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Abstract

Advances in virtual reality (VR) technology afford creation of immersive virtual learning environments that simulate real-life learning contexts with increasing fidelity. When supported by sufficiently advanced artificial intelligence (AI)-based tutoring software, such environments may facilitate asynchronous, embodied learning approaches for learning hard, procedural skills in industrial settings – addressing timeliness, accuracy, and scalability issues common in the industry.

This chapter reflects on the pedagogical setting of immersive virtual reality-based hard skills training guided by an AI tutor software agent. We examine the interfacing of traditional intelligent tutoring system (ITS) software with an immersive virtual environment. Further, we suggest the philosophies of embodied, embedded, enacted, and extended (4E) cognition as a way to fully consider learner epistemology in a virtual world and to account for and make full use of the unique opportunities afforded by the synthetic nature of the immersive virtual learning environment.

To explore possibilities for improved pedagogical approaches, we project the 4E cognition approach into the abovementioned learning context and outline a theoretical framework for a VR-native AI tutor. We then propose VR-native pedagogical principles for such as framework that could inform follow-on research.

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Deep learning.

Yann LeCun, Yoshua Bengio, Geoffrey E Hinton (2015)

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

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Grounded cognition.

Lawrence Barsalou (2008)

Grounded cognition rejects traditional views that cognition is computation on amodal symbols in a modular system, independent of the brain's modal systems for perception, action, and introspection. Instead, grounded cognition proposes that modal simulations, bodily states, and situated action underlie cognition. Accumulating behavioral and neural evidence supporting this view is reviewed from research on perception, memory, knowledge, language, thought, social cognition, and development. Theories of grounded cognition are also reviewed, as are origins of the area and common misperceptions of it. Theoretical, empirical, and methodological issues are raised whose future treatment is likely to affect the growth and impact of grounded cognition.