Immersive Modular Preparedness Intelligent Tutor for the National Institute for Occupational Safety and Health (IMPRINT NIOSH)

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Project Summary/Abstract National Institute for Occupational Safety and Health (NIOSH) Hierarchy of Control (HoC) measures help keep workers safe by determining how to overcome site specific challenges in a way that is efficacious and efficient. Instructors must deliver engaging hybrid training to support worker and first responder safety and adherence to protocol in the field. This often burdens instructors to deliver Zoom or PowerPoint simulations that, although based on real events, fail to meaningfully engage or immerse trainees. This lack of engagement can limit learning, putting trainees and others at risk by decreasing their ability to adhere to protocol when responding to incidents in the field. Therefore, emergency response training organizations need a cost- effective training solution that increases the stakes and authenticity of simulations to better equip trainees to remain safe and effective when they are in the field. Charles River Analytics, in partnership with The New England Consortium proposes to develop and evaluate an Immersive Modular Preparedness Intelligent Tutor for the National Institute for Occupational Safety and Health (IMPRINT NIOSH). IMPRINT NIOSH aims to provide a robust, commercial, portable virtual reality (VR) solution that will be complemented by an intelligent virtual training system and development framework that actively improves trainees' ability to perform HoC procedures within a range of realistic case studies, field scenarios, modules, and activities. IMPRINT NIOSH will include an intelligent tutoring system (ITS) that guides training in the untethered Oculus Quest VR headset to provide an immersive, personalized virtual training experience. With IMPRINT NIOSH, trainees can overcome complex, dangerous situations in a safe, controlled environment through guided and immersive procedure rehearsal. Trainees will be accompanied throughout their experience with an automated “training buddy”, that employs adult learning theory in the context of lessons to enhance training outcomes. In Phase II, we will produce a system that complements existing training with a production ready VR case study development suite and a library of VR case studies that replace or augment standard paper and PowerPoint scenario-based activities used to prepare trainees.