Yapay zekâ, yapay zekâ söyle bana: Keman eğitiminde yapay zekâ destekli geri bildirimin entonasyona etkisine yönelik bir model çalışması

Abstract

Grimm Kardeşler’in ünlü masalında sihirli ayna, “Ayna, ayna söyle bana…” sorusuna gerçeği yanıtlayarak kraliçeye karşı dürüst davranır. Benzer biçimde, bu araştırmada üretken yapay zekâ, keman öğrencilerinin entonasyon performanslarına ilişkin anlık ve nesnel geri bildirimler sunan bir “dijital ayna” işlevi görmüştür. Karma yöntem araştırma deseninin kullanıldığı bu çalışmanın amacı, üretken yapay zekâ destekli geri bildirimin keman eğitiminde entonasyon becerisini geliştirmedeki etkisini, süreçte sunulan içeriklerin kapsamını ve öğrencilerin uygulamaya ilişkin deneyimlerini bütüncül olarak incelemektir. Çalışma grubu, 2024-2025 akademik yılında Türkiye’deki bir devlet üniversitesinin müzik eğitimi bölümüne kayıtlı 12 keman öğrencisinden oluşmuştur. Dört haftalık deneysel süreçte, öğrencilerin performansları Cubase yazılımında kaydedilmiş, frekans analizi yapılarak cent cinsinden entonasyon verileri elde edilmiştir. Bu veriler, öğretmen müdahalesi olmaksızın ChatGPT ve DeepSeek yapay zekâ sistemleri tarafından analiz edilerek değerlendirilmiş ve öğrencilere teknik ile müzikal boyutta öneriler sunulmuştur. Nicel veriler, Bağımlı Örnekler t-testi ile öntest–sontest puanları karşılaştırılarak analiz edilmiş; sonuçlar, sontest lehine istatistiksel olarak anlamlı bir fark ortaya koymuştur. Nitel veriler ise betimsel analiz yöntemiyle incelenmiş; öğrenciler, yapay zekânın sağladığı detaylı analizlerden, kişiselleştirilmiş çok boyutlu önerilerden ve motivasyon artırıcı yönlendirmelerden memnuniyet duyduklarını ifade etmiştir. Çalışma, müzik eğitimi bağlamında ve özellikle keman eğitiminde üretken yapay zekâ destekli geri bildirimin entonasyon becerisi üzerindeki etkisine yönelik sınırlı sayıdaki araştırmalara deneysel ve nitel bulgular sunarak literatüre katkı sağlamaktadır. Gelecek çalışmalarda, görsel ve işitsel geri bildirim unsurlarını entegre eden ve öğrencinin çalışma planını otomatik olarak uyarlayabilen sistemlerin geliştirilmesi önerilmektedir.

Keywords

• müzik eğitimi
• keman eğitimi
• entonasyon
• bilgisayar destekli öğrenme
• yapay zekâ
• geri bildirim

AI AI tell me: A model study on the effect of artificial intelligence-assisted feedback on intonation in violin education

Abstract

In the famous fairy tale by the Brothers Grimm, the magic mirror responds truthfully to the question, “Mirror, mirror, tell me…,” thus acting with honesty toward the queen. Similarly, in this study, generative artificial intelligence functioned as a “digital mirror” by providing instant and objective feedback on violin students’ intonation performance. The aim of this study, which employed a mixed-methods research design, was to comprehensively examine the effect of generative AI-assisted feedback on the development of intonation skills in violin education, the scope of the content delivered during the process, and students’ experiences with the application. The study group consisted of 12 violin students enrolled in the Department of Music Education at a state university in Türkiye during the 2024–2025 academic year. Over a four-week experimental process, students’ performances were recorded using Cubase software, and intonation data in cents were obtained through frequency analysis. These data were analyzed and evaluated by the ChatGPT and DeepSeek AI systems (without teacher intervention), which then provided students with both technical and musical recommendations. Quantitative data were analyzed by comparing pre-test and post-test scores using the paired-samples t-test. The results revealed a statistically significant difference in favor of the post-test. Qualitative data were examined through descriptive analysis, and students reported satisfaction with the detailed analyses, personalized multi-dimensional recommendations, and motivational guidance provided by the AI. This study contributes to the limited body of research on the effect of generative AI-assisted feedback on intonation skills in the context of music education, particularly violin instruction, by presenting both experimental and qualitative findings. Future studies are recommended to develop systems that integrate visual and auditory feedback elements and can automatically adapt students’ practice plans.

