The Architecture and Early Results of the IL-PRO AI-Driven Immersive and Adaptive Learning System for Industrial Robotics

Abstract

While many traditional approaches to robotics training have been successful, the expense, space, and hazards associated with industrial robotics can be prohibitive and limit the scale at which students can be trained. Use of advanced digital technologies such as XR environments can provide economic and safe training alternatives. Previously introduced in this same forum, the Intelligent Learning Platform for Robotics Operations (IL-PRO) is now operational and in use in an undergraduate credentialing course at a major university. IL-PRO uses a multi-modal approach to automating instruction. It leverages students’ verbal responses and actions, a pre-trained large language model, and machine-learned models within an immersive (VR) environment for learning operations of robotic arms. At the core of the IL-PRO experience is the deployment of an automated learning system (ALS) designed to track student learning progress to personalize feedback and select i learning tasks. The ALS currently accounts for students’ levels of conceptual understanding and their motor skills relevant to operating the IL-Pro virtual robotic arm. This paper describes the learning content and system design of IL-PRO as currently implemented and presents sample student performance data from a recent pilot of the system.

Keywords: immersive learning experiences, AI, automated feedback, robotics instruction, game-based learning

DOI: 10.54941/ahfe1006661