Collaborative Research: PrimaryAI: Integrating Artificial Intelligence into Upper Elementary Science with Immersive Problem-Based Learning

合作研究：PrimaryAI：通过基于问题的沉浸式学习将人工智能融入高年级基础科学

• 批准号: 1934153
• 负责人: James Lester
• 金额: $ 98.56万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934153&HistoricalAwards=false
• 关键词: Collaborative; Research; PrimaryAI; Integrating; Artificial

Artificial intelligence has emerged as a foundational technology that is profoundly reshaping society. With rapid advances in a wide array of AI and machine learning capabilities, these technologies are quickly finding broad application in every sector of the economy. The growing recognition of the demand for an AI-literate workforce highlights the urgent need to develop a deep understanding of how to introduce K-12 students to AI and how to support K-12 teachers in this endeavor. Because the elementary grades are a critical time for developing students? positive perceptions and dispositions toward STEM, creating engaging AI learning experiences for elementary grade students is of paramount importance. Similarly, developing disciplinary core ideas in life science in the elementary grades is important for creating enduring understanding of and interest in STEM for diverse learners. However, AI has been conspicuously absent from elementary education, and there has been limited research examining AI learning and teaching at the elementary level. A key open question for AI elementary education is how can students be introduced to the fundamentals of AI in the context of its application to solving core science problems? This question poses significant challenges because addressing it entails developing a socio-cognitive account of student learning processes and outcomes that can be used to inform the design of an integrated AI and science curriculum. By embedding AI in elementary life science education, researchers of this project will investigate how to meet the demand for targeted AI education while simultaneously creating innovative approaches to robust life science learning at the elementary level. This project is funded by the STEM + Computing (STEM+C) program that supports research and development to understand the integration of computing and computational thinking in STEM learning.The project will address three research questions: 1) How can we create engaging learning experiences integrating artificial intelligence and life science for upper elementary students by leveraging immersive problem-based learning? 2) How can we design a teacher professional development model for integrating artificial intelligence and life science in upper elementary classrooms? and 3) In what ways does engagement with immersive problem-based learning support upper elementary students' learning artificial intelligence and life science? To address the first research question, the project team will iteratively design, develop, and refine PrimaryAI, an integrated AI-science curriculum and immersive learning environment that will introduce AI concepts including perception, planning, robotics, and machine learning, as well as AI ethics, into upper elementary science classrooms. PrimaryAI will enable students to collaboratively learn about artificial intelligence by using age-appropriate AI tools to solve ecology problems in science adventures as they engage in argument from evidence, analyze and interpret data, develop models, and construct explanations. To address the second research question, the project team will create the PrimaryAI professional development model. The model will prepare teachers to use PrimaryAI with fidelity within their classrooms. It will take the form of a community of practice designed around three key elements: teacher professional learning, coaching, and an online community. Teacher learning will center on mentoring and participatory co-design of the immersive problem-based learning environment to ensure deep teacher knowledge of AI-infused life science education. To address the third research question, the project team will conduct design-based research to investigate how PrimaryAI improves students' understanding of computing centered around AI concepts, and of disciplinary life science content and practices. Student learning and engagement will be assessed using 1) video analysis and interaction analysis, 2) focus groups, including thematic analyses, 3) interviews with students to pilot prototypes and measures, 4) cross-case analyses of implementations, including student engagement rubric coding, 5) pre-post measures on artificial intelligence and life science content. To assess the professional development model, teacher classroom practice will be measured with 1) video analysis and interaction analysis of co-design and implementation, and 2) analyses of teacher lesson plans, journals, materials, notes, and reflections, including fidelity and adaptation, engagement coding, heuristic case studies, and interaction analyses. The deliverables of the project will include the PrimaryAI curricula, the PrimaryAI immersive problem-based learning environment, the PrimaryAI professional development model and its associated materials, and the PrimaryAI online community portal. The outcome of this project will build knowledge on the design and development of AI-infused life science learning environments and teaching models for upper elementary grades.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.

