RAPID: DRL AI: Constructing Understandings of Generative AI and Machine Learning with High School Youth

RAPID：DRL AI：与高中生一起构建对生成式 AI 和机器学习的理解

• 批准号: 2335926
• 负责人: Antero Garcia
• 金额: $ 20万
• 依托单位: Stanford University
• 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: 2023-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335926&HistoricalAwards=false
• 关键词: RAPID; DRL; AI; Constructing; Understandings

This project explores how high school youth communicate with AI tools and assesses how they learn to use AI tools in context. Though students are using these tools widely, inciting much public discourse, very little is known about how, why, and when they use AI tools, or what they understand about the tools. Through curricular exploration, this study evaluates how historically marginalized high school youth of color interact with AI tools to write papers, write program code, or study for science tests. 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.Through a one-year study, researchers will pilot generative AI activities in classrooms and out-of-school clubs; collect quantitative data, logs, video data, screen recordings, and artifactual data; and analyze those data to find mechanistic explanations and generalizable models of how students are interacting with and learning AI content. Offering a foundational perspective for AI-related learning and augmentation, this study is a crucial and time-sensitive first step, required before scalable interventions and curriculum development. The research questions guiding the study are (1) How do students and teachers learn to communicate with generative AI? (2) How do learners demonstrate their understanding of AI and predict AI responses in order to yield desired results? and (3) How do students compare AI to their own cognitive strategies? The project has multiple potential transformative outcomes including a model for how students and teachers understand generative AI; a curriculum for teaching students and teachers how to use generative AI ethically and meaningfully; and new possibilities for using generative AI in education. Research findings, instructional materials and teacher professional development guidelines will be shared with and distributed to the research and practitioner communities.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.

该项目探索高中生如何使用人工智能工具进行交流，并评估他们如何在背景下学习使用人工智能工具。尽管学生们广泛使用这些工具，引发了大量的公共讨论，但人们对他们如何、为什么以及何时使用人工智能工具，或者他们对这些工具的理解知之甚少。通过课程探索，本研究评估了历史上被边缘化的有色人种高中青年如何与人工智能工具互动，以撰写论文、编写程序代码或为科学考试学习。这项建议是为了回应亲爱的同事来信(DCL)：在正式和非正式环境中迅速加速对K-12教育中人工智能的研究(NSF 23-097)，并由学生和教师创新技术体验(ITEST)计划资助，该计划支持一些项目，这些项目建立对实践、计划要素、背景和过程的理解，有助于提高学生对科学、技术、工程和数学(STEM)以及信息和通信技术(ICT)职业的知识和兴趣。通过为期一年的研究，研究人员将在课堂和校外俱乐部引导生成性人工智能活动；收集定量数据、日志、视频数据、屏幕记录和人工数据；并分析这些数据，以找到学生如何与人工智能内容交互和学习的机械性解释和概括模型。这项研究为人工智能相关的学习和增强提供了一个基础性的视角，是关键的和时间敏感的第一步，在可扩展的干预和课程开发之前是必需的。指导本研究的研究问题是：(1)学生和教师如何学习与生成性人工智能交流？(2)学习者如何证明他们对人工智能的理解并预测人工智能的反应，以达到预期的结果？(3)学生如何将人工智能与他们自己的认知策略进行比较？该项目具有多个潜在的变革性成果，包括一个学生和教师如何理解生成性人工智能的模型；一个教学生和教师如何在道德上和有意义地使用生成性人工智能的课程；以及在教育中使用生成性人工智能的新可能性。研究结果、教学材料和教师专业发展指南将与研究和从业者社区共享并分发给研究和实践者社区。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标准进行评估，被认为值得支持。