In the realm of music education, sight-reading is a crucial skill for pianists. Our groundbreaking study delves into the fascinating connection between a pianist's eye movements and their sight-reading abilities. By leveraging machine learning and eye-tracking technology, we aim to uncover patterns that could revolutionize how pianists learn and improve their sight-reading skills.

This innovative research project, led by Tim Smith, Kenrick Mock, and Bogdan Hoanca, explores the potential of using eye-tracking data to assess and enhance sight-reading proficiency. By analyzing the correlation between machine-extracted eye gaze features and sight-reading performance, we hope to develop tools that can provide real-time feedback and personalized guidance to piano students.

Our ultimate goal is to create an eye tracking-enabled computer system that can help students assess their current sight-reading abilities and offer targeted suggestions for improvement. This technology could transform music education, making the learning process more efficient and accessible for pianists of all levels.

As we continue to gather and analyze data, we're excited about the potential implications of our findings for music pedagogy and the broader field of human-computer interaction. Stay tuned for updates on this cutting-edge research that bridges the gap between technology and musical artistry.