SBIR Phase I: Mia Learning Independent Reading Choice Support System

这个小企业创新研究第一阶段项目将培养孩子们阅读的动机，并帮助他们阅读最适合他们个人兴趣、目的和能力的书籍。尽管强有力的研究表明，阅读的内在动机和印刷书籍的所有权极大地影响了阅读成绩，但现有的教育软件产品很少能解决这些问题。该项目将开发一款语音聊天机器人应用程序，小学生将在课堂上使用它来接受个性化的图书推荐和选择好的指导。相关的图书订阅服务将允许孩子们拥有他们选择的图书，包括通过与非营利性组织的合作在非常低收入的学校。这些产品加在一起，将进入美国2-5年级儿童读物和识字教育软件市场，这一市场的年收入将超过14亿美元。不同于大多数其他“个性化”或“自适应”的学习系统，这款应用将使用机器学习和人工智能来增加学生和教师的代理权。它将专注于帮助学生提高自己做出选择的能力，而不是为他们做出那些选择。这个项目的智力价值在于它创新地结合了推荐系统和教学代理，同时帮助学生快速、良好地完成一项真正的任务(选择要独立阅读的书籍)，同时还教会他们在多次表演中越来越有效和独立地完成任务。语音对话界面将通过苦苦挣扎的读者可以获得的情绪激动人心的叙事体验，提供这种结合的任务支持和指导。这项研究将产生该系统的现场测试原型，构建一个领域模型，创作对话内容，开发机器学习技术，并通过可用性和小学课堂的试点测试反复改进该系统。技术挑战包括在自然的课堂环境中调整自动语音识别，克服冷启动问题以生成高质量的初始建议，以及支持在结构不良的领域内获得认知和元认知技能，在该领域中，对成功表现的衡量与学生身份和社会背景有着复杂的依赖关系。

This Small Business Innovation Research Phase I project will build children's motivation to read and help them access books best suited to their individual interests, purposes, and abilities. Even though robust research demonstrates both intrinsic motivation to read and print book ownership strongly shape reading achievement, few existing educational software products address them. The project will develop a voice chatbot app elementary students will use in class to receive personalized book recommendations and coaching on choosing well. An associated book subscription service will allow kids to own books they choose, including in very low income schools through partnerships with non-profits. Together, these offerings will tap a combined U.S. children's book and literacy educational software market for grades 2-5 that tops $1.4 billion dollars annually. Unlike most other "personalized" or "adaptive" learning systems, the app will use machine learning and artificial intelligence to increase the agency of students and teachers. It will focus on helping students improve their ability to make their own choices rather than making those choices for them. The intellectual merit of this project lies in its innovative combination of a recommender system and a pedagogical agent to simultaneously assist students in completing an authentic task (choosing books to read independently) quickly and well while also teaching them to complete the task increasingly effectively and independently over multiple performances. The voice conversational interface will provide this combined task support and coaching through an emotionally engaging narrative experience accessible to struggling readers. The research will yield a field-tested prototype of the system, constructing a domain model, authoring conversational content, developing machine learning technology, and iteratively improving the system through usability and pilot testing in elementary school classrooms. Technical challenges include tuning automated voice recognition in naturalistic classroom environments, overcoming the cold start problem to generate high quality initial recommendations, and supporting acquisition of both cognitive and metacognitive skills within an ill-structured domain where measurement of successful performance has complex dependencies with student identity and social context.