Mimicking the Maestro: Exploring the Efficacy of a Virtual AI Teacher in Fine Motor Skill Acquisition

Hadar Mulian, Segev Shlomov, Lior Limonad, Alessia Noccaro, Silvia Buscaglione

Abstract:

Motor skills, especially fine motor skills like handwriting,
play an essential role in academic pursuits and everyday life.
Traditional methods to teach these skills, although effective,
can be time-consuming and inconsistent. With the rise of advanced
technologies like robotics and artificial intelligence,
there is increasing interest in automating such teaching processes.
In this study, we examine the potential of a virtual
AI teacher in emulating the techniques of human educators
for motor skill acquisition. We introduce an AI teacher
model that captures the distinct characteristics of human instructors.
Using a reinforcement learning environment tailored
to mimic teacher-learner interactions, we tested our
AI model against four guiding hypotheses, emphasizing improved
learner performance, enhanced rate of skill acquisition,
and reduced variability in learning outcomes. Our findings,
validated on synthetic learners, revealed significant improvements
across all tested hypotheses. Notably, our model
showcased robustness across different learners and settings
and demonstrated adaptability to handwriting. This research
underscores the potential of integrating Imitation and Reinforcement
Learning models with robotics in revolutionizing
the teaching of critical motor skills.