人工智能已成为一种深刻重塑社会的基础技术。随着广泛的AI和机器学习能力的快速发展，这些技术正在迅速在每个经济领域找到广泛的应用。人们对AI-Literate劳动力的需求的认识日益认识到，迫切需要对如何将K-12学生介绍给AI以及如何支持这项工作中的K-12老师。因为小学是发展学生的关键时刻？对STEM的积极看法和倾向，为小学生创造引人入胜的AI学习经验至关重要。同样，在小学成绩中发展生命科学中的纪律核心思想对于建立对多元化学习者的STEM的持久理解和兴趣很重要。但是，基础教育中没有明显的AI，并且在基础层面研究了AI学习和教学的研究有限。 AI基础教育的一个关键开放问题是，如何在解决核心科学问题的情况下向学生介绍AI的基础知识？这个问题提出了重大的挑战，因为解决该问题需要开发一个可以用来告知综合AI和科学课程设计的学生学习过程和成果的社会认知描述。通过将AI嵌入基础生命科学教育中，该项目的研究人员将研究如何满足目标AI教育的需求，同时在基础层面创建创新的生命科学学习方法。该项目由STEM +计算（STEM + C）计划资助，该计划支持研发，以了解STEM学习中计算和计算思维的整合。该项目将解决三个研究问题：1）我们如何通过利用基于安全的问题学习来创造吸引人的学习经验，为上级学生纳入人工知识和生活科学，以为上级基础学生纳入上层学生吗？ 2）我们如何设计一种教师专业发展模型，以整合高级小学教室中的人工智能和生命科学？ 3）与沉浸式问题的基于问题的学习支持上等学生的学习人工智能和生活科学如何以什么方式？为了解决第一个研究问题，项目团队将迭代设计，开发和完善PrimaryAi，这是一个综合的AI科学课程和沉浸式学习环境，将引入AI概念，包括感知，计划，机器人和机器人学习以及AI伦理学，以及AI伦理学，并将其引入高层基础科学课堂。 PrimaryAi将通过使用适合年龄的AI工具来解决科学冒险中的生态问题，从而使学生能够协作学习人工智能，从而解决科学冒险中的生态问题，从而参与证据，分析和解释数据，开发模型并构建解释。为了解决第二个研究问题，项目团队将创建主要的专业发展模型。该模型将使教师在课堂上使用忠诚度。它将采用围绕三个关键要素设计的实践社区的形式：教师专业学习，教练和在线社区。教师学习将集中在基于问题的学习环境的指导和参与式共同设计上，以确保教师对AI注入的生活科学教育的深入了解。为了解决第三个研究问题，项目团队将进行基于设计的研究，以调查小学如何改善学生对以AI概念以及纪律生命科学内容和实践为中心的计算的理解。学生的学习和参与将使用1）视频分析和互动分析评估，2）焦点小组，包括主题分析，3）与学生进行试验原型和措施的访谈，4）实施的跨案例分析，包括学生参与性公共编码，5）关于人工智能和生命科学的验证前措施。为了评估专业发展模型，将通过1）视频分析和共同设计和实施的视频分析和交互分析来衡量教师课堂实践，以及2）对教师课程计划，期刊，材料，笔记和反思的分析，包括保真度和适应性，参与编码，启发式案例研究和互动分析。该项目的可交付成果将包括初级课程，基于问题的主要学习环境，主要的专业发展模型及其相关材料以及主要的在线社区门户。该项目的结果将建立有关高级基础成绩的AI注入生命科学学习环境和教学模型的知识。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响来通过评估来支持的。

项目成果

AI-Infused Collaborative Inquiry in Upper Elementary School: A Game-Based Learning Approach

小学高年级人工智能注入的协作探究：基于游戏的学习方法

• 发表时间: 2021
• 期刊: Proceedings of the Eleventh Symposium on Educational Advances in Artificial Intelligence
• 作者: Lee, S.

Designing and Developing Interactive Narratives for Collaborative Problem-Based Learning

• DOI: 10.1007/978-3-030-33894-7_10
• 发表时间: 2019-01-01
• 期刊: INTERACTIVE STORYTELLING, ICIDS 2019
• 作者: Mott, Bradford W.;Taylor, Robert G.;Lester, James C.
• 通讯作者: Lester, James C.

Principles for AI Education for Elementary Grades Students

小学生人工智能教育原则

• DOI: 10.1145/3502717.3532143
• 发表时间: 2022
• 期刊: 27th ACM Conference on Innovation and Technology in Computer Science Education
• 作者: Ottenbreit-Leftwich, Anne;Glazewski, Krista;Jeon, Minji;Jantaraweragul, Katie;Hmelo-Silver, Cindy;Scribner, Adam;Lee, Seung;Mott, Bradford;Lester, James
• 通讯作者: Lester, James

Position: IntelliBlox: A Toolkit for Integrating Block-Based Programming into Game-Based Learning Environments

位置：IntelliBlox：将基于块的编程集成到基于游戏的学习环境中的工具包

• DOI: 10.1109/bb48857.2019.8941222
• 发表时间: 2019
• 期刊: 2019 IEEE Blocks and Beyond Workshop (B&B
• 作者: Taylor, Sandra;Min, Wookhee;Mott, Bradford;Emerson, Andrew;Smith, Andy;Wiebe, Eric;Lester, James
• 通讯作者: Lester, James

Designing a Visual Interface for Elementary Students to Formulate AI Planning Tasks

为小学生设计一个可视化界面来制定AI规划任务

• DOI: 10.1109/vl/hcc51201.2021.9576163
• 发表时间: 2021
• 期刊: 2021 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC
• 作者: Park, Kyungjin;Mott, Bradford;Lee, Seung;Glazewski, Krista;Scribner, J. Adam;Ottenbreit-Leftwich, Anne;Hmelo-Silver, Cindy E.;Lester, James
• 通讯作者: Lester, James