Integrating Artificial Intelligence with Smart Engineering and English Language Arts in Upper Elementary Education

该项目将培养高年级学生在未来职业生涯中使用人工智能(AI)的能力。人工智能将成为影响和提高未来工作效率的关键工具。因此，向K-12学生介绍基本的人工智能知识和技能，熟悉人工智能技术，并培养学生在劳动力中具有竞争力变得越来越重要。通过该项目，马萨诸塞州塔夫茨大学和密苏里州圣路易斯市玛丽维尔大学的机器人和教育研究人员团队将与圣路易斯县的50多名教师合作，开发一个研究知情的教育生态系统，将人工智能概念带给高年级小学生。这个生态系统将包括一个新颖的、低成本的、支持人工智能的硬件工具包，其中包括传感器、执行器和微控制器等组件，供学生构建智能系统，以及支持教师专业发展。通过课外和暑期计划，该项目将吸引1000多名圣路易斯县学生构建功能性人工智能解决方案，以解决学生在英语语言艺术和暑期阅读计划中阅读的虚构故事中出现的问题。该方法的目标是鼓励在人工智能、工程和识字的交叉点上进行跨学科学习。教育项目测试将包括来自小学高年级目标受众的12名教师和500名学生，其他参与者将参加K-12年级的试点测试。该项目旨在建立一种新的模式，将重要的人工智能概念引入小学生，减少将计算思维整合到学校课程中的障碍，并为学生提供有形的、可培训的人工智能表现形式，供学生探索。研究人员将调查三个主要研究问题：1)有形的人工智能元素的引入如何导致小学高年级学生对人工智能概念和态度的理解发生变化？2)不同程度和多样化的有形人工智能学习工具如何影响学生的参与度及其解决方案和设计的多样性？3)在综合工程和扫盲活动中引入有形的人工智能元素的潜在好处和挑战是什么？项目团队将应用基于设计的研究(DBR)方法来联合生成跨学科教育和学习科学理论，同时迭代地设计和开发工具包、专业发展和活动。这项研究将包括对人工智能专业人员的采访和调查，以制定和验证学生对人工智能概念和态度的理解程度的年级合适的衡量标准。该团队将使用混合方法分析方法评估研究问题，从教师访谈、课程和专业发展观察、学生制作的人工制品分析和基于人工制品的学生采访中使用来自教师和学生调查的定量数据来三角划分定性数据。该项目的贡献将使人工智能教育范式转向融合的课程，而不是需要单独教学的编码和计算的现状。该项目的调查结果将有助于向该领域通报高年级人工智能教育面临的具体挑战。该项目的成果将包括人工智能动画发明(AI2)硬件工具包、教师专业发展模型、范例活动和学生工作图库、项目有效性的衡量标准，以及用于学习人工智能概念的有形人工智能元素的可用性和实用性。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。

This project will develop upper elementary school students’ abilities to work with Artificial intelligence (AI) in future careers. AI will be a critical tool for influencing and increasing productivity in the future of work. As such, it is increasingly important to introduce K-12 students to basic AI knowledge and skills, build familiarity with AI technologies, and train students to be competitive in the workforce. Through this project, a team of robotics and education researchers at Tufts University in Massachusetts and Maryville University in St. Louis, MO will work with over 50 teachers in St. Louis County to develop a research-informed educational ecosystem bringing AI concepts to upper elementary school students. This ecosystem will include a novel, low-cost, AI-enabled hardware toolset, including components such as sensors, actuators, and a microcontroller, for students to build smart systems, as well as support for teacher professional development. Through after-school and summer programs, the project will engage over 1000 St. Louis County students in constructing functional AI-enabled solutions to problems presented in fictional stories that the students read in English language arts and summer reading programs. The goal of the approach is to encourage transdisciplinary learning at the intersection of AI, engineering, and literacy. The education program testing will include 12 teachers and 500 students from the upper elementary target audience, with other participants in pilot testing across the K-12 grade band. The project aims to generate a new model for introducing vital AI concepts to elementary students that reduces barriers to integrating computational thinking into school curricula and provides tangible, trainable representations of AI for students to explore.Researchers will investigate three primary research questions: 1) How does the introduction of tangible artificial intelligence elements lead to changes in upper elementary students’ understanding of artificial intelligence concepts and attitudes towards artificial intelligence? 2) How do different levels of complexity and variety of tangible artificial intelligence learning tools impact students' engagement and the diversity of their solutions and designs? 3) What are the potential benefits and challenges of introducing tangible artificial intelligence elements in integrated engineering and literacy activities? The project team will apply a design-based research (DBR) approach to jointly generate interdisciplinary education and learning sciences theory alongside iteratively designing and developing the toolset, professional development, and activities. The research will include interviews and surveys with AI professionals to develop and validate grade-appropriate measures of students’ understanding of AI concepts and attitudes. The team will evaluate the research questions with a mixed-methods analytic approach, triangulating qualitative data from teacher interviews, lesson and professional development observations, student-made artifact analysis, and artifact-based student interviews with quantitative data from teacher and student surveys. The project’s contribution will shift the artificial intelligence education paradigm towards a convergent curriculum and away from the status quo of coding and computing requiring separate instruction. The project findings will help inform the field about challenges specific to AI education in upper elementary grades. The deliverables from this project will include the AI Animated Inventions (AI2) hardware toolset, a teacher professional development model, a gallery of example activities and student work, and measures of the program’s effectiveness, as well as the usability and utility of the tangible artificial intelligence elements for learning AI concepts.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.