Assessing the Influence of Cognitive Load and Engagement on Nursing Students' Intention to Use an AI-Supported VR Training System

• In Event: AERA Roundtable Session Friday 11:40 am Four Seasons Ballroom 2-3
  In Roundtable Session: Pedagogical Design and Educational Technologies (Table 1)

Abstract

Researchers have studied different factors from extended versions of the Technology Acceptance Model (TAM) to find whether they are valid in the context of VR. In this study, we assessed the effects of cognitive engagement and individual dimensions of cognitive load (CL) on the perceived usefulness (PU) and perceived ease of use (PEOU) of an AI-supported VR system for nursing students' patient management training. The results showed that engagement and PEOU are significant predictors of PU, and frustration (one of the dimensions of CL) and engagement are significant predictors of PEOU.

Authors

• Jhon Bueno-Vesga, Pennsylvania State University

• Xinhao Xu, University of Missouri

• Yuanyuan Gu, University of Missouri

• Hao He, Emporia State University

• Shangman Li, University of Missouri

• Yupei Duan, University of Missouri

• Sue Yun Fowler, University of Missouri

• Hillary L. Claunch, University of Missouri

• Jason Snyder, University of Missouri