Dynamically adaptive simulation based on expertise and cognitive load

摘要

Augmented and Virtual Reality (AR/VR) simulated environments offer an unprecedented opportunity to engage users in fully immersive, highly interactive environments that offer high fidelity, and authentic, virtual representations of the real world, supporting learner development without risk to the learner, or to the entities the learner is engaging with. Cognitive load theory (CLT) describes the degree to which learners' available cognitive capacity, both in working memory and in information processing, is being utilized. Dynamically assessing and responding to cognitive load in simulated environments is, we suggest, a powerful new tool for supporting more effective learning. By combining dynamic cognitive load assessment with other performance measures, data analytics and artificial intelligence (AI), we create the potential opportunity to establish a new partnership between the learner and the learning environment. In addition to providing adaptive and dynamic responses to the learner, continuous and pervasive awareness of the learner's cognitive load allows the environment to customize certain parameters to individual learners, optimizing learning outcomes. Finally, utilizing AI and machine learning algorithms, simulated environments may be able to predict and anticipate learner cognitive states, which in turn can help further support the learning process.