AI Institute for Adult Learning and Online Education (ALOE)

人工智能成人学习和在线教育研究所 (ALOE)

基本信息

  • 批准号:
    2112532
  • 负责人:
  • 金额:
    $ 1999.04万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Cooperative Agreement
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-11-01 至 2022-11-30
  • 项目状态:
    已结题

项目摘要

Based on decades of study of human learning, it is known that much of human learning is mediated by others (guided by teachers), is a social/collaborative process, and uses a range of cognitive strategies. Childhood and adult learning lie on a spectrum of cognitive abilities. Two characteristics that distinguish adult learning from childhood learning are first, while much of K-12 learning pertains to closed, well-defined problems with clear answers, adult learning - especially adult learning in the workplace - often addresses open-ended, ill-defined problems that may have no clear answer or that may admit multiple answers. Second, while most K-12 learning is general-purpose and teacher-guided, adult learning (especially in the workplace) is task-specific and self-directed, hence the proliferation of educational resources (often online) in support of adult learning. Georgia Research Alliance (GRA) will establish a National Artificial Intelligence Institute titled “NSF AI Institute for Adult Learning and Online Education (ALOE)”, the goal of which is to make education more equitable through enhanced availability, greater affordability, and enhanced potential for success. Enhanced availability is to be achieved through the use of online educational resources for blended learning; greater affordability is to be accomplished through low-cost virtual teaching assistants that amplify teachers’ reach, while enhanced potential for success is to be achieved through cognitive and social support provided by virtual teaching assistants. The Artificial Intelligence (AI) project aims to serve the national interest through the development of transformative AI-driven models of online adult learning that blend higher and continuing education to radically improve human learning. A comprehensive and well-organized plan is proposed that uses AI simultaneously to transform online adult learning and to drive foundational research in AI. GRA is a 30-year-old private, nonprofit corporation that collaborates with state government, business community, and university system to advance science and technology that generates direct economic benefits. The ALOE AI Institute involves a large interdisciplinary research team that includes two non-profit organizations (Georgia Research Alliance, IMS Global), three industrial companies (Boeing, IBM, Wiley) and seven educational institutions (Arizona State University, Drexel University, Georgia Institute of Technology, Georgia State University, Harvard University, Technical College System of Georgia, University of North Carolina at Greensboro). Additionally, Accenture, the multinational consulting company, is partnering with NSF to provide funding for the Institute.Overall, the goals of the project are consistent with NSF AI Institutes’ vision to advance foundational research, conduct use-inspired research, and grow the next generation of diverse talent by leveraging multiple organizations. With regard to foundational research, major synergistic contributions are anticipated in four areas:(i) cognitively-grounded AI (AI virtual assistants that are grounded in cognitive theories of adult learning such as active learning); (ii) AI-based personalization at scale (collection of learning data from millions of adult learners and development of novel machine learning and natural language processing techniques for analyzing the data); (iii) human-AI Collaboration: development of novel techniques for interactive visualization that enables teachers and learners to build a mutual theory of mind; (iv) responsible AI: discovery of principles for designing sociotechnical systems for online adult education in which AI agents work ethically to benefit humans. With regard to use-inspired research, responsible fundamental AI research grounded in theories of human cognition and learning will be conducted. At least two distinct thrusts are in place: (i) development of AI teaching and learning assistants that enhance cognitive, teacher and social presence in online adult learning to help make it efficient and effective; (ii) learning analytics for personalization of large-scale online learning for adult education. The methodology employed, learning engineering, is an iterative design approach that brings the rigor of engineering to the discipline of education. Beginning with human-centered design of AI technologies, where the human could be a learner, a teacher, or a different stakeholder in the learning process, the process continues with the deployment of AI technologies and collection and analysis of large-scale data about learners and learning. The process then continues to the assessment of learning behaviors and outcomes followed by the refinement of human-centered AI technologies. A detailed plan is provided for assessment of impact on both learning and teaching through a mixed methods approach. Randomized controlled trials will be used to evaluate how the use of AI technologies facilitates and impacts learning. Quasi-experimental studies will be carried out to compare learning effectiveness and efficiency of online versus in-person classes. A plan for evaluation of the process of project execution is to be overseen by an experienced evaluator who will employ a values-engaged, educative approach which seeks to capture the viewpoints, interests, and values of all stakeholders, including those often underrepresented in the evaluation context. The National Artificial Intelligence Institutes Program is a multi-agency effort to establish institute-scale AI research with the potential for long-term payoffs in AI. In addition to advancing foundational research and conducting use-inspired research, the program supports efforts to grow the next generation of AI talent, enhance multidisciplinary AI research, leverage multiple organizations and provide a nexus point for collaborative efforts in AI research and development.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.
根据数十年来对人类学习的研究,众所周知,许多人类学习是由他人介导的(由教师指导),是一个社会/协作过程,并使用了一系列认知策略。童年和成人学习在于认知能力。将成人学习与儿童学习区分开的两个特征是首先,而K-12学习的大部分与封闭的,明确的答案,成人学习 - 尤其是在工作场所的成人学习 - 经常解决可能没有明确答案或可能接受多个答案的开放式,定义不明的问题。其次,尽管大多数K-12学习都是通用和教师指导的,但成人学习(尤其是在工作场所)是特定于任务的和自我指导的,因此教育资源的扩散(通常是在线)以支持成人学习。佐治亚州研究联盟(GRA)将建立一个名为“ NSF AI成人学习与在线教育研究所(芦荟)”的国家人工智能研究所,其目标是通过增强的可用性,更大的可用性,可用性和增强成功的潜力,使教育更加公平。通过使用在线教育资源进行混合学习,可以实现增强的可用性;可以通过放大教师覆盖范围的低成本虚拟教学助理来实现更大的可用性,同时,通过虚拟教学助理提供的认知和社会支持,可以提高成功的潜力。人工智能(AI)项目旨在通过开发变革性的AI驱动的在线成人学习模型来融合更高和继续教育,从而从根本上改善人类学习,以实现国家利益。提出了一项全面且组织良好的计划,该计划仅使用AI仅用于改变在线成人学习并推动AI的基础研究。 GRA是一家拥有30年历史的私人非营利性公司,与州政府,商业社区和大学系统合作,推进了产生直接经济利益的科学技术。芦荟AI研究所涉及一个大型跨学科研究团队,其中包括两个非营利组织(Georgia Research Alliance,IMS Global),三家工业公司(波音公司,IBM,Wiley)和七个教育机构和七个教育机构(亚利桑那州立大学,德雷克斯大学,德雷克斯大学,乔治亚大学,乔治亚大学,乔治亚大学,北部大学,乔治大学,乔治大学,乔治大学,乔治大学,乔治亚大学,乔治亚大学,乔治亚大学,乔治大学,乔治亚大学,乔治。格林斯伯勒)。跨国咨询公司Accenture与NSF合作为研究所提供资金。该项目的目标与NSF AI Institutes的愿景一致,以提高基础研究,进行使用启发性研究,并通过利用多个组织来提高下一代不同的人才。关于基础研究,预计在四个领域将会做出主要的协同贡献:(i)认知基础的AI(基于成人学习的认知理论,例如积极学习的AI虚拟助手); (ii)基于AI的个性化(从数百万的成人学习者那里收集学习数据,以及开发新颖的机器学习和自然语言处理技术,以分析数据); (iii)人类合作:开发用于互动可视化的新技术,使教师和学习者能够建立一个相互的思想理论; (iv)负责人的AI:发现设计社会技术系统的在线成人教育的原则,在该系统中,AI代理在道德上工作以使人类受益。关于使用启发的研究,将进行以人类认知和学习理论为基础的负责任的基础AI研究。至少有两个不同的推力:(i)发展AI教学助理,以增强在线成人学习中的认知,教师和社会存在,以帮助使其有效和有效; (ii)学习分析用于个性化大规模在线学习的成人教育。学习工程的方法是一种迭代设计方法,它将工程学的严格性带入了教育学科。从以人为中心的AI技术设计开始,在学习过程中,人类可以成为学习者,老师或其他利益相关者,该过程持续了AI技术的部署以及收集和分析有关学习者和学习的大型数据。然后,该过程继续评估学习行为和结果,然后进行以人为中心的AI技术的改进。提供了详细的计划,以评估通过混合方法对学习和教学的影响。随机对照试验将用于评估AI技术的使用如何促进和影响学习。将进行准实验研究,以比较在线与面对面课程的学习有效性和效率。评估项目执行过程的计划是由经验丰富的评估者监督的,该评估者将采用一种价值的,有教育性的方法,该方法试图捕获所有利益相关者的观点,利益和价值观,包括在评估环境中经常代表不足的人。国家人工智能机构计划是一项多机构努力,旨在建立研究所规模的AI研究,并有可能在AI中获得长期收益。该计划除了提高基础研究和进行使用启发的研究外,还支持努力发展下一代AI人才,增强多学科的AI研究,利用多个组织,并为AI研究和发展中的协作努力提供了下一个要点。本奖颁奖典礼反映了NSF的法定任务,并通过评估该基金会的智力效果,并以评估委员会的范围进行了评估。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Michael Garn其他文献

