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）旨在通过开发人工智能（AI）工具和方法来支持有效学习的三个关键非认知技能，从根本上重新构建教育技术与学习者的互动方式：持久性，学术弹性和协作。新一代的系统将从根本上更好地响应学习者的需求，行为和发展，并旨在支持整个学习者，超越以学科为中心的成就。受启发的研究将侧重于儿童如何交流STEM内容，他们如何学会通过具有挑战性的工作坚持下去，以及教师如何支持和促进非认知技能的发展。由此产生的基于人工智能的工具将被整合到课堂中，使教师能够以更适合发展的方式支持学习者。这项工作将生成一组丰富的数据，记录学习者与教育技术、彼此和教师的互动，使研究人员能够研究学习者随着时间的推移和不同STEM活动的成长。研究和推广活动将利用INVITE K-12合作伙伴网络，覆盖8个州的24个学区和非营利组织的96，000名学习者。该研究所将提供包容性项目，以支持不同学生参与研究体验，教育AI本科课程以及教师专业发展项目。研究所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方面追求基础AI进步，以便随着时间的推移和跨领域的非认知技能发展进行评估和建模。它将围绕三个相互关联的环节：（1）收集、分析和共享新的数据集，以实现公平、稳健的机器学习和自然语言理解;（2）构建新颖、稳健的方法来理解学习者行为，并建立持久、集成的学习者模型，其中包括对非认知技能的评估;（3）开发新的包容性STEM学习环境，与具有社会意识的教学代理人进行自然和适应性互动。符合真实的数据和模拟学习者的可解释生成模型将使人类学习的新发现和假设成为可能。受启发的研究将推进STEM学习过程中非认知技能获取的科学，并揭示人工智能系统在历史上忽视的学习相关方面。该研究所将成为研究、教育能力建设的纽带，并扩大人工智能和全民教育交叉领域的参与，为广泛的利益相关者服务。具体而言，该研究所将（1）制作一个多模式数据集数据库，供其他研究人员使用，（2）提供开源工具和机会，以开发有关创新的人工智能教育系统的使用，控制和影响的知识，以及（3）积极建立未来科学家和工程师的多元化劳动力，以设计，实施和部署下一代人工智能教育系统。美国教育部教育科学研究所的国家教育研究中心与NSF合作，为该研究所提供资金。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

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

AnaDE1.0: A Novel Data Set for Benchmarking Analogy Detection and Extraction

• 发表时间: 2024
• 期刊: Journal of Ambient Intelligence and Humanized Computing
• 作者: B. Bhavya;Shradha Sehgal;Jinjun Xiong;ChengXiang Zhai