Yapay Zekâ Destekli Müzik Eğitiminde Akış Deneyiminin Kavramsal Analizi

Öz

Bu çalışmanın amacı, yapay zekâ destekli müzik eğitimine ilişkin güncel yazını, öğrencinin derinlemesine motivasyonu ve odaklanma süreçleriyle ilişkili akış deneyimi odağında kavramsal olarak incelemektir. Akış deneyimi, eylemin kendisinden alınan hazla sürdürülen yoğun odaklanma hâli olarak ele alınmış; kuramsal çerçeve Öz-Belirleme Kuramı’nın özerklik, yeterlik ve ilişkili olma gereksinimleriyle desteklenmiştir. Araştırma, mevcut literatürü sistematik ve analitik biçimde değerlendiren nitel bir yaklaşımla, doküman incelemesi yöntemi ve kavramsal analiz teknikleri kullanılarak yürütülmüştür. Analiz sonucunda, yapay zekâ uygulamalarının bireyselleştirilmiş öğrenme yolları sunarak öğrenci özerkliğini ve gelişim odaklı geri bildirimlerle yeterlik algısını güçlendirdiği; aynı zamanda generatif yapay zekâ araçlarının ortak üretim rolüyle yaratıcılık olanaklarını artırdığı tespit edilmiştir. Ancak aşırı algoritmik yönlendirme, öğrencinin hazır çözümlere bağımlı hâle gelmesi ve estetik karar verme süreçlerinin zayıflaması yoluyla akışın içsel niteliğini kesintiye uğratma riski taşımaktadır. Bu bulguların sentezi, yapay zekânın akış üzerindeki etkisinin teknolojik kapasiteden çok uygulanan pedagojik tasarımın niteliğine bağlı olduğunu göstermiştir. Çalışma, ototelik değeri korumayı hedefleyen özgün pedagojik tasarım ilkelerini içeren kavramsal bir çerçeve sunarak literatüre katkıda bulunmaktadır.

Anahtar Kelimeler

• Yapay Zekâ
• Müzik Eğitimi
• Akış Deneyimi
• Ototelik
• Kavramsal Analiz

Etik Beyan

Araştırma yalnızca kamuya açık akademik dokümanlara dayanmaktadır. İnsan katılımcı içermediğinden etik kurul onayı gerektirmemektedir.

Conceptual Analysis of Flow Experience in AI-Supported Music Education

Öz

The aim of this study is to conceptually examine the current literature on artificial intelligence (AI)–supported music education through the lens of flow experience, which is closely associated with learners’ deep motivation and sustained focus. Flow is approached as a state of intense concentration driven by intrinsic enjoyment, and the theoretical framework is grounded in the autonomy and competence components of Self-Determination Theory. The study employs a qualitative approach using document analysis and conceptual analytical techniques to systematically and analytically evaluate existing research. The analysis indicates that AI applications strengthen learner autonomy by offering individualized learning pathways and enhance competence perceptions through developmental, formative feedback mechanisms; generative AI tools further expand creative possibilities by acting as co-creators in music-making processes. However, excessive algorithmic steering poses a risk to the intrinsic nature of flow by fostering dependence on ready-made solutions and weakening learners’ aesthetic decision-making capacities. The synthesis of findings demonstrates that the impact of AI on flow depends less on its technological capabilities and more on the pedagogical design within which it is implemented. The study contributes to the field by presenting a conceptual framework that outlines pedagogical design principles aimed at preserving the autotelic value of music learning.

Anahtar Kelimeler

• Artificial Intelligence (AI)
• Music Education
• Flow Experience
• Autotelic
• Conceptual Analysis

Etik Beyan

The research is based solely on publicly available academic documents. As it does not involve human participants, ethical approval is not required.

