AI Institute for Inclusive Intelligent Technologies for Education (INVITE)

AI 普惠智能教育技术研究所 (INVITE)

• 批准号: 2229612
• 负责人: H Chad Lane
• 金额: $ 1999.87万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229612&HistoricalAwards=false
• 关键词: AI; Institute; Inclusive; Intelligent; Technologies

The Institute for Inclusive and Intelligent Technologies for Education (INVITE) seeks to fundamentally reframe how educational technologies interact with learners by developing artificial intelligence (AI) tools and approaches to support three crucial noncognitive skills known to underlie effective learning: persistence, academic resilience, and collaboration. This new generation of systems will be radically more responsive to learner needs, behaviors, and development and be designed to support the whole learner, beyond discipline-focused achievement. Use-inspired research will focus on how children communicate STEM content, how they learn to persist through challenging work, and how teachers support and promote noncognitive skill development. The resultant AI-based tools will be integrated into classrooms to empower teachers to support learners in more developmentally appropriate ways. This work will generate a rich set of data documenting learners’ interactions with educational technologies, each other, and teachers, allowing researchers to study learner growth over time and across different STEM activities. Research and outreach activities will draw from the INVITE K-12 partner network reaching up to 96,000 learners across 24 school districts and nonprofits spanning 8 states. The Institute will offer inclusive programs to support diverse students’ participation in research experiences, undergraduate courses in AI in education, and professional development programs for teachers.Institute research will pursue foundational AI advances in robust and fair machine learning, learner modeling, and natural language understanding to enable assessment and modeling of noncognitive skill development over time and across domains. It will revolve around three interconnected strands: (1) Collect, analyze, and share novel datasets for fair and robust machine learning and natural language understanding; (2) Build novel, robust methods for understanding learner behaviors and persistent, integrated learner models that incorporate assessments of noncognitive skills; (3) Develop new inclusive STEM learning environments that provide natural and adaptive interaction with socially-aware pedagogical agents. Interpretable generative models fit to real data and simulated learners will enable new discoveries and hypotheses about human learning. Use-inspired research will advance the science of noncognitive skill acquisition during STEM learning and uncover relevant contextual aspects of learning historically overlooked by AI systems. The institute will serve as a nexus for building capacity for research, education, and broadening participation in the intersection of AI and Education for All, serving a wide array of stakeholders. Specifically, the Institute will (1) produce a database of multimodal datasets for use by other researchers, (2) provide open source tools and opportunities to develop knowledge about the use, control, and impact of innovative AI-enabled education systems, and (3) actively build a diverse workforce of future scientists and engineers to design, implement, and deploy the next generation of AI-enabled Education for All systems. The National Center for Education Research at the Institute of Education Sciences of the US Department of Education is partnering with NSF to provide funding for the Institute.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.

包容性和智能教育技术研究所（Invite）试图从根本上反映教育技术如何通过开发人工智能（AI）工具（AI）工具（AI）工具和方法来支持支持三种为有效学习的重要的非认知技能：坚持不懈，学术弹性和协作。这一新一代的系统将在从根本上对学习者需求，行为和发展的响应迅速，并旨在支持整个学习者，而不是以纪律为中心的成就。使用启发的研究将集中于儿童如何交流STEM内容，他们如何通过挑战者学习持续存在以及教师如何支持和促进非认知技能发展。最终的基于AI的工具将集成到课堂中，以增强教师以更适当的方式为学习者提供支持。这项工作将产生大量的数据文档学习者与教育技术，彼此以及教师的互动，使研究人员能够随着时间和不同的STEM活动研究学习者的成长。研究和外展活动将从邀请K-12合作伙伴网络中汲取灵感，该网络在24个学区和跨越8个州的非营利组织中最多达到96,000名学习者。该研究所将提供包容性的计划，以支持潜水员学生参与研究经验，教育中的本科课程以及为教师的专业发展计划。基金会的研究将在强大而公平的机器学习，学习者建模和自然语言理解中追求基础AI的进步，以启用非认知技能发展的评估和建模。它将围绕三个相互联系的链：（1）收集，分析和共享新颖的数据集，以提供公平，强大的机器学习和自然语言理解； （2）构建新颖的，可靠的方法，用于了解学习者的行为和持久，综合的学习者模型，以纳入对非认知技能的评估； （3）开发新的包容性STEM学习环境，以与社会意识的教学剂提供自然和适应性的相互作用。可解释的通用模型适合真实数据，模拟学习者将使有关人类学习的新发现和假设。使用启发的研究将在STEM学习过程中推进非认知技能获取的科学，并揭示AI系统历史上学习历史上学习的相关背景方面。该研究所将成为建立研究，教育和扩大参与人工智能和所有人交集的能力的联系，为所有人提供各种利益相关者。具体来说，该研究所将（1）生产一个多模式数据集的数据库供其他研究人员使用，（2）提供了开源工具和机会，以开发有关创新AI-a-Sparbiage教育系统的使用，控制和影响的知识，（（3）积极地为未来的科学家和工程师建立潜水员的劳动力，以设计，实施，实施AI-Systems Assemss Allyssems Ally Systems Assems Ally Systems Ally Systems Ally Systems。美国教育部教育科学研究所的国家教育研究中心正在与NSF合作，为该研究所提供资金。该奖项反映了NSF的法定任务，并通过基金会的知识分子的优点和更广泛的影响来评估NSF的法定任务。

项目成果

How Noisy is Too Noisy? The Impact of Data Noise on Multimodal Recognition of Confusion and Conflict During Collaborative Learning

• DOI: 10.1145/3577190.3614127
• 发表时间: 2023-10
• 期刊: Proceedings of the 25th International Conference on Multimodal Interaction
• 作者: Yingbo Ma;Mehmet Celepkolu;K. Boyer;Collin Lynch;E. Wiebe;Maya Israel

What makes a role model motivating for young girls? The effects of the role model’s growth versus fixed mindsets about ability and interest

是什么让榜样能够激励年轻女孩？

• DOI: 10.1016/j.jecp.2023.105775
• 发表时间: 2024
• 期刊: Journal of Experimental Child Psychology
• 影响因子: 2.6
• 作者: Gladstone, Jessica R.;Tallberg, Molly;Jaxon, Jilana;Cimpian, Andrei
• 通讯作者: Cimpian, Andrei

Computing Self-Efficacy in Undergraduate Students: A Multi-Institutional and Intersectional Analysis

计算本科生的自我效能感：多机构和交叉分析

• DOI: 10.1145/3626252.3630811
• 发表时间: 2024
• 期刊: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V.1
• 作者: Ojha, Vidushi;West, Leah;Lewis, Colleen M.
• 通讯作者: Lewis, Colleen M.

IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions

• DOI: 10.18653/v1/2023.emnlp-main.881
• 发表时间: 2023-12
• 期刊: ArXiv
• 作者: Ziheng Zeng;Kellen Tan Cheng;Srihari Venkat Nanniyur;Jianing Zhou;Suma Bhat

Non-compositional Expression Generation Based on Curriculum Learning and Continual Learning

• DOI: 10.18653/v1/2023.findings-emnlp.286
• 发表时间: 2023
• 作者: Jianing Zhou;Ziheng Zeng;Hongyu Gong;Suma Bhat