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

早期学习是为随后的教育和经济成就进行干预的关键时期。研究表明，小学早期的阅读水平可以预测高中毕业率。有效的学前教育干预计划被认为是通过改善就业机会、降低监禁率和减少对社会服务的需求，为社会带来有效的投资回报(ROI)。智能设备和其他数字学习技术的可扩展性和易于部署具有变革性的潜力，可以提高幼儿园准备成果的广为普及和负担得起。尤其是对于无法获得优质学前教育的儿童。然而，现代数字早教工具没有对儿童进行持续评估和建模的AI和机器学习算法-S识字和口语技能，也无法个性化满足儿童个体的多样化需求。这项拟议研究的目标是弥合儿童早期学习对个性化干预的需求与移动设备的普及性和可负担性之间的差距。研究团队建议开发、部署和评估一个能够自动和连续评估、个性化内容适应和人工智能支持的教育指导的系统，以向幼儿提供高质量、可扩展和负担得起的早期识字干预。拟议的研究将通过开发三个创新技术组件，开发一个在移动设备上进行个性化早期学习的智能系统：1.嵌入式学生评估算法，可以从包括音频、面部表情和实时游戏/使用数据在内的各种多模式数据源评估阅读技能。2.个性化的指导算法，使教育内容适应应用程序使用的个人行为模式，以优化个人学习结果。3.感知情感的内容适配和个性化算法，以优化参与度和学习。这些技术将嵌入两个新的互动教育应用程序中。第一个是讲故事和词汇发展。第二个是用来阅读的。两者都使用一般评估数据、实时音频和面部表情识别来评估绩效并优化动机、参与度和学习。这些技术将嵌入行业合作伙伴好奇心学习的移动软件平台，该平台目前为儿童提供早期学习软件，以促进美国幼儿园儿童的早期识字准备。该项目将评估这些技术评估阅读技能、培养词汇和口语的能力，并通过为儿童个性化好奇学习系统的教育内容来提高参与度和学习能力。

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.