Summary
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Language delays are estimated to affect 5-8% of preschool children (US Preventive Task Force, 2013) and are prevalent among children with autism spectrum disorder (ASD), hearing loss (HL), and developmental delays (DD). 48% of the nearly half a million preschool children identified with disabilities or delays (including ASD and HL) attend regular preschool classrooms at least 10 hours per week (U.S. OSEP, 2023). Although children receiving preschool special education services make gains on some pre-academic measures, most do not “catch up” to their typically developing peers (Blackorby, 2010; Elbaum, 2020). These results underscore the urgency of identifying predictors of children’s classroom behavior that are associated with positive developmental outcomes.
This team will adopt a holistic approach, capturing some of the complexities inherent to interaction in real-world environments. This proposal brings together an interdisciplinary team from Computer Science, Psychology, and Physics at the UM College of Arts and Sciences. This project proposes multiple multimodal AI approaches to analyze spatial, audio, and demographic data collected in a classroom environment which includes children with and without developmental disabilities and delays. |
Team
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Vanessa Aguiar-Pulido (Computer Science), Lynn Perry (Psychology), Daniel Messinger (Psychology), Chaoming Song (Physics)
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