Keywords

• music education
• violin education
• intonation
• computer-based learning
• artificial intelligence
• feedback

References

1. Abbas, M., Jam, F. A. ve Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21, 10. https://doi.org/10.1186/s41239-024-00444-7
2. Akdeniz, A. Ö. (2023). Keman eğitiminde tını, ses rengi, ton üretimi ve müzikalitenin incelenmesi. Korkut Ata Türkiyat Araştırmaları Dergisi, (12), 1452-1470. https://doi.org/10.51531/korkutataturkiyat.1332319
3. Aksoy, Y. (2023). Seeing sounds: The effect of computer-based visual feedback on intonation in violin education. International Journal of Education and Literacy Studies, 11(2), 2-12. https://doi.org/10.7575/aiac.ijels.v.11n.2p.2
4. Angı, Ç. E. (2013). Keman öğretiminde karşılaşılan entonasyon problemleri ve çözüm önerileri. Sanat Eğitimi Dergisi, 1(2), 48-69. https://doi.org/10.7816/sed-01-02-04
5. Altun, E. (2024). Yapay zekâ ve pedagoji: Eğitimde fırsatlar ve zorluklar. Journal of Digital Technologies and Education, 3(1), 80-95.
6. Aykut, E. ve Taş, S. (2023). Gamified violin playing in virtual reality based metaverse environment. Journal of Emerging Computer Technologies, 3(1), 7-11. https://doi.org/10.57020/ject.1298919
7. Bagırov, R. (2020). Keman metodu. EÇM Yayınevi.
8. Büyükaksoy, F. (1997). Keman öğretiminde ilkeler ve yöntemler. Armoni Ltd. Şti.

