the adoption of AI music generators is negotiated through intersecting values of pedagogy, cultural identity, and technological pragmatism.
Keywords: AI music generator, early childhood music education, netnography, digital educator community, music stimulation.
Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim code. Polity Press.
Berson, I. R., Berson, M. J., & Luo, W. (2025). Innovating responsibly: Ethical considerations for AI in early childhood education. AI, Brain and Child, 1(1), Article 2. https://doi.org/10.1007/s44436-025-00003-5
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806
Carnovalini, F., & Roda, A. (2020). Computational creativity and music generation systems: An introduction to the state of the art. Frontiers in Artificial Intelligence, 3, Article 14. https://doi.org/10.3389/frai.2020.00014
Chen, L. (2025). Unlocking the beat: How AI tools drive music students' motivation, engagement, creativity and learning success. European Journal of Education, 60(1), e12823. https://doi.org/10.1111/ejed.12823
Cheng, L. (2025). The impact of generative AI on school music education: Challenges and recommendations. Arts Education Policy Review. Advance online publication. https://doi.org/10.1080/10632913.2025.2451373
Choi, W. C., & Chang, C. I. (2025). Suno AI: Opportunities and challenges of AI-generated music and lyrics in secondary music education. In Proceedings of the 2025 8th Artificial Intelligence and Cloud Computing Conference (pp. 1–8). ACM. https://doi.org/10.1145/3789982.3790019
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Discetti, R., & Anderson, V. (2023). The value of netnography for research in HRD. Human Resource Development Review, 22(1), 59–83. https://doi.org/10.1177/15344843221137506
Faisol, A., Rohman, H., & Zukhrufi, M. (2024). The utilization of TikTok as a learning support medium in PAUD settings. Jurnal Pendidikan Anak Usia Dini, 8(2), 115–128. https://doi.org/10.21009/jpaud.v8i2.24156
Fitria, T. N. (2024). Being songwriter and singer using Suno as AI music generator: Creating English songs for young learners in teaching vocabularies. International Journal of Computer and Information System, 6(3). https://doi.org/10.29040/ijcis.v6i3.230
He, S., & Ren, Y. (2025). Exploring pre-service music teachers' acceptance of generative artificial intelligence: A PLS-SEM-ANN approach. Frontiers in Psychology, 16, 1571279. https://doi.org/10.3389/fpsyg.2025.1571279
Ilari, B., Huisman Koops, L., Andang'o, E., Bautista, A., Dean, B., Madalozzo, V., Madalozzo, T., Palmer, K., & Yi, G. (2021). Music programs for young children during the COVID-19 pandemic: Stories from across the world. In A. Nyland & M. Barrett (Eds.), The impact of COVID-19 on early childhood education and care (pp. 475–492). Springer. https://doi.org/10.1007/978-3-030-96977-6_24
Kim, J. (2025). Technical support, resources, and community of practice for teachers adopting AI. Frontiers in Education, 10, Article 1488147. https://doi.org/10.3389/feduc.2025.1488147
Kozinets, R. V. (2020). Netnography: The essential guide to qualitative social media research (3rd ed.). SAGE Publications.
Kozinets, R. V., & Gretzel, U. (2023). Qualitative social media methods: Netnography in the age of technocultures. In N. Denzin & Y. Lincoln (Eds.), The SAGE handbook of qualitative research (6th ed., pp. 403–420). SAGE Publications.
Kurian, N. (2023). AI's empathy gap: The risks of conversational artificial intelligence for young children's well-being and key ethical considerations for early childhood education and care. Contemporary Issues in Early Childhood, 26(1), 132–139. https://doi.org/10.1177/14639491231206004
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310
Ljungcrantz, L. (2026). The interaction of AI and early childhood education: A state-of-the-art review 2020-2024. Early Childhood Education Journal. Advance online publication. https://doi.org/10.1007/s10643-025-02079-3
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
Pan, H.-L. W., & Chen, W.-Y. (2023). Networked learning communities in promoting teachers' receptivity to change: How professional learning beliefs and behaviors mediate. Sustainability, 15(3), 2396. https://doi.org/10.3390/su15032396
Roldán-Cardona, M., Chacón-Castro, M., Jadan-Guerrero, J., Salvador-Ullauri, L., & Acosta-Vargas, P. (2025). Music education with artificial intelligence for inclusive and sustainable early childhood learning. Emerging Science Journal, 9(Special Issue). https://doi.org/10.28991/ESJ-2025-SP109-07
Rowe, D. W., Nichols, T. P., & Dunston, P. J. (2025). The science of reading on social media: TikTok content creators' discourse patterns and bodies of knowledge. Reading Research Quarterly. Advance online publication. https://doi.org/10.1002/rrq.70057
Sturm, B. L. T., Iglesias, M., Ben-Tal, O., Miron, M., & Gómez, E. (2019). Artificial intelligence and music: Open questions of copyright law and engineering praxis. Arts, 8(3), 115. https://doi.org/10.3390/arts8030115
Turyamureeba, S. (2024). Integrating music education into the curriculum for cognitive development. Research Invention Journal of Research in Education, 4(3), 50–54. https://doi.org/10.59298/RIJRE/2024/435054
Ulfa, M., & Hina, S. (2025). The role of artificial intelligence (AI) in improving the quality of education in early childhood education programs. Indonesian Journal of Progressive Pedagogy, 1(1), 49-59. https://doi.org/10.61987/ijpp.v1i1.666
U.S. Copyright Office. (2025). Copyright and artificial intelligence, Part 2: Copyrightability. https://www.copyright.gov/ai/
Varadi, J. (2022). A review of the literature on the relationship of music education to the development of socio-emotional learning. SAGE Open, 12(1), 1–16. https://doi.org/10.1177/21582440211068501
Vizcaíno-Verdú, A., & Abidin, C. (2023). TeachTok: Teachers of TikTok, micro-celebrification, and fun learning communities. Teaching and Teacher Education, 123, 103978. https://doi.org/10.1016/j.tate.2022.103978
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. Cambridge University Press.
Zhou, W., & Kim, Y. (2024). Innovative music education: An empirical assessment of ChatGPT-4's impact on student learning experiences. Education and Information Technologies, 29(16), 20855–20881. https://doi.org/10.1007/s10639-024-12705-z
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
The copyright is reserved to Research in Education, Technology, and Multiculture that is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Address:
CV. IMRECS: Jalan Gagak I, Kecamatan Larangan, Kota Tangerang. Postal Code: 15155
STKIP Al Islam Tunas Bangsa: Jalan Z.A. Pagar Alam No. 41 Gedong Meneng Bandar Lampung