A research team led by Professor Hong-Yeol Um of the Department of Design at UNIST has achieved success in the global music tech market with AI-based music learning technology that reads a pianist's hand movements in real time and provides immediate feedback based on performance data. 'ROLI Piano' and 'ROLI Airwave,' whose design development was led by the team in collaboration with the British music tech company ROLI, have won awards at international design and technology events, demonstrating their potential for commercialization.

UNIST announced on the 18th that two products incorporating this technology received the 2026 CES Innovation Award and the iF Design Award, and were also included in 'The Best Inventions' selected by the American news magazine Time. This is noteworthy in that design research from a university laboratory has led to actual market products through collaboration with a global company.

The key is the hand tracking technology applied to the 'ROLI Airwave'. This device captures the player's hand movements in real time, analyzes the position and movement of the hands on the keyboard, and provides visual guidance and feedback based on finger positions and gestures. It is characterized by a design that allows for an intuitive understanding of hand position, timing, and expression methods, which were difficult to master in traditional instrument education.

This technology combines with ROLI's digital instrument system to expand into an interactive platform that supports performance, learning, and creation. In particular, it features a structure that integrates with the 'AI Music Coach' to analyze the performer's movements and performance data, returning the results in real time. This allows users to review their playing and immediately address areas for improvement, even while practicing alone.

The scope of application extends across the entire fields of music education and creation. By reducing trial and error during the learning process based on personalized feedback and analyzing a performer's mode of expression, it presents a data-driven, interactive educational environment distinct from traditional one-way lesson methods. Its structure allows for broad utilization by everyone from novice learners to creators.

This achievement is regarded as a case demonstrating that research combining AI, design, and music education can be implemented as actual products in the industrial field. At the same time, it has shown the potential for music education to expand beyond experience- and sense-based training to personalized learning methods that combine data analysis and real-time feedback.