Dr. Julian Abich IV

Senior Human Factors Engineer, Quantum Improvements Consulting

Dr. Julian Abich IV is a Senior Human Factors Engineer at Quantum Improvements Consulting. HIs work focuses on implementing a user-centered approach to design, develop, and assess innovative training and learning solutions. He has over 15 years of experience working side-by-side with stakeholders and end-users to improve the experience, effectiveness, and efficiency of tasks, processes, and technologies used in real-world applications. These technologies span from mobile to immersive platforms, such as augmented, virtual, and mixed reality. He continues to serve the research community through his national and international publications and presentations, top-tiered journal reviews, and participation in conference program committees, currently as the Deputy Conference Chair for MODSIM World 2023. He served on the UCF faculty and continues to support their Modeling and Simulation graduate program as a Graduate Faculty Scholar. He also advocates for Science, Technology, Engineering, Arts, and Mathematics (STEAM) outreach efforts by encouraging public support and fostering posterity’s interest within these domains.

Factors and Considerations to Improve the Probability of XR Adoption for Training

Extended reality (XR) technologies have been utilized as effective training tools across many contexts, including military aviation, although commercial aviation has been slower to adopt these technologies. While there is hype behind every new technology, XR technologies have evolved past the emerging classification stage and are at a state of maturity where their impact on training is supported by empirical evidence. Diffusion of innovation theory (Rogers, 1962) presents key factors that, when met, increase the likelihood of adoption. These factors consider the relative advantage, trialability, observability, compatibility, and complexity of the XR technology. Further, there are strategic approaches that should be implemented to address each of these innovation diffusion factors.

This presentation will discuss each diffusion factor, provide exemplar use cases, and outline evidence-backed considerations to improve the probability of XR adoption for training. Considerations will discuss various effects that may occur with the introduction XR technology, such as the novelty effect where improved performance initially improves due to new technology and not because of learning. Key questions will be presented that should be addressed under each diffusion factor that will help guide the information needed to support the argument for XR adoption. Importantly, the quality of research evidence to support XR implementation and adoption is critical to reducing the risk of ineffective training. Therefore, a discussion of research-related considerations will also be presented to ensure an appropriate interpretation of existing XR research literature. The goal is to provide the audience with an objective lens to help them determine whether XR technologies should be adopted for their training needs.