LAUSR

Virtual reality, which enables users to engage in physical activities in ways distinct from those in the real world, is increasingly recognized for its potential to enhance motor skill acquisition. Research on co-embodiment learning, in which instructors and learners utilize a single avatar that represents a weighted average of their movements, has demonstrated its efficacy in facilitating motor skill development. However, the current implementation of co-embodiment learning necessitates the real-time participation of instructors proficient in both virtual reality and co-embodiment, which poses challenges for its widespread adoption. To address this limitation, this study proposed a method for developing instructors trained on human motor data to effectively support motor skill learning through co-embodiment. The AI model was trained using supervised learning on data obtained from human motor learning sessions that employed co-embodiment. To evaluate the performance of the AI instructor, we compared the learning performance in co-embodiment learning with that of the AI instructor, recorded human instructor data, and a human instructor as well as in solo learning. The results showed that practicing with the AI instructor significantly improved learning efficiency compared with practicing alone or with recorded data and was comparable to that achieved by practicing with a human instructor.