Classically efficient regimes in measurement based quantum computation performed using diagonal two qubit gates and cluster measurements
使用对角两个量子位门和簇测量执行基于测量的量子计算的经典有效机制
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    S. Atallah;Michael Garn;Yi Tao;S. Virmani
  • 通讯作者:
    S. Virmani

Michael Garn的其他文献

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{{ truncateString('Michael Garn', 18)}}的其他基金

NSDL Backpack
NSDL背包
  • 批准号:
    1043616
  • 财政年份:
    2010
  • 资助金额:
    $ 1999.04万
  • 项目类别:
    Standard Grant

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中国地方综合科研机构组织优化模型及评价体系研究
  • 批准号:
    79060001
  • 批准年份:
    1990
  • 资助金额:
    2.5 万元
  • 项目类别:
    地区科学基金项目
中国地方综合科研机构发展研究
  • 批准号:
    79060002
  • 批准年份:
    1990
  • 资助金额:
    3.0 万元
  • 项目类别:
    地区科学基金项目

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  • 批准号:
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通过人工智能生成的保真度反馈提高社区心理健康领域 CBT 的质量
  • 批准号:
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  • 财政年份:
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Enhancing the quality of CBT in community mental health through AI-generated fidelity feedback
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The Development and Systematic Evaluation of an AI-Assisted Just-in-Time-Adaptive-Intervention for Improving Child Mental Health
人工智能辅助改善儿童心理健康的即时适应性干预的开发和系统评估
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