FORMATION OF MUSICAL IDENTITY DURING VOCAL TRAINING BASED ON MACHINE INTELLIGENCE TOOLS

Authors

  • Liubov Kaniuka Senior Teacher, Department of Singing and Choral Conducting, Faculty of Singing and Jazz Art, R. Glier Kyiv Municipal Academy of Music, Kyiv, Ukraine
  • Tamara Koval Docent, Department of Singing and Choral Conducting, Faculty of Singing and Jazz Art, R. Glier Kyiv Municipal Academy of Music, Kyiv, Ukraine
  • Olha Vasylenko Associate PhD in Art Studies, Associate Professor, Head of the Department of Music History, Faculty of Performing Arts and Musicology, R. Glier Kyiv Municipal Academy of Music, Kyiv, Ukraine
  • Svitlana Borovyk Honored Artist of Ukraine, Professor, Associate Professor, Head of the Department of Academic Singing and Choral Conducting, Faculty of Singing and Jazz Art, R. Glier Kyiv Municipal Academy of Music, Kyiv, Ukraine
  • Volodymyr Humeniuk Senior Lecturer, Department of Humanitarian and Musical and Innovative Disciplines, R. Glier Kyiv Municipal Academy of Music, Kyiv, Ukraine

DOI:

https://doi.org/10.29121/shodhkosh.v7.i1.2026.7031

Keywords:

Digital Age, Music Pedagogy, Subjectivity, Algorithmic Composition Techniques, Musical Style, Acting

Abstract [English]

Modern technologies contribute to the gradual development of vocal skills, which affects the rethinking of traditional learning. Digital algorithms are aimed at obtaining individual musical experience, which affects the formation of musical identity. The purpose of the research is to determine the advantages of artificial intelligence for the development of musical identity, which is associated with taking into account the challenges and prospects for vocal and theoretical schools. The research strategy involved the use of systems analysis methods, observation, the R. Likert scale, ANOVA analysis of variance, and the method of analysis of hierarchies. In the course of the research, it was found that the formation of musical identity using digital instruments is associated with a change in the structure of the educational process, assessment methods and planning of the educational approach, the development of vocal technique, planning of song performance methods and repertoire development. The analysis showed that digital instruments influence the formation of musical identity skills, which are related to creativity and aesthetics of performance, cognitive-analytical and performance capabilities. Adaptation of VocalPitchMonitor, SpectraLayers AI, and AI Artistic Evaluation into the educational process allowed for developing the technique, expressiveness of singing, and focus on creating musical improvisations. Based on the students’ results, it was found that the formed level of musical identity was significantly higher after training (4.9 and 4.8 points) than before the start of the study (3.0 and 2.7 points). Observations showed that the prospects of machine intelligence in education are associated with an individual approach to learning (0.36) and receiving systematic feedback (0.33). Among the challenges of such training were the formation of a unified sound (0.28) and the development of technocratization of education (0.26). The practical direction of the research is associated with the selection of effective tools for the formation of musical identity in the process of vocal training of second-year students.

References

Aliksiichuk, O., Borysova, T., Kartashova, Z., Priadko, O., Kuziv, M., and Chaban-Chaika, S. (2025). Modern Digital Approaches to Training Music Teachers: Evolution From Classical to Interactive. International Journal on Culture, History, and Religion, 7(SI1), 273–296. https://doi.org/10.63931/ijchr.v7iSI1.201 DOI: https://doi.org/10.63931/ijchr.v7iSI1.201

Al-Khleifat, K. (2025). The Effects of Vocal Training on the Cognitive and Emotional Development of Young Children. Scientific Bulletin of Mukachevo State University. Series Pedagogy and Psychology, 11(1), 113–124. https://doi.org/10.52534/msu-pp1.2025.113 DOI: https://doi.org/10.52534/msu-pp1.2025.113

Alsaleh, A. (2024). The Impact of Technological Advancement on Culture and Society. Scientific Reports, 14(1), 32140. https://doi.org/10.1038/s41598-024-83995-z DOI: https://doi.org/10.1038/s41598-024-83995-z

AmperScore AI. (2025). AmperScore AI. https://aithenas.com/tools/amper-score

Bai, Y. (2025). How Does the Use of Modern Technologies Affect Students' Perception of Information? The Effectiveness of Holographic Projection Technology in Music Education. Technology, Pedagogy and Education, 34(1), 35–47. https://doi.org/10.1080/1475939X.2024.2402274 DOI: https://doi.org/10.1080/1475939X.2024.2402274

