Dan Wells

Papers

Total Citations

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About

Dan Wells is a researcher at the intersection of robotics, human-computer interaction, and music education, best known for his pioneering work in developing interactive robotic systems for musical learning. His most cited paper, "instruMentor: An Interactive Robot for Musical Instrument Tutoring" (2019), introduces a novel robotic platform designed to provide real-time, personalized feedback to students learning instruments. This work has garnered 7 citations, establishing Wells as a key contributor to the emerging field of robotic tutoring in the arts. By combining principles of mechanical design, sensor integration, and pedagogical algorithms, Wells demonstrates how robots can serve as patient, adaptive instructors—offering corrections on posture, timing, and technique without replacing human teachers. His research not only advances assistive robotics but also opens new pathways for accessible music education, particularly for learners without access to private instructors. Wells’ contributions are notable for their interdisciplinary approach, bridging engineering and musicology, and his work continues to inspire further exploration into how embodied AI can enrich creative learning environments.