Kaynakça

1. Acar, A. K. (2025). Yeni medya teknolojilerinde müzisyen kimliğinin dönüşümü: Yapay zeka, metaverse ve dijital dönüşüm kavramları üzerine bibliyometrik analiz. Online Journal of Music Sciences, 10(2), 219-234. https://doi.org/10.31811/ojomus.1624090
2. Addessi, A. R., Anelli, F., Benghi, D., & Friberg, A. (2017). Child-computer interaction at the beginner stage of music learning: Effects of reflexive interaction on children's musical improvisation. Frontiers in Psychology, 8, 65. https://doi.org/10.3389/fpsyg.2017.00065
3. Addessi, A. R., Ferrari, L., & Carugati, F. (2015). The flow grid: A technique for observing and measuring emotional state in children interacting with a flow machine. Journal of New Music Research, 44(2), 129–144. https://doi.org/10.1080/09298215.2014.991738
4. Aydın, N. (2024). Türkiye’de yapay zekâ alanında yapılan çalışmaların bibliyometrik analizi. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 27(52), 387–407. https://doi.org/10.31795/baunsobed.1545006
5. Ayduğ, D., & Altınpulluk, H. (2025). Are Turkish pre-service teachers worried about AI? A study on AI anxiety and digital literacy. AI & SOCIETY, 40, 5823–5834. https://doi.org/10.1007/s00146-025-02348-0
6. Bilget Güçlüer, S. (2025). A qualitative study on Turkish composers' views on artificial intelligence and composition. Konservatoryum-Conservatorium. Advance Online Publication. https://doi.org/10.26650/CONS2025-1736623
7. Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27-40. https://doi.org/10.3316/QRJ0902027
8. Bozkurt, A., Xiao, J., Farrow, R., Bai, J. Y. H., Nerantzi, C., Moore, S., Dron, J., Stracke, C. M., Singh, L., Crompton, H.,

