Collaborative Research: RAPID: Integrating Culturally Relevant Project-based AI Learning into High School STEM Education

合作研究：RAPID：将文化相关的基于项目的人工智能学习融入高中 STEM 教育

• 批准号: 2333098
• 负责人: Shenghua Zha
• 金额: $ 15.14万
• 依托单位: University of South Alabama
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333098&HistoricalAwards=false
• 关键词: Collaborative; Research; RAPID; Integrating; Culturally

The exponential expansion of artificial intelligence (AI) has created a significant demand on future AI workforce development. Several key challenges exist hindering the widespread incorporation of AI into K-12 curricula, including the lack of teacher development in AI and a comprehensive pedagogical framework for K-12 AI education. This project aims to engage high-school students from North Dakota and southern Alabama, two geographical areas with large populations from historically marginalized groups, in the learning of AI by implementing and examining a culturally relevant project-based AI-integrated learning framework. The framework connects AI learning with students' learning experiences in science, technology, engineering, and mathematics (STEM) classes, fostering the growth of their interdisciplinary problem-solving skills. To develop students' individual interest in AI, their learning is situated within the context of personal experiences. This proposal was received in response to the Dear Colleague Letter (DCL): Rapidly Accelerating Research on Artificial Intelligence in K-12 Education in Formal and Informal Settings (NSF 23-097) and funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.The project includes two components: (1) training of high-school teachers, and (2) teachers' classroom implementation. Seventeen teachers will participate in small group and individual hands-on practices, immersive workshops, reflection and discussion, and presentations. Researchers will carry out mixed-method design research examining teacher training outcome. With scaffolding and support from researchers, teachers will develop and teach AI-STEM integrated lessons in biology and pre-calculus at their own high schools in North Dakota and southern Alabama. Multimodal data collection and analysis will be used for researching the perceptual, cognitive, and kinesthetic processes about students' problem-solving skills, interdisciplinary knowledge use, and classroom participation, as well as how these processes affect students' STEM and AI learning and interest development. This research has significant potential to contribute to our knowledge on how to effectively support historically marginalized students' interest in and learning of AI.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（AI）的指数级扩张对未来人工智能劳动力的发展提出了巨大的需求。 阻碍人工智能广泛纳入 K-12 课程的几个关键挑战包括人工智能教师发展的缺乏以及 K-12 人工智能教育的综合教学框架。该项目旨在通过实施和检查与文化相关的基于项目的人工智能集成学习框架，让来自北达科他州和阿拉巴马州南部这两个历史上边缘群体人口众多的地理区域的高中生参与人工智能的学习。 该框架将人工智能学习与学生在科学、技术、工程和数学 (STEM) 课程中的学习体验联系起来，促进他们跨学科解决问题的技能的发展。 为了培养学生对人工智能的个人兴趣，他们的学习是在个人经历的背景下进行的。该提案是为了回应亲爱的同事信 (DCL)：在正式和非正式环境中快速加速 K-12 教育中的人工智能研究 (NSF 23-097)，并由学生和教师创新技术体验 (ITEST) 资助）计划，支持加深对实践、计划要素、背景和流程的理解的项目，有助于增加学生对科学、技术、工程和数学 (STEM) 以及信息和通信技术的知识和兴趣(ICT) 职业。该项目包括两个部分：(1) 高中教师培训，(2) 教师课堂实施。十七名教师将参加小组和个人实践、沉浸式研讨会、反思和讨论以及演示。研究人员将开展混合方法设计研究，检查教师培训成果。在研究人员的支持和支持下，教师将在北达科他州和阿拉巴马州南部的自己的高中开发和教授 AI-STEM 生物学和预微积分课程。通过多模态数据收集和分析，研究学生解决问题能力、跨学科知识运用和课堂参与的感知、认知和动觉过程，以及这些过程如何影响学生的STEM和AI学习和兴趣发展。这项研究具有巨大的潜力，有助于我们了解如何有效地支持历史上边缘化的学生对人工智能的兴趣和学习。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。