RAPID: DRL AI: Scaffolding Automated Feedback for Teachers

RAPID：DRL AI：为教师提供自动反馈的脚手架

• 批准号: 2337772
• 负责人: Dorottya Demszky
• 金额: $ 20万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2337772&HistoricalAwards=false
• 关键词: RAPID; DRL; AI; Scaffolding; Automated

While Artificial intelligence (AI) has the potential to improve both science, technology, engineering and mathematics (STEM) teaching practice and students' overall classroom experiences, it is critical to better understand how teachers can more easily adapt it within their classrooms. In particular, supporting AI-driven tool adoption in resource-poor schools is crucial to address educational inequities. This RAPID project addresses an urgent need to facilitate integration of AI technologies into schools to maximize benefits while reducing the burden on teachers’ time. Specifically, the goal of this project is to understand how instructional coaches can implement AI teacher feedback tools, leveraging the advantages of such tools (cost effectiveness, scalability, customizability, data-based and privacy) and mitigating technical and time barriers to adoption. The findings and products of this project will support professional learning organizations as well as district-based coaches and teachers interested in automated feedback, and has the potential to significantly increase the quality of instruction at various types of institutions. The time-sensitive research will involve interviewing highly-skilled coaches to develop scaffolding resources by leveraging existing collaborations with two teacher professional learning programs. Working with coaches and teachers who serve grade 4-8 math classrooms with a large percentage of marginalized students, the project will design generalizable coaching cycles and conversational routines that take advantage of information from automated feedback, while designing for different coaching models, different coaching contexts, and teachers with varying aptitudes for technology. The study incorporates an interview phase, a design phase for coaching cycles and routines, and a pilot phase, with room for iteration and emphasis on dissemination of the findings. Overall, the study will provide insights into how AI-driven feedback can be integrated into teacher coaching, contributing to knowledge about the challenges and opportunities of implementing AI within existing instructional processes. Ultimately, this project will help uncover how AI can be harnessed to enhance teacher effectiveness and student learning in real-world educational settings in a scalable way. This proposal was received in response to the Dear Colleague Letter (DCL): Rapidly Accelerating Research on Artificial Intelligence in K-12 Education in Formal and Informal Settings (NSF 23-097) and funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.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.

虽然人工智能 (AI) 具有改善科学、技术、工程和数学 (STEM) 教学实践和学生整体课堂体验的潜力，但更好地了解教师如何更轻松地将其应用到课堂中至关重要。支持在资源匮乏的学校采用人工智能驱动的工具对于解决教育不平等问题至关重要，该项目解决了促进人工智能技术融入学校的迫切需要，以最大限度地提高效益，同时减轻教师的时间负担。这个项目的目的是了解如何教学教练可以实施人工智能教师反馈工具，利用此类工具的优势（成本效益、可扩展性、可定制性、基于数据和隐私）并减少采用的技术和时间障碍。该项目的研究结果和产品将为专业学习组织提供支持。以及对自动反馈感兴趣的地区教练和教师，并且有可能显着提高各种类型机构的教学质量。这项时间敏感的研究将涉及采访高技能教练，以利用现有的脚手架资源。与两位老师的合作该项目将与为 4-8 年级数学课堂提供大量边缘化学生的教练和教师合作，设计通用的辅导周期和对话例程，利用自动反馈的信息，同时设计不同的辅导模式。 、不同的辅导环境以及具有不同技术能力的教师该研究包括访谈阶段、辅导周期和例程的设计阶段以及试点阶段，并有迭代的空间并强调研究结果的传播。将提供见解如何将人工智能驱动的反馈整合到教师辅导中，有助于了解在现有教学过程中实施人工智能的挑战和机遇，最终，该项目将有助于揭示如何利用人工智能来提高教师的效率和学生的实际学习。该提案是为了回应《亲爱的同事信》(DCL)：在正式和非正式环境中快速加速 K-12 教育中的人工智能研究 (NSF 23-097) 并提供资助。通过学生和教师创新技术体验 (ITEST) 计划，该计划支持旨在加深对实践、计划要素、背景和贡献过程的理解的项目，以增加学生对科学、技术、工程和数学 (STEM) 的知识和兴趣，以及该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。