APTIMA INC — Department of Defense STTR Phase I: AF20C-TCSO1

Description

Training is costly and time-intensive, yet absolutely essential in nearly every endeavor where human skills drive the enterprise. For example, training a fighter pilot to proficiency costs the USAF nearly $11 million. Ineffective training can result in loss of life, destruction of valuable equipment, and squandered person-hours. Training success can be quantified from the perspective of economics (minimizing cost-per-trainee), time (minimizing time-to-proficiency), person-power (maximizing throughput into the workforce), or performance metrics that vary by domain. Regardless of one’s perspective, the challenge is the same: how can training be optimized to accelerate learning and enhance retention? When instructors must use the same material for all learners, the risk is that some students will not quite grasp the material and are left without the necessary remediation as the content continues to progress on a set timeline. Conversely, more knowledgeable students may be bored and unable to achieve their full potential. Adaptive learning approaches were introduced to counter this “one-size-fits-all” approach and ensure that learners receive the content that is most appropriate for their current knowledge and skill level. However, recent advances in neurophysiological sensing technology have introduced new opportunities for even more rapid discoveries. “Neuroadaptive learning” employs a closed-loop brain-computer interface (BCI) to optimize training by adjusting itself in real time to each individual learner’s cognitive and affective state. Aptima, with our partners at Cognionics, Reflexion, and the University of Chicago, will incorporate cutting-edge cognitive and affective neurophysiological sensing technology, artificial intelligence and machine learning (AI/ML), and the Reflexion interactive learning environment, to develop this first-of-its kind solution. The NeuroAdapt system will integrate real-time training data from Reflexion with neurophysiological measures such as electroencephalography (EEG), heart-rate variability (HRV), galvanic skin response (GSR), and likely others such as functional near-infrared spectroscopy (fNIRS) and eye tracking. A powerful AI/ML engine based on Aptima’s existing platforms will adapt to each individual's performance and neurocognitive/affective state, learning the patterns and pathways that generate optimal learning outcomes, and adjusting the training paradigm to accelerate learning and enhance retention.