Better understanding of how to improve and augment human learning requires multidisciplinary research ranging from molecular (i.e., neuron) biology and cognitive science to behavioral science and cultural history before integrating results into advanced learning technologies.

Example payoffs of empirical learning technology research include improvements in work efficiency, sustained motivation to learn, enhanced individual sensory & cognitive capabilities, and more.

In order to help achieve significant learning performance enhancements, The Juxtopia Group conducts research in perfecting human machine interfaces, neuromorphic engineering, “intelligent” learning environments, and improving methods to measure human learning performance from the molecular level to external environmental variables.