9. Koutropoulos, A., Terentev, E., Pazurek, A., Nichols, M., Sidorkin, A. M., Costello, E., Watson, S., Mulligan, D., Honeychurch, S., ... Asino, T. I. (2024). The manifesto for teaching and learning in a time of generative AI: A critical collective stance to better navigate the future. Open Praxis, 16(4), 487–513. https://doi.org/10.55982/openpraxis.16.4.777
10. Chen, L. (2024). Unlocking the beat: How AI tools drive music students' motivation, engagement, creativity and learning success. European Journal of Education, e12823. https://doi.org/10.1111/ejed.12823
11. Cheng, L. (2025). The impact of generative AI on school music education: Challenges and recommendations. Arts Education Policy Review, 126(4), 255–262. https://doi.org/10.1080/10632913.2025.2451373
12. Chirico, A., Serino, S., Cipresso, P., Gaggioli, A., & Riva, G. (2015). When music “flows”. State and trait in musical performance, composition and listening: A systematic review. Frontiers in Psychology, 6, 906. https://doi.org/10.3389/fpsyg.2015.00906
13. Creswell, J. W. & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches. SAGE Publications.
14. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper Perennial.
15. Dargut Güler, T. (2024). Yapay zeka (AI) tabanlı uzaktan eğitimde etkileşim tasarımı: Öğrenci başarısını artırmak için yeni yaklaşımlar. Kuantum Teknolojileri ve Enformatik Araştırmaları, 2(2), 51–60. https://doi.org/10.70447/ktve.2464
16. Deci, E. L., & Ryan, R. M. (2000). The “What” and “Why” of Goal Pursuits: Human Needs and the Self-Determination of Behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
17. Diraksin, P., Luo, Q., Jirasanyansakul, P., Wenxin, D., Shang, W., Thetlek, R., & Chinnapakjarusiri, N. (2025). The role of artificial intelligence in enhancing music teaching and learning practices. Advance Knowledge for Executives, 4(1), 1–11. https://www.europub.co.uk/articles/-A-772737
18. dos Santos Silva, C., Blackwell, J., McPherson, G. E., & Boruchovitch, E. (2025). “Go home and practice”: How shaping feedback to students can foster independent musicianship. Frontiers in Psychology, 16, 1705295. https://doi.org/10.3389/fpsyg.2025.1705295
19. Dulkadir, D., & Belge, O. (2025). Müzik bölümünde okuyan öğrencilerin yapay zekaya yönelik tutumları. Yegah Musicology Journal, 8(3), 1786-1804. https://doi.org/10.51576/ymd.1747528
20. Eremenko, V., Morsi, A., Narang, J., & Serra, X. (2020). Performance assessment technologies for the support of musical instrument learning. In Proceedings of the 12th International Conference on Computer Supported Education - Volume 1: CSME, ISBN 978-989-758-417-6, pages 629-640. https://doi.org/10.5220/0009575006240632
21. Ertek Babaç, E. & Göklen Yılmaz, A. (2025). Ses eğitiminde piyano eşlikli ve eşliksiz şarkı söylemenin akış deneyimine etkisi. Batı Anadolu Eğitim Bilimleri Dergisi, 16(2), 2123–2143. https://doi.org/10.51460/baebd.1613194
22. Evans, P. (2015). Self-determination theory: An approach to motivation in music education. Musicae Scientiae, 19(1), 65–83. https://doi.org/10.1177/1029864914568044
23. Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal, 26(2), 91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x
24. Güdek, B., & Açıksöz, F. (2024). Yapay zeka destekli müzik eğitimi: Yeni bir öğrenme deneyimi. International Journal of Social and Humanities Sciences Research, 11(112), 1909-1922. https://doi.org/10.5281/zenodo.14020368
25. Güdek, B., & Arıcı, G. (2019). Mesleki müzik eğitimi alan öğrencilerin sürekli optimal performans duygu durumlarının farklı değişkenler açısından incelenmesi. The Journal of Academic Social Science Studies, 76, 339–363. https://doi.org/10.29228/JASSS.30221
26. Güngör, İ. C., & Öztürk, T. (2025). Öğretmenler yapay zekâyı benimsedi mi? K-12 eğitim sistemlerinden öğretmen deneyimlerinin bir sentezi. Recep Tayyip Erdoğan Üniversitesi Sosyal Bilimler Dergisi, 12(1), 70–100. https://doi.org/10.34086/rteusbe.1660052
27. Gürer, M., & Sonsel, Ö. B. (2025). Sanal gerçeklik ve müzik eğitimi alanında yazılmış makalelerin incelenmesi. Gazi Eğitim Fakültesi Dergisi, 45(1), 487–509. https://doi.org/10.17152/gefad.1469758
28. Halkiopoulos, C., & Gkintoni, E. (2024). Leveraging AI in e-learning: Personalized learning and adaptive assessment through cognitive neuropsychology—A systematic analysis. Electronics, 13(18), 3762. https://doi.org/10.3390/electronics13183762