Boltsi, A., Kalovrektis, K., Xenakis, A., Chatzimisios, P., and Chaikalis, C. (2024). Digital Tools, Technologies, and Learning Methodologies for Education 4.0 Frameworks: A STEM-Oriented Survey. IEEE Access, 12, 12883–12901. https://doi.org/10.1109/ACCESS.2024.3355282 DOI: https://doi.org/10.1109/ACCESS.2024.3355282

Borkowski, A. (2023). Vocal Aesthetics, AI Imaginaries: Reconfiguring Smart Interfaces. Afterimage, 50(2), 129–149. https://doi.org/10.1525/aft.2023.50.2.129 DOI: https://doi.org/10.1525/aft.2023.50.2.129

Burnard, P., and Mackinlay, E. (2025). Performing Ethical Response-Ability in Music Education Research: Who Cares and What Matters? Action, Criticism, and Theory for Music Education, 24(4), 110–127. https://doi.org/10.22176/act24.4.110

Canyakan, S. (2024). The Role of AI in Creative Processes: Ethical and Legal Perspectives in the Music Industry. Journal of Music Theory and Transcultural Music Studies, 2(2), 143–158. https://doi.org/10.5281/zenodo.15031855

Casebourne, I., Shi, S., Hogan, M., Holmes, W., Hoel, T., Wegerif, R., and Yuan, L. (2025). Using AI to Support Education for Collective Intelligence. International Journal of Artificial Intelligence in Education, 35(3), 1597–1629. https://doi.org/10.1007/s40593-024-00437-7 DOI: https://doi.org/10.1007/s40593-024-00437-7

Chang, S., Li, D., and Qi, Y. (2021). Pearson's Goodness-of-Fit Tests for Sparse Distributions. Journal of Applied Statistics, 50(5), 1078–1093. https://doi.org/10.1080/02664763.2021.2017413 DOI: https://doi.org/10.1080/02664763.2021.2017413

Chang, Z. (2022). The Use of Online Vocal Training Programs as a Means to Develop Creative Thinking and Vocal Prowess. Interactive Learning Environments, 31(10), 7214–7225. https://doi.org/10.1080/10494820.2022.2064514 DOI: https://doi.org/10.1080/10494820.2022.2064514

Cipta, F., Sukmayadi, Y., Milyartini, R., and Hardini, T. I. (2024). Optimizing AI-Powered Music Creation Social Media to Amplify Learning Content. Jurnal Kependidikan, 10(3), 881–892. https://doi.org/10.33394/jk.v10i3.12332 DOI: https://doi.org/10.33394/jk.v10i3.12332

Concina, E. (2023). Effective Music Teachers and Effective Music Teaching Today: A Systematic Review. Education Sciences, 13(2), 107. https://doi.org/10.3390/educsci13020107 DOI: https://doi.org/10.3390/educsci13020107

Fan, C., and Shi, L. (2025). Technological and Managerial Aspects of the Digital Transformation in Music Education: A Big Data Perspective. Journal of Computational Methods in Sciences and Engineering, 25(1), 1075–1086. https://doi.org/10.1177/14727978251322025 DOI: https://doi.org/10.1177/14727978251322025

Forbes, M., Goopy, J., and Krause, A. E. (2024). Becoming Singular: Musical Identity Construction and Maintenance Through the Lens of Identity Process Theory. Psychology of Music, 53(5), 746–761. https://doi.org/10.1177/03057356241267863 DOI: https://doi.org/10.1177/03057356241267863

Fox, M., Vaidyanathan, G., and Breese, J. L. (2024). The Impact of Artificial Intelligence on Musicians. Issues in Information Systems, 25(3). https://doi.org/10.48009/3_iis_2024_121 DOI: https://doi.org/10.48009/3_iis_2024_121

Frytsiuk, V., Kshyvak, I., Baranovska, I., Teplova, O., Novosadova, A., and Khilya, A. (2025). Digital Ecosystems for Music Teacher Training: ICT, Social Media, and Online Learning Environments. Environment. Technology. Resources. Proceedings, 2, 137–144. https://doi.org/10.17770/etr2025vol2.8598 DOI: https://doi.org/10.17770/etr2025vol2.8598

