RAPID: Responsible, Ethical, and Effective Acceptable Use Policies for the Integration of Generative AI in US School Districts and Beyond

RAPID:在美国学区及其他地区集成生成式人工智能的负责任、道德和有效的可接受使用政策

基本信息

  • 批准号:
    2334525
  • 负责人:
  • 金额:
    $ 17.73万
  • 依托单位:
  • 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:
    2023-09-15 至 2024-08-31
  • 项目状态:
    已结题

项目摘要

The rapidly evolving space of artificial intelligence (AI) is requiring school and district leaders to make sense of how emerging technology applications, including those that use generative AI (GenAI), are being integrated in schools and districts across the United States. Much uncertainty exists about what GenAI is, how it works, and what the implications are for students, families, educators, and the broader school community. School and district leaders have shared challenges that they are facing regarding the use of AI for teaching and learning, including concerns around issues of privacy, data security, and bias. They are also concerned about existing inequities in accessing digital technologies and tools, and that this disparity could present further structural barriers for students and communities. To address the need for policies, guidelines, and guardrails, this project will recruit and convene a GenAI Working Group made up of school and district leaders that represent diverse identities and district demographics.The GenAI Working Group, in collaboration with a Digital Promise team and subject matter experts, will work to answer the following research questions: (1) What tensions do leaders experience when adapting acceptable use policies (AUPs) for emerging technologies such as GenAI? (2) What do acceptable use policies that are "ethical, responsible, and effective" look like? (3) How can districts develop policies that allow for learning to advance while protecting and centering human agency? This project will be both informative to the research field and have direct broad impacts via AUPs for districts and schools. The GenAI Working Group will: (1) write, adapt, and share a set of sample GenAI AUPs for a range of district contexts; (2) work with others at their districts to write and share their own district's AUPs for the responsible, ethical, and effective integration of GenAI; and (3) participate in a final public webinar where they will share what they learned about GenAI and their policies. 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.
人工智能(AI)的快速发展要求学校和地区领导者了解新兴技术应用程序(包括使用生成AI(GenAI)的应用程序)如何整合到美国各地的学校和地区。关于GenAI是什么,它是如何工作的,以及它对学生,家庭,教育工作者和更广泛的学校社区的影响存在很大的不确定性。学校和地区领导人分享了他们在使用人工智能进行教学和学习方面面临的挑战,包括对隐私,数据安全和偏见问题的担忧。他们还对目前在获取数字技术和工具方面存在的不平等现象表示关切,这种不平等可能给学生和社区带来进一步的结构性障碍。为了满足对政策、指导方针和护栏的需求,该项目将招募并召集一个GenAI工作组,该工作组由代表不同身份和地区人口的学校和地区领导人组成。GenAI工作组将与数字承诺团队和主题专家合作,致力于回答以下研究问题:(1)在为GenAI等新兴技术调整可接受使用政策(AUP)时,领导者会经历哪些紧张局势?(2)“合乎道德、负责任和有效”的可接受使用政策是什么样的?(3)地区应如何发展政策,让学习得以提升,同时保护并集中人类的能动性?该项目将为研究领域提供信息,并通过地区和学校的AUP产生直接的广泛影响。GenAI工作组将:(1)编写、改编和分享一套适用于一系列地区背景的GenAI AUP样本;(2)与所在地区的其他人合作,编写和分享他们自己地区的AUP,以实现GenAI的负责任、道德和有效整合;(3)参加最终的公共网络研讨会,分享他们对GenAI及其政策的了解。本提案是对亲爱的同事信(DCL)的回应:在正式和非正式环境中快速加速人工智能在K-12教育中的研究(NSF 23-097),并由学生和教师创新技术经验(ITEST)计划资助,该计划支持建立对实践,计划要素,有助于增加学生对科学,技术,工程,信息和通信技术(ICT)该奖项反映了NSF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。

项目成果

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Patricia Ruiz其他文献

Mobile Networks Simulation
移动网络模拟
  • DOI:
    10.1002/9781118833209.ch4
  • 发表时间:
    2014
  • 期刊:
  • 影响因子:
    0
  • 作者:
    B. Dorronsoro;Patricia Ruiz;Grégoire Danoy;Yoann Pigné;P. Bouvry
  • 通讯作者:
    P. Bouvry
Urinary cystatin C and N-acetyl-beta-D-glucosaminidase (NAG) as early biomarkers for renal disease in dogs with leishmaniosis.
尿胱抑素 C 和 N-乙酰基-β-D-氨基葡萄糖苷酶 (NAG) 作为利什曼病犬肾脏疾病的早期生物标志物。
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    2.6
  • 作者:
    Patricia Ruiz;Ángela Durán;F. J. Duque;Mario Alberto González;José Ignacio Cristóbal;Paloma Nicolás;E. Pérez;B. Macías;R. Barrera
  • 通讯作者:
    R. Barrera
Efficient conditional and promoter-specific in vivo expression of cDNAs of choice by taking advantage of recombinase-mediated cassette exchange using FlEx gene traps
通过使用 FlEx 基因陷阱,利用重组酶介导的盒式交换,对所选 cDNA 进行有效的条件性和启动子特异性体内表达
  • DOI:
  • 发表时间:
    2010
  • 期刊:
  • 影响因子:
    14.9
  • 作者:
    L. Schebelle;C. Wolf;Carola Stribl;T. Javaheri;F. Schnütgen;A. Ettinger;Z. Ivics;Jens Hansen;Patricia Ruiz;H. von Melchner;W. Wurst;T. Floss
  • 通讯作者:
    T. Floss
Intelligent Electric Drive Management for Plug-in Hybrid Buses
插电式混合动力客车的智能电力驱动管理
  • DOI:
    10.1007/978-3-030-41913-4_8
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Patricia Ruiz;Aarón Arias;R. Massobrio;Juan Carlos de la Torre;M. Seredynski;B. Dorronsoro
  • 通讯作者:
    B. Dorronsoro
Optimization and Performance Analysis of the AEDB Broadcasting Algorithm
AEDB广播算法优化及性能分析

Patricia Ruiz的其他文献

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