Collaborative Research: Using AI-Enabled Smart Objects to Understand and Support Spatial Reasoning and Learning

空间思维是理解和使用有关位置的信息来解决问题的能力。 空间思维对于STEM学科的成就至关重要。 幸运的是，空间思维是可塑的，它对训练和生活经验做出反应，需要人们使用他们的空间技能。这项研究将开发和评估一个有助于幼儿空间思维的人工智能系统。 该系统依赖于一种经过时间考验的提高儿童空间技能的方法：积木游戏。 主要的进步是，这些块将是“智能的”，因为它们可以通过跟踪移动、加速度、旋转以及不同对象如何放置和处理来检测儿童交互。 通过分析儿童的建筑模式，研究人员将使用机器学习来突出积木建筑的模式，并在儿童建造时向他们提出可能导致更高水平的空间思维的策略。 研究结果可能会支持我们如何支持幼儿的空间思维，并最终支持STEM学习的变革性变化。该项目开发了一种变革性的工程概念，为儿童玩具和日常教室/游戏室对象赋予智能和互联网连接。这些物体的例子包括积木、玩具、拼图等，目的是让这些玩具和物体精确地记录儿童在玩耍和学习过程中如何与它们进行单独和集体互动。 这种数据是以非常高的时间和空间分辨率无线记录和收集的。该项目还开发了一系列新兴的基于人工智能的算法来分析智能对象生成的数据，以找出儿童处理这些对象的特定模式。然后分析这些模式，以评估空间思维和空间智力，以及它们在不同训练方案下如何随时间演变。研究人员将开发嵌入式计算工具，包括硬件和软件，以及低功耗无线通信机制，以实现智能对象的这种转变工程概念。从长远来看，他们设想将无处不在的智能引入到许多日常物品中，以便对空间智能的跟踪和分析可以无缝和高度可扩展。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Spatial thinking is the ability to understand and use information about locations to solve problems. Spatial thinking is critically important to achievement in STEM disciplines. Fortunately, spatial thinking is malleable, it responds to training and to life experiences that require people to use their spatial skills. This research will develop and assess an AI system that will facilitate young children’s spatial thinking. The system relies on a time-tested method for improving children’s spatial skills: block play. The major advancement is that the blocks will be “smart”, in that they can detect child-interactions by tracking movement, acceleration, rotation, and how different objects are placed and handled with respect to each other. By analyzing children’s building patterns, the researchers will use machine learning to highlight patterns of block building and to suggest strategies to children as they build that may result in higher levels of spatial thinking. The results may support a transformative change in how we support spatial thinking, and ultimately STEM learning, in young children.The project develops a transformative engineering concept of endowing smartness and internet connectivity to children’s toys and everyday classroom/playroom objects. Examples of such objects include playing blocks, toys, jigsaw puzzle pieces, etc. The objective is for those toys and objects to precisely record how a child interacts with them individually and in a group as parts of their play and learning process. Such data is recorded and collected wirelessly at very high temporal and spatial resolutions. The project also develops a family of emerging Artificial Intelligence based algorithms to analyze the smart object-generated data to find out specific patterns in children’s handling of those objects. Such patterns are then analyzed for evaluating spatial thinking and spatial intelligence, and how they evolve over time under different training regimens. The investigators will develop embedded computing tools, both hardware and software, as well as low power wireless communication mechanisms for realizing this transforming engineering concept of smart objects. For the long term, they envision introducing ubiquitous smartness into many everyday objects so that tracking and analysis of spatial intelligence can be seamless and highly scalable.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.