The Role of Cognitive Load and Engagement in Students' Adoption of an AI-supported VR Training System

The Technology Acceptance Model (TAM) has been used extensively to understand technology adoption in the context of virtual reality (VR). The model includes external variables that are important drivers of attitudes towards adopting technology. In this mixed-methods study, we assessed the effects of cognitive engagement and individual dimensions of cognitive load (CL) on the attitudes driving the intention to use an AI-supported VR system for nursing students' patient management training: perceived usefulness (PU) and perceived ease of use (PEOU). The participants were a group of nursing students from a university in the Midwestern United States. We also explored interview data to understand the participants' perceptions about the resulting factors. The quantitative results showed that engagement and PEOU are significant predictors of PU, and so are frustration (one of the dimensions of CL) and engagement with PEOU. Interview data revealed that participants' frustrations did not always have a negative effect. They generally enhanced their engagement by making the scenarios feel realistic and valuable for skill development.