Genelza, G. G. (2024). A Systematic Literature Review on AI Voice Cloning Generator: A Game-Changer or a Threat? Journal of Emerging Technologies, 4(2), 54–61. https://doi.org/10.57040/ag587791

Gobinath, A., Manjula Devi, C., Suthan Raja, S. J., Prakash, P., Anandan, M., and Srinivasan, A. (2024). Voice Assistant With AI Chat Integration Using OpenAI. In Proceedings of INCOS 2024 (1–6). IEEE. https://doi.org/10.1109/INCOS59338.2024.10527726 DOI: https://doi.org/10.1109/INCOS59338.2024.10527726

Irianti, L., Faridi, A., Pratama, H., and Suwandi. (2024). Flipped Classroom and Critical Thinking on Public Speaking Class. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2024.2315815 DOI: https://doi.org/10.1080/2331186X.2024.2315815

Jiayu, O. (2025). Performing Abilities of a Student Vocalist and Their Classification. Paradigm of Knowledge, 1(65). https://doi.org/10.26886/2520-7474.1%2865%292025.4 DOI: https://doi.org/10.26886/2520-7474.1(65)2025.4

Johansen, G. (2024). School Music Education and the Society of Tomorrow: The Necessity of Navigating in Chaos. In The Sage Handbook of School Music Education. SAGE. https://doi.org/10.4135/9781529674842.n2 DOI: https://doi.org/10.4135/9781529674842.n2

Jude, G. (2025). Future Voices to Come: AI Singing After Miku. In How Vocaloid Works (pp. 63–86). Palgrave Macmillan. https://doi.org/10.1007/978-3-031-92727-0_4 DOI: https://doi.org/10.1007/978-3-031-92727-0_4

Kojima, T., Fujimura, S., Hasebe, K., et al. (2024). Objective Assessment of Pathological Voice Using Artificial Intelligence Based on the GRBAS Scale. Journal of Voice, 38(3), 561–566. https://doi.org/10.1016/j.jvoice.2021.11.021 DOI: https://doi.org/10.1016/j.jvoice.2021.11.021

Konovalova, I., Breslavets, H., Riabukha, N., et al. (2025). The Evolution of World Music Pedagogy in the Information Society. BRAIN, 16(S1), 99–116. https://doi.org/10.70594/brain/16.S1/9 DOI: https://doi.org/10.70594/brain/16.S1/9

Li, P., and Wang, B. (2023). Artificial Intelligence in Music Education. International Journal of Human-Computer Interaction, 40(16), 4183–4192. https://doi.org/10.1080/10447318.2023.2209984 DOI: https://doi.org/10.1080/10447318.2023.2209984

Liang, Y. (2024). Collaborative Music Making in the Digital Age: Fostering Creativity in Vocal Ensembles. Interactive Learning Environments, 33(1), 615–630. https://doi.org/10.1080/10494820.2024.2353195 DOI: https://doi.org/10.1080/10494820.2024.2353195

Liu, L. (2024). Current Situation and Innovative Methods of Brass Music Teaching Based on Network Information Technology. Journal of Electrical Systems, 20(1). https://doi.org/10.52783/jes.683 DOI: https://doi.org/10.52783/jes.683

Lulu, W., Sensai, P., and Homhuan, W. (2024). Education and Literacy Transmission of Chinese Pansori in Chaoxian Ethnic Group. International Journal of Education and Literacy Studies, 12(4), 185–192. https://doi.org/10.7575/aiac.ijels.v.12n.4p.185 DOI: https://doi.org/10.7575/aiac.ijels.v.12n.4p.185

Lv, H. Z. (2023). Innovative Music Education: Using an AI-Based Flipped Classroom. Education and Information Technologies, 28, 15301–15316. https://doi.org/10.1007/s10639-023-11835-0 DOI: https://doi.org/10.1007/s10639-023-11835-0

Maychyk, O., Slyusar, T., Voitovych, O., Katrych, O., and An, L. (2024). Development of the Technique of Forming the Professionalism of a Pop Artist: Vocal Discourse. Convergences: Journal of Research and Arts Education, 17(33), 89–104. https://doi.org/10.53681/c1514225187514391s.33.239 DOI: https://doi.org/10.53681/c1514225187514391s.33.239

Merchán Sánchez-Jara, J. F., González Gutiérrez, S., Cruz Rodríguez, J., and Syroyid Syroyid, B. (2024). Artificial Intelligence-Assisted Music Education: A Critical Synthesis of Challenges and Opportunities. Education Sciences, 14(11), 1171. https://doi.org/10.3390/educsci14111171 DOI: https://doi.org/10.3390/educsci14111171