29. Han, Y. (2025). Exploring a digital music teaching model integrated with recurrent neural networks under artificial intelligence. Scientific Reports, 15(1), 7495. https://doi.org/10.1038/s41598-025-92327-8
30. Klimova, B., & Pikhart, M. (2025). Exploring the effects of artificial intelligence on student and academic well-being in higher education: A mini-review. Frontiers in Psychology, 16, 1498132. https://doi.org/10.3389/fpsyg.2025.1498132
31. Köktürk-Güzel, B. E., Büyük, O., Bozkurt, B., & Baysal, O. (2023). Automatic assessment of student rhythmic pattern imitation performances. Digital Signal Processing, 133, 103880. https://doi.org/10.1016/j.dsp.2022.103880
32. Krause, A. E., North, A. C., & Davidson, J. W. (2019). Using self-determination theory to examine musical participation and well-being. Frontiers in Psychology, 10, 405. https://doi.org/10.3389/fpsyg.2019.00405
33. Laure, M., & Habe, K. (2024). Stimulating the development of rhythmic abilities in preschool children in Montessori kindergartens with music-movement activities: A quasi-experimental study. Early Childhood Education Journal, 52, 563–574. https://doi.org/10.1007/s10643-023-01459-x
34. Leukel, C., Leuders, T., Bessi, F., & Loibl, K. (2023). Unveiling cognitive aspects and accuracy of diagnostic judgments in physical education teachers assessing different tasks in gymnastics. Frontiers in Education, 8, 1162499. https://doi.org/10.3389/feduc.2023.1162499
35. Li, J., Xu, Y., Wang, X., Yuan, X., Liu, S., Li, M., Ren, X., Yang, D., Liu, S., Jiang, Z., Gou, Q., Yang, M., & Xu, Q. (2024). Flow and performance: A quantitative study of elicitation modeling in a piano performance perspective. Frontiers in Psychology, 15, 1386831. https://doi.org/10.3389/fpsyg.2024.1386831
36. Li, P. ping, & Wang, B. (2024). Artificial intelligence in music education. International Journal of Human-Computer Interaction, 40(16), 4183–4192. https://doi.org/10.1080/10447318.2023.2209984
37. Li, W., Cui, X., Manoharan, P., Dai, L., Liu, K., & Huang, L. (2025). AI-assisted feedback and reflection in vocal music training: Effects on metacognition and singing performance. Frontiers in Psychology, 16, 1598867. https://doi.org/10.3389/fpsyg.2025.1598867
38. Lin, H., & Chen, Q. (2024). Artificial intelligence (AI)-integrated educational applications and college students’ creativity and academic emotions: Students and teachers’ perceptions and attitudes. BMC Psychology, 12, 487. https://doi.org/10.1186/s40359-024-01979-0
39. Lincoln, Y. S. & Guba, E.G. (1985). Naturalistic inquiry. SAGE Publications.
40. Lu, W. (2024). Inevitable challenges of autonomy: Ethical concerns in personalized algorithmic decision-making. Humanities and Social Sciences Communications, 11, 1321. https://doi.org/10.1057/s41599-024-03864-y
41. Merchán Sánchez-Jara, J. F., González Gutiérrez, S., Cruz Rodríguez, J., & 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
42. Moral-Bofill, L., López de la Llave, A., & Pérez-Llantada, M. C. (2020). Relationships between high ability (gifted) and flow in music performers: Pilot study results. Sustainability, 12(10), 4289. https://doi.org/10.3390/su12104289
43. Nuristama, R. U. (2024). Empowering musical engagement: Applying self determination theory in digital music education for Generation Z. Harmonia: Journal of Music and Arts, 2(2), 112–124. https://doi.org/10.61978/harmonia.v2i2.1022
44. O’Leary, E. (2025). Considering the possibilities and problems of AI in music education: The need for critical literacies. Action, Criticism, and Theory for Music Education, 24(3), 138–164. https://doi.org/10.22176/act24.3.138
45. Ou, J., Nogueira, J., & Qin, C. (2025). Exploring the impact of AI-assisted practice applications on music learners' performance, self-efficacy, and self-regulated learning. Frontiers in Psychology, 16, 1675762. https://doi.org/10.3389/fpsyg.2025.1675762
46. Özer, M., Tanberkan, H., & Perc, M. (2025). Artificial intelligence threatens critical thinking in education systems. Yükseköğretim ve Bilim Dergisi, 15(2), 157–164. https://doi.org/10.5961/higheredusci.1747885
