In a piano lesson, a student often imitates the teacher’s playing in terms of speed, dynamics, and fingering. While this learning model leverages one’s visual and even audial perception for emulation, it still lacks an important component of piano playing – the tactile sensation. We seek to convey the tactile sensations of the teacher’s keystrokes and then signal the student’s corresponding fingers. We implemented an instrumented fingerless glove called Tactile Teacher to detect finger taps on hard surfaces. Since finger taps generate acoustic signals and cause vibrations, we embedded three vibration sensors on the glove and use machine learning algorithms to analyze the data from the sensors. After a brief training procedure, this prototype can accurately identify single finger tap in a very good performance at above 89% accuracy, and two finger taps resulted in accuracy around 85%.