  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 using these technologies, via human-robot and human-computer
interactions. 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 Reinforcement Learning and
Imitation Learning models with robotics in revolutionizing the teaching of
critical motor skills.

Machine Learning