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

该研究项目解决了国家迫切需要大幅度扩大辅导劳动力，这主要是由准专业人员有限的正规培训，作为教育工作者。人类辅导已多次被证明对学生的学习产生巨大影响，现在正准备成为日常学校教育的核心结构部分。导师可以发挥重要作用，解决日益扩大的成绩和机会差距，以及在照顾学生的社会情感需求增加。随着辅导工作队伍的扩大，新技术和专业学习模式对于维护和加强辅导对学生的学习益处至关重要，包括来自历史上被边缘化群体和社会经济背景较低的学生。该项目将为在职培训开发一个人工智能增强的专业支持模型，该模型以个性化，数据驱动的反馈为中心，以增强导师的实践及其专业成长。该项目将为教师能力建设提供重要的新知识，以向所有学生提供高质量的教学，并将辅导作为进入教育行业的途径，可能有助于解决合格教师严重短缺的问题。该项目汇集了来自科罗拉多大学博尔德分校的跨学科研究人员团队，与来自佐贺教育的实践伙伴合作。前一个小组在语音和语言处理、人机交互、数学教育、教师学习和辅导、教育政策和行为科学的深度学习方面拥有专业知识。补充这些观点，后一组提供了丰富的经验，在开发，组织和支持数学辅导计划的标题我公立学校。这项研究的具体工作背景将是一个现有的校内，数学辅导计划提供的佐贺教育。该项目将开发和研究一个创新的辅导分析系统，沿着一个工具包，支持教师在日常工作中学习有效的教学技能。该系统将通过提供关于辅导员与学生合作时使用的话语策略的近实时反馈，提高辅导员的教学和关系技能。更具体地说，深度学习模型将用于自动分析教师与学生的对话，并识别重要的话语事件。多模式反馈界面将为教师提供与每个辅导课程相关的个性化信息。这种多层次的方法还将包括支持导师和监督教练在专业学习课程期间使用反馈进行反思注意和目标设定的资源。研究将调查这种模式在多大程度上改变了工作性质（辅导互动和实践），并导致工人的专业化（导师效能和工作参与度）。该项目得到了人类-技术前沿跨部门计划工作未来的资助，以促进对相互依存的人类-技术的更深入的基本理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的评估，被认为值得支持。影响审查标准。

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.