Agent-customized training for human learning performance enhancement

Training individuals from diverse backgrounds and in changing environments requires customized training approaches that align with the individual learning styles and ever-evolving organizational needs. Scaffolding is a well-established instructional approach that facilitates learning by incrementally removing training aids as the learner progresses. By combining multiple training aids (i.e. multimodal interfaces), a trainer, either human or virtual, must make real-time decisions about which aids to remove throughout the training scenario. A significant problem occurs in implementing scaffolding techniques since the speed and choice of removing training aids must be strongly correlated to the individual traits of a specific trainee. We detail an agent-based infrastructure that supports the customization of scaffolding routines as triggered by the performance of the trainee. The motivation for this agent-based approach is for integration into a training environment that leverages augmented reality (AR) technologies. Initial experiments using the simulated environment have compared the proposed adaptive approach with traditional static training routines. Results show that the proposed approach increases the trainees' task familiarity and speed with negligible introduction of errors.