HUMAN-AI COLLABORATION AND LEARNING ANALYTICS IN A PIANO CMOOC: PSYCHOLOGICAL INSIGHTS INTO MOTIVATION AND SELF-REGULATED LEARNING AMONG HIGH SCHOOL STUDENTS

Abstract

This paper addresses the effect of human-AI collaboration and learning analytics on motivation and self-managed learning of students enrolled in a Piano Connectivist Massive Open Online Course (CMOOC) in high school. With the introduction of intelligent tutoring systems and real-time analytics dashboard, however, AI-enabled feedback systems are also redefining the interaction of learners in courses that demand complex learning skills such as music education. According to Self-Determination Theory (Deci and Ryan, 2000) and the Zimmerman (2002) interactive AI feedback, peer collaboration, and performance analytics, the proposed study explores how interactive AI feedback, peer collaboration, and performance analytics influence intrinsic motivation and goal setting and self-monitoring behaviors. The mixed methods approach was used to gather data about 180 students attending a semester-long course in piano CMOOC, which combined quantitative learning analytics data with qualitative reflections and interviews. The results indicate that AI-based scaffolding improves student independence and perseverance, especially with facilitation and peer interaction of the teacher. In addition, the integration of AI analytics helped the students to monitor their progress, manage emotions, and stay motivated with individual insights. However, in some cases excessive dependence on algorithmic feedback minimized creativity and expression of emotion in music representation. The paper adds to the growing debate concerning the human-artificial intelligence synergy in education by providing the psychological understanding of how technological-mediated learning space can support a moderate autonomy and prolonged attachment among teenage students.