Metin, E., Uygur, K., Okur, E., Metin, B., and Gündüz, B. (2024). Temperament and Voice Quality in Patients With Vocal Fold Nodules. Journal of Voice. https://doi.org/10.1016/j.jvoice.2024.08.005 DOI: https://doi.org/10.1016/j.jvoice.2024.08.005

Mínguez-Alcaide, X., and Bobowik, M. (2025). Social Identity, Collective Self-Esteem, and Musical Preferences in Electronic Dance Music Culture: The Role of Emotions. Psychology of Music. https://doi.org/10.1177/03057356251361754 DOI: https://doi.org/10.1177/03057356251361754

Mygdanis, Y. (2025). Design-Based Research in Music Education: Theoretical Foundations, Methodological Perspectives, and Practice Implications. Futurity Education, 5(2), 90–114. https://doi.org/10.57125/FED.2025.06.25.05 DOI: https://doi.org/10.57125/FED.2025.06.25.05

Noufi, C., May, L., and Berger, J. (2025). A Model of Vocal Persona: Context, Perception, Production. Frontiers in Computer Science, 7, 1575296. https://doi.org/10.3389/fcomp.2025.1575296 DOI: https://doi.org/10.3389/fcomp.2025.1575296

O'Leary, E. J., and Bannerman, J. K. (2025). Online Curriculum Marketplaces and Music Education: A Critical Analysis of Music Activities on TeachersPayTeachers.com. International Journal of Music Education, 43(1), 39–53. https://doi.org/10.1177/02557614241307242 DOI: https://doi.org/10.1177/02557614241307242

Pandey, S., and Sinha, K. (2023). Developments in Analysis of Variance (ANOVA) and Experimental Design: A Comprehensive Overview. Journal of Advanced Research in Applied Mathematics and Statistics, 8(3–4), 8–13. https://journals.indexcopernicus.com/api/file/viewByFileId/2037033

Perakaki, E. (2025). Exploring Flipped Learning Practices in Piano and Music Theory: A Case Study of Two Music Teachers in Greece. Research Studies in Music Education. https://doi.org/10.1177/1321103X251342708 DOI: https://doi.org/10.1177/1321103X251342708

Qian, C., and Jiang, M. (2024). Exploring the Effects of Digital Game-Based Learning on Music Education. Studies in Social Science and Humanities, 3(5), 6–9. https://doi.org/10.56397/SSSH.2024.05.02 DOI: https://doi.org/10.56397/SSSH.2024.05.02

Rui, Y. (2024). Simulation of E-Learning in Vocal Network Teaching Experience System Based on Intelligent Internet of Things Technology. Entertainment Computing, 50, 100711. https://doi.org/10.1016/j.entcom.2024.100711 DOI: https://doi.org/10.1016/j.entcom.2024.100711

Sai, Y. (2024). Online Music Learning Based on Digital Multimedia for Virtual Reality. Interactive Learning Environments, 32(5), 1751–1762. https://doi.org/10.1080/10494820.2022.2127779 DOI: https://doi.org/10.1080/10494820.2022.2127779

Sakalar, A., and Gürel, S. (2024). Academic Perspectives on the Use of Digital Platforms and Mobile Applications in Vocal Training. Online Journal of Music Sciences, 9(2), 389–404. https://doi.org/10.31811/ojomus.1564925 DOI: https://doi.org/10.31811/ojomus.1564925

Shiyao, W., and Noordin, Z. M. (2024). The Influence and Impact of the Orff-Music Method on Teaching and Learning in Music Education Course in Higher Education in China. International Journal of Academic Research in Business and Social Sciences, 14(6), 1805–1817. https://doi.org/10.6007/IJARBSS/v14-i6/21971 DOI: https://doi.org/10.6007/IJARBSS/v14-i6/21971

Shpyrka, A., Bondarenko, L., Kondratenko, G., and Shpyrka, A. (2021). Emotional Expressiveness of the Vocalist: A Cross-Sectional Study. Rast Musicology Journal, 9(2), 2893–2916. https://doi.org/10.12975/rastmd.20219211 DOI: https://doi.org/10.12975/rastmd.20219211

