PFI:BIC - Development, Deployment and Evaluation of an Intelligent Service System for Personalized Early Literacy Learning Using Mobile Devices

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Early learning is a key time to intervene for subsequent educational and economic achievement. Research shows that reading levels in early primary years can predict high school graduation rates. Effective Pre-K educational intervention programs are recognized as an effective return on investment (ROI) for society via improved employment opportunities, lower incarceration rates, and decreased need for social services. The scalability and ease-of-deployment of smart devices and other digital learning technologies has transformative potential to increase Kindergarten readiness outcomes that is widely accessible and affordable. Especially for children who cannot access quality pre-K education. However, modern digital early educational tools do not possess AI and machine learning algorithms for continuous assessment and modeling of children?s literacy and and oral language skills, nor are they capable of personalization to meet the diverse needs of individual children. The goal of this proposed research is to bridge this gap between the need for personalized interventions for early childhood learning with the ubiquity and affordability of mobile devices. The research team proposes to develop, deploy, and evaluate a system capable of automatic and continuous assessment, personalized content adaptation, and AI-powered educational mentorship to deliver high quality, scalable, and affordable early literacy interventions to young children.The proposed research will develop a smart system for personalized early learning on mobile devices, by developing three innovative technology components: 1. Embedded student assessment algorithms, that can estimate reading skill from diverse, multimodal data sources including audio, facial expression, and real-time gameplay/usage data. 2. Personalized mentoring algorithms that adapt educational content to individual patterns of behavior of app usage to optimize for individual learning outcomes. 3. Affect-aware content adaptation and personalization algorithms to optimize engagement and learning. These technologies shall be embedded within two novel interactive educational apps. The first is for storytelling and vocabulary development. The second is for reading.  Both use general assessment data, real-time audio, and facial expression recognition to estimate performance and to optimize motivation, engagement, and learning. These technologies will be embedded within industry partner Curious Learning's mobile software platform, which currently delivers early learning software for children to promote early literacy readiness of US kindergarten children. The project will evaluate the ability of these technologies to assess reading skill, foster vocabulary and oral language, and to improve engagement and learning by personalizing the Curious Learning system's educational content to children.