Conference: Accelerating the Future of AI and Data-driven Education

会议：加速人工智能和数据驱动教育的未来

• 批准号: 2230697
• 负责人: Beverly Woolf
• 金额: $ 10万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230697&HistoricalAwards=false
• 关键词: Conference; Accelerating; Future; AI; Data

This NSF Convergence Accelerator Workshop will create smart and integrated platforms, devices and processes for education technology. Current educational technologies often do not sufficiently leverage basic research on learning, nor have a culture of continuous improvement, nor meet the needs of diverse learners, nor leverage the growing power of computers. One issue is the disconnected, fragmented, and often closed nature of different sectors: educators, parents, commercial ventures, not-for-profit organizations, stakeholders, researchers, and communities. A concerted effort among diverse stakeholders is needed now to create real and immediate solutions. Significant technology exists for digital instruction. Experts in academia (learning science, AI, human-computer interaction, education, psychology), industry and government will identify barriers and solutions to the delivery of high-quality online education; they will inform best practices in design, generate future development and testing, and leverage technology and new modes of platform design. This workshop will support communities to reason about fruitful near-term approaches for scaling up innovative pedagogies. We will increase the number of trials of new products; test more often and fail faster; identify promising interventions, and evaluate the conditions and circumstances that increase the probability of successful products. The scientific agenda will investigate and augment human learning at large scale in authentic education settings (online, hybrid, and on-the-job). The workshop will establish a framework for new AI, learning science, and education theory and technologies to understand, model, infer, and respond to learning. It will explore new theory, algorithms, big data, and systems that optimize every point in the education process to understand students, organize what they can learn, and optimize how they learn. The workshop will couple use-inspired AI research with foundational AI and learning science research in a virtuous cycle and forge new partnerships among diverse stakeholders as they bridge the divide with novel tools from engineers, makers, technologists, and designers. It will make a laser focus on projects that present well-defined deliverables within 3-years.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.

这个NSF融合加速器研讨会将为教育技术创建智能和集成的平台，设备和流程。目前的教育技术往往不能充分利用学习的基础研究，也没有持续改进的文化，也不能满足不同学习者的需求，也不能利用计算机日益增长的能力。一个问题是不同部门的脱节，分散和往往封闭的性质：教育工作者，家长，商业企业，非营利组织，利益相关者，研究人员和社区。现在需要各利益攸关方作出协调一致的努力，以立即找到真实的解决办法。数字化教学存在重要的技术。学术界（学习科学、人工智能、人机交互、教育、心理学）、工业界和政府的专家将确定提供高质量在线教育的障碍和解决方案;他们将为设计提供最佳实践，产生未来的开发和测试，并利用技术和平台设计的新模式。该研讨会将支持社区对扩大创新性生物学的富有成效的近期方法进行推理。我们将增加新产品的试验数量;更频繁地测试，更快地失败;确定有希望的干预措施，并评估增加产品成功概率的条件和情况。科学议程将在真实的教育环境（在线，混合和在职）中大规模调查和增强人类学习。该研讨会将为新的人工智能，学习科学和教育理论和技术建立一个框架，以理解，建模，推断和响应学习。它将探索新的理论、算法、大数据和系统，优化教育过程中的每一个环节，以了解学生、组织他们能学到的东西并优化他们的学习方式。该研讨会将使用启发的人工智能研究与基础人工智能和学习科学研究结合起来，形成良性循环，并在不同的利益相关者之间建立新的伙伴关系，因为他们用工程师，制造商，技术人员和设计师的新颖工具弥合分歧。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。