AI Institute for Engaged Learning

人工智能参与学习研究所

• 批准号: 2112635
• 负责人: James Lester
• 金额: $ 1999.63万
• 依托单位: North Carolina State University
• 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: 2021-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112635&HistoricalAwards=false
• 关键词: AI; Institute; Engaged; Learning

Emerging breakthroughs in AI create significant and timely opportunities for accelerating innovation relevant to pressing national challenges in STEM education. Advances in AI are enabling new levels of interactivity, engagement, and inclusion. Driven by a vision in which AI supports and extends the intelligence of teachers and learners, the NSF AI Institute for Engaged Learning will design, develop, and investigate AI-driven narrative learning environments that create engaging story-based, collaborative problem-solving experiences. The Institute will conduct foundational AI research in natural language processing, computer vision, and machine learning, as well as in AI ethics. Building on these foundational advances, the Institute will conduct use-inspired AI research on narrative learning environments with rich AI-driven virtual agents and powerful multimodal sensing capabilities to understand how students learn and collaborate in rich story-based problem scenarios. The Institute will provide a robust infrastructure to support at-scale implementations of AI-driven narrative learning environments. It will create a nexus for distinctive innovations in in-school and out-of-school STEM education. Its research vision will be to empower diverse learners to become the next-generation STEM workforce by creating generative, collaborative AI-driven narrative learning environments that deeply engage learners in schools, at museums, and within their own communities. This vision will be informed by connections with diverse stakeholders to ensure that the Institute’s learning environments are ethically designed and promote diversity, equity, and inclusion. The NSF AI Institute for Engaged Learning will produce transformative advances in STEM teaching and learning by bringing together a team of diverse researchers from four universities (North Carolina State University; Indiana University, University of North Carolina at Chapel Hill; Vanderbilt University), as well as an educational non-profit organization (Digital Promise) which will bring educational practitioners, policy makers, and product developers into the work. The Institute’s partners include a national network of K-12 schools, museums, and non-profit organizations. The Institute will create narrative learning environments that generate interactive stories dynamically tailored to the needs and interests of individual students and small groups and in multiple settings (classrooms, after-school programs, and museums). The Institute’s research has three complementary thrusts, to create: (1) narrative learning environments that generate engaging interactive story-based problem scenarios that elicit rich communication, require coordination, and spark collaborative creativity; (2) embodied conversational agent technologies with multiple modalities for communication (speech, facial expression, gesture, gaze, and posture) to support engaging learning interactions. Embodied conversational agents will be driven by advances in natural language understanding, natural language generation, and computer vision; and, (3) an innovative multimodal learning analytics framework that analyzes parallel streams of multimodal data derived from students’ conversations, gaze, facial expressions, gesture, and posture as they interact with each other, with teachers, and with embodied conversational agents. Woven throughout the Institute’s activities will be a strong focus on ethics, with an emphasis on creating AI-augmented learning that is deeply informed by considerations of fairness, accountability, transparency, trust, and privacy.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.

人工智能领域不断出现的突破为加快创新创造了重要和及时的机会，这些创新与STEM教育领域紧迫的国家挑战相关。人工智能的进步使互动、参与度和包容性达到了新的水平。在人工智能支持和扩展教师和学习者智能的愿景下，NSF人工智能参与式学习研究所将设计、开发和研究人工智能驱动的叙事学习环境，以创造引人入胜的基于故事的协作解决问题体验。该研究所将在自然语言处理、计算机视觉和机器学习以及人工智能伦理方面进行基础人工智能研究。在这些基础性进展的基础上，该研究所将利用丰富的人工智能驱动的虚拟代理和强大的多模式传感能力，在叙事学习环境中进行受使用启发的人工智能研究，以了解学生如何在丰富的基于故事的问题场景中学习和协作。该研究所将提供一个强大的基础设施，以支持人工智能驱动的叙事学习环境的大规模实施。它将为校内和校外STEM教育的独特创新创造一个纽带。其研究愿景将是通过创建生成性、协作性的人工智能驱动的叙事学习环境，使学习者在学校、博物馆和自己的社区中深度参与，从而使不同的学习者能够成为下一代STEM劳动力。这一愿景将通过与不同利益攸关方的联系来实现，以确保研究所的学习环境是合乎道德的设计，并促进多样性、公平和包容性。NSF人工智能参与式学习研究所将通过汇集来自四所大学(北卡罗来纳州立大学、印第安纳大学、北卡罗来纳大学教堂山分校、范德比尔特大学)以及一个教育非营利性组织(Digital Promise)的不同研究人员团队，在STEM教学和学习方面取得革命性进展，该组织将把教育从业者、政策制定者和产品开发人员带入这项工作。该研究所的合作伙伴包括一个由K-12学校、博物馆和非营利性组织组成的全国性网络。该研究所将创建叙事性学习环境，根据学生个人和小组的需求和兴趣，在多个环境(教室、课后计划和博物馆)动态地生成互动故事。该研究所的研究有三个相辅相成的重点，以创造：(1)叙述性学习环境，生成引人入胜的基于故事的互动问题场景，引发丰富的沟通，需要协调，并激发协作创造力；(2)具体化的对话代理技术，具有多种沟通方式(语音、面部表情、手势、凝视和姿势)，以支持参与式学习互动。具体式会话代理将由自然语言理解、自然语言生成和计算机视觉方面的进步推动；以及(3)创新的多模式学习分析框架，分析学生与彼此、与教师和与具体式会话代理交互时的对话、凝视、面部表情、手势和姿势的并行多模式数据流。该研究所的活动将是对伦理的强烈关注，重点是创建人工智能增强的学习，它深受公平、问责、透明度、信任和隐私的考虑。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。

项目成果

Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

• DOI: 10.48550/arxiv.2205.05638
• 发表时间: 2022-05
• 期刊: ArXiv
• 作者: Haokun Liu;Derek Tam;Mohammed Muqeeth;Jay Mohta;Tenghao Huang;Mohit Bansal;Colin Raffel

XAI to Increase the Effectiveness of an Intelligent Pedagogical Agent.

XAI 提高智能教学代理的有效性。

• 发表时间: 2023
• 期刊: In Proceedings of the 23rd ACM International Conference on Intelligent Virtual Agents (IVA 2023
• 作者: Hostetter, J.;Conati, C.;Yang, X.;Abdelshiheed, M.;Barnes, T.;Chi, M.
• 通讯作者: Chi, M.

Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees

• DOI: 10.48550/arxiv.2209.10492
• 发表时间: 2022-09
• 期刊: ArXiv
• 作者: Swarnadeep Saha;Shiyue Zhang;Peter Hase;Mohit Bansal

Unified Coarse-to-Fine Alignment for Video-Text Retrieval

• DOI: 10.1109/iccv51070.2023.00264
• 发表时间: 2023-09
• 期刊: 2023 IEEE/CVF International Conference on Computer Vision (ICCV)
• 作者: Ziyang Wang;Yi-Lin Sung;Feng Cheng;Gedas Bertasius;Mohit Bansal

Seeing Student Engagement in Classroom Video: Affordances of Cognitive and Sociocultural Frameworks

查看学生在课堂视频中的参与：认知和社会文化框架的可供性

• 发表时间: 2023
• 期刊: Proceedings of the International Society for the Learning Sciences Annual Meeting 2023
• 作者: Steinberg, S.;Hmelo-Silver, C.;Zou, X.;Danish, J.;Lester, J.
• 通讯作者: Lester, J.