47. Pang, Z. (2022). Peculiarities of music performance in the flow state. Música Hodie, 22, e70537. https://doi.org/10.5216/mh.v22.70537
48. Patton, M. Q. (2015). Qualitative research & evaluation methods. SAGE Publications.
49. Phanichraksaphong, V., & Tsai, W.-H. (2021). Automatic evaluation of piano performances for STEAM education. Applied Sciences, 11(24), 11783. https://doi.org/10.3390/app112411783
50. Phanichraksaphong, V., & Tsai, W.-H. (2023). Automatic assessment of piano performances using timbre and pitch features. Electronics, 12(8), 1791. https://doi.org/10.3390/electronics12081791
51. Selwyn, N. (2024). On the limits of artificial intelligence (AI) in education. Nordisk Tidsskrift for Pedagogikk og Kritikk, 10, 3–14. https://doi.org/10.23865/ntpk.v10.6062
52. Song, H., Tsiakas, K., Ham, J., Markopoulos, P., & Barakova, E. I. (2024). ‘How would you score yourself?’: The effect of self-assessment strategy through robots on children’s motivation and performance in piano practice. International Journal of Social Robotics, 16, 327–340. https://doi.org/10.1007/s12369-023-01080-3
53. Tan, L., & Sin, H. X. (2019). Flow research in music contexts: A systematic literature review. Musicae Scientiae, 25(4), 399-428. https://doi.org/10.1177/1029864919877564
54. Teztel, G. (2016). Psikolojide akış kuramı ve müzik icrası bağlamında değerlendirilmesi. Konservatoryum, 3(2), 1–17. https://dergipark.org.tr/tr/pub/konservatoryum/issue/35182/390390
55. Üçer, Ö., Yılmaz, H., & Açar, Y. (2025). Investigation of artificial intelligence literacy levels of music teachers. Journal for the Interdisciplinary Art and Education, 6(2), 117–132. https://doi.org/10.5281/zenodo.15510414
56. Väkevä, L., & Partti, H. (2025). Generative AI as a collaborator in music education: An action-network theoretical approach to fostering musical creativities. Action, Criticism, and Theory for Music Education, 24(3), 16–52. https://doi.org/10.22176/act24.3.16
57. Verghis, A. M., Jose, B., Sheeja, P. R., Varghese, S. M., Mumthas, S., & Shaiju, K. S. (2025). Beyond personalization: Autonomy and agency in intelligent systems education. Frontiers in Education, 10, 1610239. https://doi.org/10.3389/feduc.2025.1610239
58. Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students’ academic development. Education Sciences, 15(3), Article 343. https://doi.org/10.3390/educsci15030343
59. Wang, P., & Liu, Y. (2025). The role of technophilia, positive experience and flow in enhancing learning efficiency in AI-based music education: A self-determination theory approach. European Journal of Education, 60, e70200. https://doi.org/10.1111/ejed.70200
60. Wang, T., & Zhang, J. (2025). Enhancing musical creativity through AI: A chain-mediation model of self-efficacy and emotional intelligence. Frontiers in Psychology, 16, 1665609. https://doi.org/10.3389/fpsyg.2025.1665609
61. Wang, Y., Abdul Karim, A., & Kamsin, I. F. (2025). The use of AI in music teaching in higher education: A systematic literature review. International Journal of Learning, Teaching and Educational Research, 24(11), 39–72. https://doi.org/10.26803/ijlter.24.11.3
62. Yavuz, M. S., Karaosmanoğlu, M. K., Yeprem, M. S., Karşıcı, G., & Sezikli, U. (2025). Türkiye'de müzik ve yapay zeka üzerine bir değerlendirme. Öneri Dergisi, 20 (MX Yaratıcı Endüstriler Çalıştayı 2024: Yapay Zeka Çağında Yaratıcı Endüstriler Özel Sayısı), 50–70. https://doi.org/10.14783/maruoneri.1499345
63. Yetişensoy, O. (2024). Tomorrow’s teachers and artificial intelligence: Exploring attitudes and perceptions of Turkish prospective social studies teachers. Eurasian Journal of Teacher Education, 5(1), 1–31. https://dergipark.org.tr/tr/pub/ejte/issue/84261/1408227
64. Yıldırım, A. & Şimşek, H. (2021). Sosyal bilimlerde nitel araştırma yöntemleri. Seçkin Yayıncılık.
65. Yu, X., Ma, N., Zheng, L., Wang, L., & Wang, K. (2023). Developments and applications of artificial intelligence in music education. Technologies, 11(2), 42. https://doi.org/10.3390/technologies11020042
66. Yüceland, E. (2025). Çalgı eğitimi bağlamında akış durumunun motivasyon ve dikkat süreçleri bakımından kuramsal analizi. Online Journal of Music Sciences, 10(4), 967-982. https://doi.org/10.31811/ojomus.1776098
67. Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11, 28. https://doi.org/10.1186/s40561-024-00316-7