Supporting Inclusive Participation in the Symposium on Educational Advances in Artificial Intelligence

支持包容性参与人工智能教育进展研讨会

基本信息

  • 批准号:
    1901575
  • 负责人:
  • 金额:
    $ 2.73万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2019
  • 资助国家:
    美国
  • 起止时间:
    2019-01-01 至 2020-12-31
  • 项目状态:
    已结题

项目摘要

The integration of artificial intelligence (AI) into everyday life is increasing. This creates a parallel increase in the difficulty and importance of related discussions related to AI research, policy, and public debate. Broad and informed participation in these discussions by a diverse community is critical to the nation's future. This project supports the engagement of AI educators from a diverse set of backgrounds to participate in the Symposium on Educational Advances in Artificial Intelligence (EAAI) in 2019 and 2020. EAAI provides a venue for AI researchers and educators to discuss pedagogical issues and share resources related to teaching and using AI in education across a variety of curricular levels, from K-12 through postgraduate training. Increased diversity in EAAI participation has the potential to broaden the pipeline of AI professionals as well as the types of discussions surrounding the development of AI technologies, including those of ethical implications and unintended consequences. The symposium is co-located with the Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence. This is one of the premier conferences on AI, drawing wide domestic and international participation. Co-location allows EAAI participants to engage in discussions of both cutting-edge disciplinary research and state-of-the-art methods of educational delivery and assessment. This project addresses the need to develop a diverse set of faculty to educate the next generation of AI researchers, practitioners, and policy makers. The goal of this effort is to increase understanding of how to engage a more demographically diverse pool of AI educators. Support for participation in EAAI over two years will be provided to ten students, ten current higher education faculty and secondary school educators, ten new or future educators, and four invited speakers. Priority for support will be given to first authors/presenters, paper co-authors, Birds-of-a-Feather undergraduate participants, and poster authors. The Selection Committee will be composed of the EAAI co-chairs and track chairs. Recruitment will target women and other groups underrepresented in the AI community, as well as late-stage graduate students or early-stage faculty. This last group, the New or Future Educators, will be invited to assume future EAAI leadership positions. This project aims to collect, analyze, and disseminate data on the efficacy of recruitment venues for achieving a demographically diverse EAAI community.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)越来越多地融入日常生活。 这使得与人工智能研究、政策和公共辩论相关的讨论的难度和重要性同时增加。多元化社区广泛、知情地参与这些讨论对于国家的未来至关重要。该项目支持来自不同背景的人工智能教育工作者参加 2019 年和 2020 年人工智能教育进展研讨会 (EAAI)。EAAI 为人工智能研究人员和教育工作者提供了一个场所,讨论教学问题,并共享与从 K-12 到研究生培训等各种课程级别的教学和使用人工智能相关的资源。 EAAI 参与多样性的增加有可能扩大人工智能专业人员的渠道以及围绕人工智能技术发展的讨论类型,包括道德影响和意外后果。本次研讨会与人工智能促进会人工智能大会同期举办。这是人工智能领域最重要的会议之一,吸引了国内外的广泛参与。同一地点允许 EAAI 参与者参与前沿学科研究和最先进的教育交付和评估方法的讨论。该项目满足了培养多元化师资队伍的需求,以教育下一代人工智能研究人员、从业者和政策制定者。这项工作的目标是加深对如何吸引人口结构更加多样化的人工智能教育工作者的了解。将为 10 名学生、10 名现任高等教育教师和中学教育工作者、10 名新任或未来的教育工作者以及 4 名受邀演讲者提供两年多参与 EAAI 的支持。将优先支持第一作者/演讲者、论文合著者、“羽毛之鸟”本科生参与者和海报作者。评选委员会将由 EAAI 联合主席和分会主席组成。招聘对象将是女性和人工智能社区中代表性不足的其他群体,以及后期研究生或早期教师。最后一个群体,即新的或未来的教育工作者,将被邀请担任未来 EAAI 的领导职务。该项目旨在收集、分析和传播关于招募场所有效性的数据,以实现人口多元化的 EAAI 社区。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力优点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

期刊论文数量(0)
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科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Nate Derbinsky其他文献

Functional Interactions Between Memory and Recognition Judgments
记忆与识别判断之间的功能相互作用
  • DOI:
    10.1609/aaai.v26i1.8150
  • 发表时间:
    2012
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Justin Li;Nate Derbinsky;J. Laird
  • 通讯作者:
    J. Laird
Continual and Real-time Learning for Modeling Combat Identification in a Tactical Environment
战术环境中战斗识别建模的持续实时学习
  • DOI:
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ying Zhao;Nate Derbinsky;Lydia Y. Wong;Jonah Sonnenshein;Tony Kendall
  • 通讯作者:
    Tony Kendall
Competence-preserving retention of learned knowledge in soar's working and procedural memories
在 soar 的工作和程序记忆中保留所学知识的能力保留
  • DOI:
    10.1037/e557512013-001
  • 发表时间:
    2012
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Nate Derbinsky;J. Laird
  • 通讯作者:
    J. Laird
A Multi-Domain Evaluation of Scaling in a General Episodic Memory
一般情景记忆缩放的多领域评估
Computationally efficient forgetting via base-level activation
通过基础级激活实现计算高效的遗忘
  • DOI:
    10.1037/e557102013-030
  • 发表时间:
    2012
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Nate Derbinsky;J. Laird
  • 通讯作者:
    J. Laird

Nate Derbinsky的其他文献

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