Yapay Zekâ Destekli Müzik Eğitiminde Akış Deneyiminin Kavramsal Analizi

Abstract

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.

Keywords

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

Ethical Statement

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

Conceptual Analysis of Flow Experience in AI-Supported Music Education

Abstract

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.

Keywords

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

Ethical Statement

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

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