A Qualitative Study on Turkish Composers’ Views on Artificial Intelligence and Composition

Abstract

Technological advancements have profoundly impacted artistic and musical creation, redefining the boundaries between human creativity and artificial intelligence (AI). The growing integration of AI into compositional practices has sparked debates on creativity, originality, and aesthetic values. This study analyzes the perspectives of five composers-each representing a distinct artistic approach within the contemporary Turkish music scene-regarding AI’s influence on composition. A qualitative methodology was employed, using semi-structured in-depth interviews, and the data were interpreted through content analysis. The findings indicate that while composers acknowledge the functional potential of AI, particularly in commercial music production, they also stress that artistic creativity is shaped by human emotions, originality, and aesthetic sensitivity. Although some view AI as a technical support tool, the prevailing consensus is that AI-generated music lacks artistic depth and emotional expressiveness. Additionally, concerns such as ethical implications, plagiarism risks, and technological dependency emerge as significant points of contention. This study offers valuable insight into the evolving relationship between AI and creative processes, revealing both the limitations and potential applications of AI in composition.

Keywords

• Composition
• creativity
• artificial intelligence

References

1. Arielli, E., & Manovich, L. (2023). AI-aesthetics and the anthropocentric myth of creativity [Yapay zekâ estetiği ve insan merkezli yaratıcılık miti] (K. O. Bayraktar, Trans.). MUJAD, 14(1), 214-226. https://doi.org/10.29228/sanat.23google scholar
2. Atalay, E. (2025, May 25). Interview with Sultan Bilget Güçlüer. google scholar
3. Balaban, M. (2002). Structure and interpretation of music concepts: Music from a computational perspective. In C. Anagnostopoulou, M. Ferrand, & A. Smaill (Eds.), Music and artificial intelligence: Second International Conference, ICMAI 2002, Edinburgh, Scotland, September 12-14, 2002, proceedings (pp. 1-3). Springer. google scholar
4. Bolşen, A. N. (2025, May 27). Interview with Sultan Bilget Güçlüer. google scholar
5. Bono Rosselló, L., & Bersini, H. (2023). Music generation with multiple ant colonies interacting on multilayer graphs. In C. Johnson, N. Rodríguez-Fernández, & S. M. Rebelo (Eds.), Artificial intelligence in music, sound, art and design: 12th International Conference, EvoMUSART 2023, held as part of EvoStar, Brno, Czech Republic, proceedings (pp. 34-49). Springer.google scholar
6. Bown, O. (2021). Sociocultural and design perspectives on AI-based music production: Why do we make music and what changes if AI makes it for us? In E. R. Miranda (Ed.), Handbook of artificial intelligence for music: Foundations, advanced approaches, and developments for creativity (pp. 1-20). Springer. google scholar
7. Caramiaux, B. (2020). Research for CULT Committee - The use of artificial intelligence in the cultural and creative sectors: Concomitant expertise for INI report (PE 629.220). Policy Department for Structural and Cohesion Policies, Directorate-General for Internal Policies, European Parliament. https://www.europarl.europa.eu/RegData/etudes/BRIE/2020/629220/IPOL_BRI(2020)629220_EN.pdfgoogle scholar
8. Colton, S., & Banar, B. (2023). Automatically adding to artistic cultures. In C. Johnson, N. Rodríguez-Fernández, & S. M. Rebelo (Eds.), Artificial intelligence in music, sound, art and design: 12th International Conference, EvoMUSART 2023, held as part of EvoStar, Brno, Czech Republic, proceedings (pp. 50-66). Springer. google scholar

