从学习优化到学习者蓬勃发展:在学生-人工智能团队研究所(iSAT)重新认识人工智能在教育中的应用

IF 2.5 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Ai Magazine Pub Date : 2024-02-21 DOI:10.1002/aaai.12158
Sidney K. D'Mello, Quentin Biddy, Thomas Breideband, Jeffrey Bush, Michael Chang, Arturo Cortez, Jeffrey Flanigan, Peter W. Foltz, Jamie C. Gorman, Leanne Hirshfield, Mon-Lin Monica Ko, Nikhil Krishnaswamy, Rachel Lieber, James Martin, Martha Palmer, William R. Penuel, Thomas Philip, Sadhana Puntambekar, James Pustejovsky, Jason G. Reitman, Tamara Sumner, Michael Tissenbaum, Lyn Walker, Jacob Whitehill
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引用次数: 0

摘要

学生-人工智能团队研究所(iSAT)要解决的基本问题是:如何通过丰富的社会协作学习体验促进所有学生的深度概念学习?我们推进语音、计算机视觉、人机协作、计算机支持的协作学习、扩展性共同设计和扩大参与科学方面的研究,以设计和研究下一代人工智能技术(称为 "人工智能伙伴"),将其嵌入学生协作学习团队,并与教师协调。我们的研究所采用的理论观点旨在通过与 K-12 教育工作者、不同种族的学生、家长和其他社区成员合作,负责任地设计技术、课程和教学法,创造一个广泛参与的规范环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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From learning optimization to learner flourishing: Reimagining AI in Education at the Institute for Student-AI Teaming (iSAT)

The Institute for Student-AI Teaming (iSAT) addresses the foundational question: how to promote deep conceptual learning via rich socio-collaborative learning experiences for all students?—a question that is ripe for AI-based facilitation and has the potential to transform classrooms. We advance research in speech, computer vision, human-agent teaming, computer-supported collaborative learning, expansive co-design, and the science of broadening participation to design and study next generation AI technologies (called AI Partners) embedded in student collaborative learning teams in coordination with teachers. Our institute ascribes to theoretical perspectives that aim to create a normative environment of widespread engagement through responsible design of technology, curriculum, and pedagogy in partnership with K–12 educators, racially diverse students, parents, and other community members.

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来源期刊
Ai Magazine
Ai Magazine 工程技术-计算机:人工智能
CiteScore
3.90
自引率
11.10%
发文量
61
审稿时长
>12 weeks
期刊介绍: AI Magazine publishes original articles that are reasonably self-contained and aimed at a broad spectrum of the AI community. Technical content should be kept to a minimum. In general, the magazine does not publish articles that have been published elsewhere in whole or in part. The magazine welcomes the contribution of articles on the theory and practice of AI as well as general survey articles, tutorial articles on timely topics, conference or symposia or workshop reports, and timely columns on topics of interest to AI scientists.
期刊最新文献
Issue Information AI fairness in practice: Paradigm, challenges, and prospects Toward the confident deployment of real-world reinforcement learning agents Towards robust visual understanding: A paradigm shift in computer vision from recognition to reasoning Efficient and robust sequential decision making algorithms
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