생성형 AI 기반 작곡교육의 시스템 품질이 수업만족도에 미치는 영향: 자기조절학습의 매개효과를 중심으로

This study analyzes the impact of system quality in AI-based composition education on learners' class satisfaction and empirically explores the mediating effect of self-regulated learning in this relationship. The results of structural equation modeling analysis show that the system quality of AI-based composition education significantly enhances learners' self-regulated learning abilities (cognitive regulation, motivational regulation, and behavioral regulation), which in turn increases class satisfaction. Specifically, the finding that self-regulated learning positively impacts learners suggests that as learners improve their ability to set learning goals and adjust learning strategies, their satisfaction with the class also increases. This indicates that self-regulated learning plays a key role in enhancing learners' sense of achievement and engagement, which are important factors in boosting class satisfaction. Additionally, bootstrapping analysis confirmed the mediating effect of self-regulated learning abilities in the relationship between AI-based composition education and class satisfaction. This demonstrates that AI-based music education not only enhances learners' satisfaction with the class but also helps them develop self-regulated learning skills, which in turn strengthens their positive perception and satisfaction with the class. Generative AI provides personalized feedback and an interactive learning environment tailored to learners' individual needs and interests, supporting them in actively managing their learning process. This suggests that AI can complement aspects that are challenging in traditional educational settings. Therefore, this study emphasizes that the combination of generative AI and self-regulated learning is an important method for improving learner satisfaction and the quality of learning experiences, confirming that AI-based education is an effective tool for promoting learner-centered learning.