9. Büyüköztürk, Ş., Çokluk, Ö. ve Köklü, N. (2013). Sosyal bilimler için istatistik (12. bs.). Pegem Akademi.
10. Can, Ö. (2019). Keman eğitimi 1. Önder Matbaacılık.
11. Canyakan, S. (2013). Profesyonel müzik kayıtlarında perde doğrulamanın performansa yönelik etkileri (Tez No. 331903) [Yüksek lisans tezi, Dokuz Eylül Üniversitesi]. Yükseköğretim Kurumu Ulusal Tez Merkezi.
12. Chan, C. K. Y. ve Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, 43. https://doi.org/10.1186/s41239-023-00411-8
13. Chen, J., Woollacott, M. H., Pologe, S. ve Moore, G. P. (2008). Pitch and space maps of skilled cellists: Accuracy, variability, and error correction. Experimental Brain Research, 188(4), 493-503. https://doi.org/10.1007/s00221-008-1380-2
14. Creswell, J. W. ve Plano Clark, V. L. (2015). Designing and conducting mixed methods research (3. bs.). SAGE Publications.
15. Dale, P. (2023). Teachers reluctant to use technology in music teaching, say experts. University of York. https://www.york.ac.uk/news-and-events/news/2023/research/reluctance-to-use-technology-in-music-teaching/
16. Dalmazzo, D. ve Ramirez, R. (2017). Air violin: A machine learning approach to fingering gesture recognition. In Proceedings of the 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education (pp. 63-66). Association for Computing Machinery. https://doi.org/10.1145/3139513.3139526
17. Fernández-Barros, A., Duran, D. ve Viladot, L. (2023). Peer tutoring as a tool for developing the intonation of violin and viola students in elementary music education. Music Education Research, 25(2), 176-189. https://doi.org/10.1080/14613808.2023.2193210
18. Fayez, S. (2001). Kuramdan uygulamaya başlangıç keman eğitimi. Yurtrenkleri Yayınevi.
19. Flesch, C. (2000). The art of violin playing. Carl Fischer LLC.
20. Ganioğlu, T. (2016). Keman çalma eğitiminde sağ el/sol el tekniği. Akademisyen Kitabevi.
21. Geringer, J. M., MacLeod, R. B., Madsen, C. K. ve Napoles, J. (2015). Perception of melodic intonation in performances with and without vibrato. Psychology of Music, 43(5), 675-685. https://doi.org/10.1177/0305735614534004
22. Ghaeimi, F., Aghazadeh, Y. ve Salehi, K. (2025). Transforming education with artificial intelligence: A systematic review of applications, potentials, and outcomes. Sciences and Techniques of Information Management, 21(1). https://doi.org/10.22091/stim.2025.10570.2083
23. Gliem, J. A. ve Gliem, R. R. (2003). Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales. Midwest Research-to-Practice Conference in Adult, Continuing, and Community Education, The Ohio State University, Columbus, OH.
24. Gün, E., Elmas Gün-Duru, E. ve Demirtaş, O. (2016). Müzik eğitiminin bilişsel gelişime etkisi. The Journal of Academic Social Science Studies, 50, 117-124. https://doi.org/10.9761/JASSS3555
25. Han, Y. (2025). Exploring a digital music teaching model integrated with recurrent neural networks under artificial intelligence. Scientific Reports, 15, 7495. https://doi.org/10.1038/s41598-025-92327-8
26. Haenlein, M. ve Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5-14. https://doi.org/10.1177/0008125619864925
27. Holmes, W., Bialik, M. ve Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
28. Joordens, S. (2023). Education’s paradigm shift: How AI is reshaping learning. University World News. https://www.universityworldnews.com/post.php?story=20231129101102152
29. Kalender, C. ve Akgül Barış, D. (2022). Effectiveness of the teaching program prepared to improve intonation skills in violin education. Education and Science, 47(211), 171-193. https://doi.org/10.15390/EB.2022.11530
30. Knotts, C. J. (2018). The effect of tactile markers on intonation and posture of beginning violin and viola students [Doktora tezi, Florida State University]. http://purl.flvc.org/fsu/fd/2018_Su_Knotts_fsu_0071E_14625
31. Lee, S. S. ve Moore, R. L. (2024). Harnessing generative AI (GenAI) for automated feedback in higher education: A systematic review. Online Learning, 28(3), 82-104. https://doi.org/10.24059/olj.v28i3.4593
32. Li, J. (2023). Advancing violin pedagogy through biomedical inspired AI: A digital technology assisted approach to violin teaching. Journal of Commercial Biotechnology, 28(2), 363-376. https://commercialbiotechnology.com/article-detail/?id=1498
33. Li, P. ve 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
34. Liu, W. (2025). Using the Trala application for learning to play the violin: A study of techniques, which affect listeners. Acta Psychologica, 255, 104930. https://doi.org/10.1016/j.actpsy.2025.104930
35. Loeffler, B. D. (2006). Instrument timbres and pitch estimation in polyphonic music [Yüksek lisans tezi, Georgia Institute of Technology]. https://repository.gatech.edu/server/api/core/bitstreams/6fe78bf0-ea7c-49e2-ba03-2690dd669f85/content
36. 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
37. Marinova, E. (2024). Impactul inteligenței artificiale în educația muzicală și artele spectacolului. Symbolon, 25(1(46)), 99-105. https://doi.org/10.46522/S.2024.01.8
38. Merchán Sánchez Jara, J. F., González Gutiérrez, S., Cruz Rodríguez, J. ve 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
39. Merriam, S. (2018) Qualitative research. A guide to design and implementation (S. Turan, Ed. ve Çev.). Nobel Akademik Yayıncılık.
40. Miles, M. B. ve Huberman, A. M. (1994). Qualitative data analysis: An expanded source book. Sage.
41. Moradi, H. (2025). Integrating AI in higher education: Factors influencing ChatGPT acceptance among Chinese university EFL students. International Journal of Educational Technology in Higher Education, 22, 30. https://doi.org/10.1186/s41239-025-00530-4
42. Morrison, S. ve Fyk, J. (2002). Intonation. In R. Parncutt ve G. McPherson (Ed.), The science ve psychology of music performance (s. 183-197). Oxford University Press.