Singer, J. B. (2025). Podcasting in Social Work Education for Clinical Skill Development. In M. Fox and J. B. Singer (Eds.), Podcasting in Social Work Education (51–72). Routledge. https://doi.org/10.4324/9781003530275-6 DOI: https://doi.org/10.4324/9781003530275-6

SpectraLayers AI. (2025). SpectraLayers AI. https://www.steinberg.net/spectralayers/

Stevens, C. (2025). Teachers and Teaching: Pedagogy, Digital Skills and Professional Development. Open Learning: The Journal of Open, Distance and e-Learning, 40(1), 1–3. https://doi.org/10.1080/02680513.2024.2436665 DOI: https://doi.org/10.1080/02680513.2024.2436665

Tang, K. H. D. (2024). Implications of Artificial Intelligence for Teaching and Learning. Acta Pedagogia Asiana, 3(2), 65–79. https://doi.org/10.53623/apga.v3i2.404 DOI: https://doi.org/10.53623/apga.v3i2.404

Tian, J. (2025). Digital Transformation Perspective on Art-Science Integration' Empowering Innovative Talent Cultivation in Vocational Music Education. Journal of Sociology and Education, 1(7). https://doi.org/10.63887/jse.2025.1.7.35 DOI: https://doi.org/10.63887/jse.2025.1.7.35

VocalPitchMonitor. (2025). VocalPitchMonitor. https://play.google.com/store/apps/details?id=com.tadaoyamaoka.vocalpitchmonitor

Wang, Y. (2022). Vocal Education in Higher Educational Institutions in China: Student Motivation and Creativity. Interactive Learning Environments, 32(3), 813–823. https://doi.org/10.1080/10494820.2022.2098778 DOI: https://doi.org/10.1080/10494820.2022.2098778

Wang, Y. (2024). Vocal Creativity: Analyzing Students Song Making Processes in Blended Learning. Interactive Learning Environments, 32(5), 2196–2206. https://doi.org/10.1080/10494820.2022.2141267 DOI: https://doi.org/10.1080/10494820.2022.2141267

Yang, G., and Xiangming, L. (2025). Graduate Socialization and Anxiety: Insights via Hierarchical Regression Analysis and Beyond. Studies in Higher Education, 50(7), 1365–1381. https://doi.org/10.1080/03075079.2024.2375563 DOI: https://doi.org/10.1080/03075079.2024.2375563

Yang, X. (2022). The Perspectives of Teaching Electroacoustic Music in the Digital Environment in Higher Music Education. Interactive Learning Environments, 32(4), 1183–1193. https://doi.org/10.1080/10494820.2022.2115080 DOI: https://doi.org/10.1080/10494820.2022.2115080

Zhang, L. (2025a). Compositional Tools Based on Artificial Intelligence for Choral Artistic Education: Enhancing Creative Skills in Choral Arrangements. Thinking Skills and Creativity, 56, 101768. https://doi.org/10.1016/j.tsc.2025.101768 DOI: https://doi.org/10.1016/j.tsc.2025.101768

Zhang, X., Wang, Y., Han, Y., Liang, C., Chatterjee, I., Tang, J., and Shi, Y. (2024). The Earsavas Dataset: Enabling Subject-Aware Vocal Activity Sensing on Earables. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 8(2), 1–26. https://doi.org/10.1145/3659616 DOI: https://doi.org/10.1145/3659616

Zhang, Y. (2025b). Increasing Emotional Perception in Academic Singing During Vocal Performance: The Use of AI Solutions. International Journal of Human-Computer Interaction, 41(19), 12086–12094. https://doi.org/10.1080/10447318.2025.2452213 DOI: https://doi.org/10.1080/10447318.2025.2452213

Zong, L. (2025). Evaluation on the Effect of Enhancing Vocal Music Training Experience With Virtual Reality Technology. International Journal of Web-Based Learning and Teaching Technologies, 20(1), 1–20. https://doi.org/10.4018/IJWLTT.382590 DOI: https://doi.org/10.4018/IJWLTT.382590

Downloads

Published

2026-03-25

How to Cite

Kaniuka, L., Koval, T. ., Vasylenko, O. ., Borovyk, S. ., & Humeniuk, V. . (2026). FORMATION OF MUSICAL IDENTITY DURING VOCAL TRAINING BASED ON MACHINE INTELLIGENCE TOOLS. ShodhKosh: Journal of Visual and Performing Arts, 7(1), 187–207. https://doi.org/10.29121/shodhkosh.v7.i1.2026.7031