ABSTRACT

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future.

Neurodiversity is an emerging concept through which certain neurological differences?Autism, Attention Deficit Hyperactivity Disorder, Dyslexia, and others?are considered a natural part of human neurocognitive variation, associated not only with impairments but also with unique strengths. Indeed, many neurodiverse people have capabilities that are in high demand across many sectors. Yet, while some 70,000 Americans with autism enter adulthood every year, currently 85% of them will be unemployed or underemployed relative to their skill levels, representing a cost to the United States of $175 billion annually. Thus, optimizing workforce engagement for individuals with autism holds the potential to transform great cost into great value. This National Science Foundation Convergence Accelerator (C-Accel) award to Vanderbilt University will address this grand challenge by bringing together cutting-edge Artificial Intelligence (AI) innovations with transdisciplinary expertise?spanning engineering and computer science to organizational psychology, clinical translation, and implementation science?to create a suite of commercially viable technologies that integrate AI within virtual environments, robotic systems, human-human interactions, and novel assessment tools. These technologies will be created using input from stakeholders, including employers of individuals with autism, companies that develop technological products to help employment, state vocational and rehabilitation services that provide job training, and advocacy groups that provide guidance regarding community needs. The technologies will be transitioned to practice through deployment with private- and public-sector partners, together with analysis using implementation science to ensure long-term sustainability and the broadest impact.

This C-Accel Phase II program will advance the scientific and technological methodologies of the projects initiated in Phase I that are designed to create a pipeline to employment for people with autism. Specifically, the suite of tools to be developed include: (1) Visual and Cognitive AI Tools to Assess Autistic Talent; (2) Virtual Reality (VR)-based Simulator for Improving Job-Interview Skills; (3) Collaborative Virtual Environments with Embedded Intelligent Agent for Social Interaction Assessment and Support; (4) Social Robotic System to Assess and Train Tolerance to Interruption; and (5) Computer Vision Tools to Measure and Improve Non-verbal Communication. Across these projects, we will make fundamental scientific and technological advancements in: (i) data-driven visual AI for innovative assessment tools to identify strengths, talents, and job-relevant skills, as well as employer-identified work needs; (ii) novel VR-based platform for job interview training that utilizes real-time closed-loop multimodal affective computing for stress and attention recognition; (iii) a collaborative virtual environment that create new skill estimation algorithms and a peer-based learning paradigm mediated by an AI agent; (iv) a home-based skill assessment and training systems using socially assistive robotics; and (v) novel computer-vision and deep learning methods and algorithms to assess real-world generalization of nonverbal social communication. The project?s intellectual property plan includes advancing each of these technologies from prototype to minimum viable product (MVP) stage and into commercial use through licensing agreements within the two-year project period. Through Vanderbilt University?s Frist Center for Autism & Innovation, graduate students and neurodiverse interns will participate in all aspects of the C-Accel research and development efforts.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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