Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the “big data” revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.

This March, IES Director Mark Schneider released a blog in which he discussed exploring a partnership with the National Science Foundation (NSF) to encourage scientists with expertise in AI and related fields to address the important post-pandemic need for accelerating learning. IES is now excited to announce our resulting participation in NSF's National Artificial Intelligence (AI) Research Institutes--Accelerating Research, Transforming Society, and Growing the American Workforce solicitation. In this blog, we describe this new funding opportunity, provide examples of existing NCSER-funded research in this area, and highlight the potential for such research to further improve outcomes for learners with disabilities. With funding from the American Rescue Plan, NCSER plans to support research under Theme 6, Track B: AI-Augmented Learning for Individuals with Disabilities. Proposals must discuss how the work will respond to the needs of learners with or at risk for a disability in an area where the COVID-19 pandemic has further widened existing gaps and/or resulted in decreased access and opportunities for students with disabilities to learn and receive support services.

Care.Coach, a Silicon Valley healthcare startup, disclosed today details of a rapid expansion of its virtual avatar program for health plans to combat COVID-19's exacerbation of the loneliness epidemic in the United States. The company, founded in 2012 by Massachusetts Institute of Technology (MIT) graduate Victor Wang, develops virtual avatars that provide companionship to individuals who live with complex health conditions and chronic loneliness. A large body of published research demonstrates that the feeling of loneliness increases morbidity and mortality; in a time of social distancing, self-isolation, and quarantine, loneliness has become a clear public health crisis. Care.Coach is rising to the challenge by providing its customers with as many avatars as are needed to support these at-risk populations. For the past 8 years, Care.Coach has been working with care providers to help those populations which possess "outsized risk", focusing on older adults with psychosocial risks such as loneliness, depression, and anxiety, alongside medical risks such as diabetes, hypertension, COPD, heart failure, and medication non-adherence.

There is a compelling need to develop tools and strategies for people with intellectual and developmental disabilities (I/DD) in order to facilitate independence, self sufficiency, and address poor employment outcomes in adulthood. Through use of augmented reality (AR) and machine learning methods, we create an intelligent, contextually aware instructional system for persons with I/DD. We present results that demonstrate our system can be used independently by students with I/DD to quickly and easily acquire the skills required for performance of three relevant vocational tasks.