Human Tutoring Augmented by Artificial Intelligence (AI): A Tutoring Analytics and Performance Support Model to Improve the Work and Professional Growth of Future Tutors

人工智能 (AI) 增强的人工辅导：一种辅导分析和绩效支持模型，可改善未来辅导员的工作和专业成长

• 批准号: 2222647
• 负责人: Tamara Sumner
• 金额: $ 180万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-15 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2222647&HistoricalAwards=false
• 关键词: Human; Tutoring; Augmented; Artificial; Intelligence

This research project addresses the urgent national need to dramatically enlarge the tutoring workforce, which is primarily comprised of paraprofessionals with limited formal training as educators. Human tutoring has been repeatedly shown to produce large impacts on student learning, and is now poised to become a core structural part of daily schooling. Tutors can play an important role in addressing widening achievement and opportunity gaps as well as in attending to students’ increased social-emotional needs. As the tutoring workforce expands, new technologies and professional learning models will be critical to preserve and enhance tutoring's learning benefits for students, including students from historically marginalized groups and low socio-economic backgrounds. This project will develop an AI-augmented professional support model for on-the-job training that centers personalized, data-driven feedback in order to enhance tutors’ practices as well as their professional growth. This project will contribute vital new knowledge towards building capacity for tutors to provide high quality instruction to all students and for tutoring to serve as a pathway into education professions, potentially helping to address the dire national shortage of qualified teachers.This project brings together an interdisciplinary team of researchers from the University of Colorado Boulder working with practitioner partners from Saga Education. The former group has expertise in deep learning for speech and language processing, human-computer interaction, mathematics education, teacher learning and coaching, educational policy, and behavioral science. Complementing these perspectives, the latter group offers extensive experience in developing, organizing, and supporting mathematics tutoring programs in Title I public schools. The specific work context for this research will be an existing in-school, mathematics tutoring program provided by Saga Education. The project will develop and study an innovative tutoring analytics system, along with a toolkit that supports the learning of effective pedagogical skills as tutors engage in their everyday work. The system will enhance tutors’ pedagogical and relational skills by providing near-real-time feedback on the discourse strategies they use when working with students. More specifically, deep learning models will be used to automatically analyze tutor-student dialogues and recognize important discursive events. Multimodal feedback interfaces will provide tutors with personalized information related to each tutoring session. This multilayered approach will also include resources that support tutors’ and supervising coaches’ use of the feedback for reflective noticing and goal-setting during professional learning sessions. Research will investigate the extent to which this model changes the nature of work (tutoring interactions and practices) and leads to the professionalization of workers (tutor efficacy and work engagement).This project has been funded by the Future of Work at the Human-Technology Frontier cross-directorate program to promote deeper basic understanding of the interdependent human-technology partnership in work contexts by advancing the design of intelligent work technologies that operate in harmony with human workers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该研究项目涉及大幅度增加辅导劳动力的紧急国家需求，该劳动力主要由专业人士组成，作为教育者的正式培训有限。人类的辅导反复被证明会对学生的学习产生巨大的影响，现在被毒死成为日常教育的核心结构部分。导师可以在解决扩大成就和机会差距以及注意学生增长的社会情感需求方面发挥重要作用。随着辅导员工的扩大，新技术和专业学习模型对于维护和增强辅导对学生的学习优势至关重要，包括历史上边缘化的群体和低社会经济背景的学生。该项目将为在职培训中开发一个AI-Eair的专业支持模型，以个性化数据驱动的反馈为中心，以增强辅导员的实践及其专业成长。该项目将为教师提供高质量教学的能力，为所有学生提供高质量的教学，并为辅导作为进入教育专业的途径，有助于解决该部门国家短缺合格的教师。该项目汇集了与科罗拉多大学大学研究人员的跨学科研究人员，与科罗拉多大学的研究人员与从业者党派的研究人员一起工作。前小组在言语和语言处理，人类计算机互动，数学教育，教师学习和教练，教育政策和行为科学方面具有深入学习方面的专业知识。鉴于这些观点，后一组在标题I公立学校中提供了发展，组织和支持数学辅导计划的丰富经验。这项研究的特定工作环境将是Saga Education提供的现有的校内数学辅导计划。该项目将开发和研究一种创新的辅导分析系统，以及一个工具包，该工具包在导师每天从事他们的日常工作时支持学习有效的教学技能。该系统将通过提供与学生合作时使用的话语策略的近实时反馈，从而提高导师的教学和关系技能。更具体地说，深度学习模型将用于自动分析教师实习对话并认识到重要的讨论事件。多模式反馈接口将为导师提供与每个辅导会话有关的个性化信息。这种多层方法还将包括支持辅导员和监督教练在专业学习课程中使用反思性通知和目标设定的资源。研究将调查该模型改变工作性质（辅导互动和实践）的程度，并导致工人的专业化（辅导效率和工作参与）。该项目由人类技术领域的工作未来提供资金工人。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估值得支持。