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

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
批准号:
2334525
负责人:
Patricia Ruiz
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
项目状态:
已结题
起止时间:
2023-09-15 至 2024-08-31

项目摘要

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中文摘要
翻译
人工智能(AI)领域的快速发展要求学校和学区领导人理解新兴技术应用程序,包括那些使用生成性人工智能(GenAI)的应用程序,是如何在美国各地的学校和学区中整合的。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,仍然存在许多不确定性。学校和学区领导人分享了他们在使用人工智能进行教学方面面临的挑战,包括对隐私、数据安全和偏见问题的担忧。他们还对在获取数字技术和工具方面存在的不平等现象表示关切,并担心这种不平等可能会给学生和社区带来进一步的结构性障碍。为了解决政策、指导方针和护栏的需求,该项目将招募并召集一个由代表不同身份和地区人口的学校和地区领导人组成的GenAI工作组。GenAI工作组将与Digital Promise团队和主题专家合作,努力回答以下研究问题:(1)领导人在为GenAI等新兴技术调整可接受使用政策(AUP)时会遇到什么压力?(2)可接受的使用政策是什么样子的?(3)地区如何制定政策,允许在保护和集中人权机构的同时学习进步?该项目将为研究领域提供信息,并通过AUPS对地区和学校产生直接和广泛的影响。GenAI工作组将:(1)编写、改编和共享一组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,为负责任、合乎道德和有效地整合GenAI编写和分享本地区的AUP;以及(3)参加最后的公开网络研讨会,他们将在会上分享他们对GenAI及其政策的了解。收到这份建议书是为了回应亲爱的同事来信(DCL):在正式和非正式环境中迅速加速对K-12教育中人工智能的研究(NSF 23-097),并由学生和教师创新技术体验(ITEST)计划资助,该计划支持一些项目,这些项目建立对实践、计划元素、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。
英文摘要
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.
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