RAPID: DRL AI: Scaffolding Automated Feedback for Teachers

虽然人工智能（AI）有可能改善科学、技术、工程和数学（STEM）的教学实践和学生的整体课堂体验，但更好地了解教师如何在课堂上更容易地适应人工智能至关重要。特别是，支持在资源贫乏的学校采用人工智能驱动的工具对于解决教育不公平问题至关重要。该RAPID项目解决了促进人工智能技术融入学校的迫切需求，以最大限度地提高效益，同时减轻教师的时间负担。具体来说，该项目的目标是了解教学教练如何实施人工智能教师反馈工具，利用这些工具的优势（成本效益、可扩展性、可定制性、基于数据和隐私），并减轻采用的技术和时间障碍。该项目的研究成果和产品将支持专业学习组织以及对自动反馈感兴趣的地区教练和教师，并有可能显著提高各类机构的教学质量。这项时间敏感的研究将包括采访高技能的教练，通过利用与两个教师专业学习项目的现有合作来开发脚手架资源。该项目与4-8年级数学教室的教练和老师合作，这些教室有大量边缘化学生，该项目将设计可通用的教练周期和会话例程，利用自动反馈的信息，同时为不同的教练模式、不同的教练背景和不同技术天赋的教师设计。这项研究包括一个访谈阶段、一个指导周期和惯例的设计阶段和一个试点阶段，其中有重复的余地，并强调结果的传播。总体而言，该研究将提供有关如何将人工智能驱动的反馈整合到教师培训中的见解，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以可扩展的方式在现实世界的教育环境中提高教师效率和学生学习。这项建议是在回复致同事信（DCL）后收到的：快速加速正式和非正式环境中K-12教育中的人工智能研究（NSF 23-097），由学生和教师创新技术体验（ITEST）项目资助，该项目支持建立对实践、项目要素、背景和过程的理解，有助于提高学生对科学、技术、工程和数学（STEM）以及信息和通信技术（ICT）职业的知识和兴趣。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.