9. Dannenberg, R. B. (2006). Computer models of musical creativity [Review of the book Computer models of musical creativity, by D. Cope]. Artificial Intelligence, 170(18), 1218-1221. google scholar
10. Erdener, A. T. (2025, May 26). Interview with Sultan Bilget Güçlüer. google scholar
11. Eryılmaz, E. (2025, June 1). Interview with Sultan Bilget Güçlüer. google scholar
12. Grabe, I., Zhu, J., & Agirrezabal, M. (2022). Fashion style generation: Evolutionary search with Gaussian mixture models in the latent space. In T. Martins, N. Rodríguez-Fernández, & S. M. Rebelo (Eds.), Artificial intelligence in music, sound, art and design: 11th International Conference, EvoMUSART 2022, held as part of EvoStar, Spain, proceedings (pp. 84-100). Springer. google scholar
13. Guljajeva, V., & Canet Sola, M. (2023). AI-aided ceramic sculptures: Bridging deep learning with materiality. In C. Johnson, N. Rodríguez- Fernández, & S. M. Rebelo (Eds.), Artificial intelligence in music, sound, art and design: 12th International Conference, EvoMUSART 2023, held as part of EvoStar, Brno, Czech Republic, proceedings (pp. 357-371). Springer. google scholar
14. Hadjeres, G., Pachet, F., & Nielsen, F. (2016, December 3). DeepBach: A steerable model for Bach chorales generation [Preprint]. arXiv. https://arxiv.org/abs/1612.01010 google scholar
15. Martins, T., Rodríguez-Fernández, N., & Rebelo, S. M. (2022). Preface. In T. Martins, N. Rodríguez-Fernández, & S. M. Rebelo (Eds.), Artificial intelligence in music, sound, art and design: 11th International Conference, EvoMUSART 2022, held as part of EvoStar, Spain, proceedings (pp. v-vi). Springer. google scholar
16. Miranda, E. R. (2021). Preface. In E. R. Miranda (Ed.), Handbook of artificial intelligence for music: Foundations, advanced approaches, and developments for creativity (pp. Xix-xxi). Springer. google scholar
17. Miranda, E. R. (2022). Mimetic development of intonation. In T. Martins, N. Rodríguez-Fernández, & S. M. Rebelo (Eds.), Artificial intelligence in music, sound, art and design: 11th International Conference, EvoMUSART 2022, held as part of EvoStar, Spain, proceedings (pp. 107-118). Springer. google scholar
18. Musical Metacreation. (n.d.). About. Retrieved in 2025 from https://musicalmetacreation.org/about/ google scholar
19. Özkoç, Ö. (2025, June 7). Interview with Sultan Bilget Güçlüer. google scholar
20. Parlak, İ. H. (2021). Turkish makam music composition by using deep learning techniques (Order No. 708985) [Doctoral dissertation, Dokuz Eylül University]. Council of Higher Education Thesis Center. https://tez.yok.gov.tr/ google scholar
21. Privato, N., Rampado, O., & Novello, A. (2022). A creative tool for the musician combining LSTM and Markov chains in Max/MSP. In T. Martins, N. Rodríguez-Fernández, & S. M. Rebelo (Eds.), Artificial intelligence in music, sound, art and design: 11th International Conference, EvoMUSART 2022, held as part of EvoStar, Spain, proceedings (pp. 228-242). Springer.google scholar
22. Raynor, W. J. (1999). The international dictionary of artificial intelligence. Glenlake Publishing Company. google scholar
23. Reje, A. (2022). Ethical risk analysis of the use of AI in music production (Master’s thesis, KTH Royal Institute of Technology). google scholar
24. Steels, L. (2021). Foreword: From audio signals to musical meaning. In E. R. Miranda (Ed.), Handbook of artificial intelligence for music: Foundations, advanced approaches, and developments for creativity (pp. v-xviii). Springer. google scholar
25. Yavuz, M. S., Karaosmanoğlu, M. K., Yeprem, S., Karşıcı, G., & Sezikli, U. (2025). Türkiye’de müzik ve yapay zeka üzerine bir değerlendirme. Öneri Dergisi, 20 (MX Yaratıcı Endüstriler Çalıştayı 2024: Yapay Zekâ Çağında Yaratıcı Endüstriler Özel Sayısı), e50-70. DOI: 10.14783/maruoneri.1499345google scholar
26. Zlatkov, D., Ens, J., & Pasquier, P. (2023). Searching for human bias against AI-composed music. In C. Johnson, N. Rodríguez-Fernández, & S. M. Rebelo (Eds.), Artificial intelligence in music, sound, art and design: 12th International Conference, EvoMUSART 2023, held as part of EvoStar, Brno, Czech Republic, proceedings (pp. 308-23). Springer. google scholar
27. Zorluel, M. (2019). Yapay zekâ ve telif hakkı. TBB Journal, 2019(142), 305-353. https://tbbdergisi.barobirlik.org.tr/m2019-142-1851 google scholar