43. Müller, C. ve Mildenberger, T. (2021). Facilitating flexible learning by replacing classroom time with an online learning environment: A systematic review of blended learning in higher education. Educational Research Review, 34, 100394. https://doi.org/10.1016/j.edurev.2021.100394
44. Nunez, M. L. (2002). Comparison of aural and visual instructional methodologies designed to improve the intonation accuracy of seventh -grade violin and viola instrumentalists (Yayın No. 3127050). [Doktora tezi, University Of North Texas]. https://www.proquest.com/dissertations-theses/comparison-aural-visual-instructional/docview/305553042/se-2
45. OECD. (2021). Digital education outlook 2021: Pushing the frontiers with artificial intelligence, blockchain and robots. OECD Publishing. https://www.oecd.org/en/publications/oecd-digital-education-outlook-2021_589b283f-en.html
46. Özcan, A. T. (2017). Keman eğitiminde çoksesli aranje tekniklerinin kullanımı. Mersin Üniversitesi Eğitim Fakültesi Dergisi, 13(1), 290-302. https://doi.org/10.17860/mersinefd.305980
47. Özdemir, A. (2024). Dijital sanatın geleceği, yapay zekâ ile yeni ufuklar. TURAN-SAM, 16(64), 545-552. https://www.ceeol.com/search/article-detail?id=1292220
48. Pardue, L. S. ve McPherson, A. P. (2019). Real-time aural and visual feedback for improving violin intonation. Frontiers in Psychology, 10, 627. https://doi.org/10.3389/fpsyg.2019.00627
49. Rees, F. J. (2002). Distance learning and collaboration in music education. In R. Colwell ve C. Richardson (Ed.), The new handbook of research in music education (s. 257-273). Oxford University Press.
50. Roads, C. (1980). Artificial intelligence and music. Computer Music Journal, 4(2), 13-25. https://doi.org/10.2307/3680079
51. Say, A. (2009). Müzik sözlüğü. Müzik Ansiklopedisi Yayınları.
52. Sökezoğlu Atılgan, D. ve Tazegül, G. (2024). The impact of accompaniment in the instrument (violin) education course on the musical development of the student and their perspective on the course. Eurasian Journal of Music and Dance, 25, 183-209. https://doi.org/10.31722/ejmd.1524512
53. Sanganeria, S. ve Gala, R. (2024). Tuning music education: AI-powered personalization in learning music. In Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS 2024), Creative AI Track (s. 1-14). https://doi.org/10.48550/arXiv.2412.13514
54. Spiegel, M. F. ve Watson, C. S. (1984). Performance on frequency-discrimination tasks by musicians and nonmusicians. The Journal of the Acoustical Society of America, 76(6), 1690-1695. https://doi.org/10.1121/1.391605
55. Spohr, L. (1843). Louis Spohr’s celebrated violin school. R. Cocks & Co.
56. Suzuki, S. (2007). Suzuki violin school: Violin part. Alfred Publishing.
57. Taş, F. (2020). Yaylı çalgılar eğitiminde entonasyonu geliştirmeye yönelik kullanılan üç yöntemin karşılaştırılması. İdil, 9(76), 1802-1820. https://doi.org/10.7816/idil-09-76-04
58. Tertis, L. (1975). My viola and I: A complete autobiography: With beauty of tone in string playing and other essays (2. bs.). Elek.
59. Thurmond, V. A. (2001). The point of triangulation. Journal of Nursing Scholarship, 33(3), 253-258. https://doi.org/10.1111/j.1547-5069.2001.00253.x
60. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R. ve Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10, 15. https://doi.org/10.1186/s40561-023-00237-x
61. Ursavaş, Ö. F., Yalçın, Y., İslamoğlu, H., Bakır-Yalçın, E. ve Cukurova, M. (2025). Rethinking the importance of social norms in generative AI adoption: Investigating the acceptance and use of generative AI among higher education students. International Journal of Educational Technology in Higher Education, 22, 38. https://doi.org/10.1186/s41239-025-00535-z
62. Wang, X. ve Fitri bin Haris, M. (2025). AI-driven violin performance evaluation: A deep learning approach for automated assessment and personalized feedback in music education. In Proceedings of the 2025 International Conference on Generative Artificial Intelligence and Digital Media (s. 126-132). Association for Computing Machinery. https://doi.org/10.1145/3734921.3734941
63. Wei, J., Karuppiah, M. ve Prathik, A. (2022). College music education and teaching based on AI techniques. Computers and Electrical Engineering, 100, 107851. https://doi.org/10.1016/j.compeleceng.2022.107851
64. Xiao, X. (2025). Innovative application of artificial intelligence in piano music education and evaluation. Journal of Computational Methods in Sciences and Engineering. https://doi.org/10.1177/14727978251361785
65. Yağışan, N. (2008). Keman çalmanın biyomekanik analizi. Eğitim Kitabevi.
66. Yang, L., Rajab, K. Z. ve Chew, E. (2016). Ava: An interactive system for visual and quantitative analyses of vibrato and portamento performance styles. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR) (s. 108-114).
67. Yavuz, M. S., Karaosmanoğlu, M. K., Yeprem, M. S., Karşıcı, G. ve Sezikli, Ü. (2025). Türkiye’de müzik ve yapay zekâ üzerine bir değerlendirme [Özel sayı]. Öneri Dergisi, 20 (MX), 50-70. https://izlik.org/JA32XA72NY
68. Yıldırım, A. ve Şimşek, H. (2008). Sosyal bilimlerde nitel araştırma yöntemleri. Seçkin Yayıncılık.
69. Yin, J., Wang, Y. ve Hsu, D. (2005). Digital violin tutor: An integrated system for beginning violin learners. In Proceedings of the 13th Annual ACM International Conference on Multimedia (MULTIMEDIA ’05) (s. 976-985). Association for Computing Machinery. https://doi.org/10.1145/1101149.1101353
70. Yu, X., Ma, N., Zheng, L., Wang, L. ve Wang, K. (2023). Developments and applications of artificial intelligence in music education. Technologies, 11(2), 42. https://doi.org/10.3390/technologies11020042
71. Zabanal, J.-R. A. (2020). Improving intonation with a drone accompaniment. American String Teacher, 70(4), 35-40. https://doi.org/10.1177/0003131320963107
72. Zhou, W. ve Kim, Y. (2024). Innovative music education: An empirical assessment of ChatGPT-4’s impact on student learning experiences. Education and Information Technologies, 29, 20855-20881. https://doi.org/10.1007/s10639-024-12705-z