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

Autor: Mulian, Hadar, Shlomov, Segev, Limonad, Lior, Noccaro, Alessia, Buscaglione, Silvia
Rok vydání: 2023
Předmět:
Zdroj: EAAI Symposium: AI for Education 2024
Druh dokumentu: Working Paper
DOI: 10.1609/aaai.v38i21.30369
Popis: 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.
Comment: arXiv admin note: The first version of this paper has been removed by arXiv administrators as the submitter did not have the right to agree to the license at the time of submission. This version resolves the rights issue, includes two additional authors, and is cleared to go public
Databáze: arXiv