Generating, interpreting and adapting a 3D learning environment using ANNs

Abstract

A simulated environment is generated and adapted through interaction with a user. Prior performance metrics indicating behavior of a student in performing a prior learning task are obtained. A student profile is updated based on the prior performance metrics. Lesson parameters indicating content to be included in an interactive lesson are obtained. Rules for the interactive lesson are then generated based on the student profile and the lesson parameters. The rules are applied to a classifier trained via a reference data set representing prior performance of a student population. Via the classifier, instructions for generating a simulated environment encompassing the interactive lesson are generated. A representation of the simulated environment is then generated based on the instructions.

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