I-Corps: Translation potential of an artificial intelligence-based violin tutoring system

The broader impact of this I-Corps project is the development of an artificial intelligence (AI) platform to help students learn to play the violin more effectively and affordably. Currently, many students struggle to get private music lessons due to their high cost and limited availability. This platform solves that problem by using regular webcams and microphones, making it easy for anyone to access with a computer.

The system provides real-time feedback on how students are playing, helping them improve their posture, bowing, and sound quality. In addition, the technology helps them avoid common mistakes and progress faster while reducing the risk of injuries. Teachers also may benefit from this technology by getting detailed insights into their students' practice habits, allowing them to offer more personalized support.

This AI-driven tool may expand access to music education, making it available to more students regardless of where they live or their financial situation. The solution also may appeal to schools, music teachers, and students looking for affordable ways to improve their skills, whether they are learning online or in person. This technology has the potential to keep students more engaged in their musical journey and help them achieve their goals faster.

This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of advanced artificial intelligence (AI) algorithms that analyze how violin students play by using both visual and audio inputs. The technology evaluates students' posture and bowing technique, providing instant feedback for improvement.

Additionally, a unique collection of recorded violin exercises has been digitized to assess students' skills and suggest tailored practice materials. The project aims to enhance online music education by offering personalized tools that help students develop the motor skills essential for playing an instrument. The solution also advances research on how sound and movement interact, creating educational modules customized to individual learners.

This project may improve music education and also contribute to scientific knowledge in areas like causal modeling and computational analysis of motion and sound. The data and tools developed may be applied broadly to understand how perception and physical actions are linked, providing valuable insights into the science of learning.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.