RAPID: DRL AI: Understanding Perceptions and Use of AI in K-12 Education Using a Nationally Representative Sample

RAPID：DRL AI：使用全国代表性样本了解 K-12 教育中 AI 的认知和使用

• 批准号: 2334172
• 负责人: Candice Odgers
• 金额: $ 19.95万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334172&HistoricalAwards=false
• 关键词: RAPID; DRL; AI; Understanding; Perceptions

Schoolchildren are exposed to hundreds of digital tools each year, many of which are already driven by AI technologies. Parents and teachers must consider how to incorporate these learning tools into their daily lives at a rapid pace. Yet, very little is known about the current use and perceptions of AI among these key stakeholders. This time-sensitive RAPID project will identify the opportunities and challenges that arise for parents, teachers, and youth regarding the use of these AI-driven technologies in K-12 education. Specifically, it will identify risks of generative AI in educational settings and explore opportunities for supporting future design and engagement, including supports for teacher training and regulation in educational contexts. Findings will have theoretical and practical relevance for education, child development, technology design, and policy. Results will also support policymakers as they work to establish guidelines and regulations to protect youth’s privacy, safety, and well-being in the context of AI interactions. Ideally, this work will inform how AI might be used to close versus widen existing disparities and learning gaps in youth. 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 study employs a large stratified random sample of parents, teachers, and youth across age, gender, socio-economic status, and geographical location. The probability-based nationally representative sample will be drawn from the NORC AmeriSpeak panel. The survey includes both open and close-ended questions that focus on use, perception, and trust in AI systems. Questions will probe how youth engage with generative AI as well as more traditional forms of AI in education, such as personalized and adaptive learning. An embedded experimental manipulation within the survey will test how participants evaluate AI-powered versus non-AI powered educational platforms with respect to usefulness, expertise, and trust. Qualitative interviews augment the survey findings, relying on remote participation to ensure broad geographic representation in a very short time window. Research materials will be made available as part of an online toolkit, enabling multiple investigators to use them across their studies and pool the data for future analyses through an open science approach. This large-scale mixed methods study will generate knowledge to directly inform policy guidelines regarding youth’s safety, privacy, and well-being in AI interactions. It is also positioned to help build design guidelines for AI product developers that prioritize child well-being, learning, and development.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.

学生每年都会接触到数百种数字工具，其中许多已经由人工智能技术驱动，家长和教师必须考虑如何快速将这些学习工具融入他们的日常生活中，但目前人们对此知之甚少。这些关键利益相关者对人工智能的使用和看法。这个时间敏感的 RAPID 项目将确定家长、教师和青少年在 K-12 教育中使用这些人工智能驱动的技术所带来的机遇和挑战。识别教育环境中生成人工智能的风险并探索机会支持未来的设计和参与，包括支持教育背景下的教师培训和监管。研究结果将对教育、儿童发展、技术设计和政策具有理论和实践意义。结果还将为政策制定者制定指导方针和法规提供支持。理想情况下，这项工作将告知如何利用人工智能来缩小和扩大青少年现有的差距和学习差距。同事信 (DCL)：快速加速正式和非正式环境中 K-12 教育中的人工智能研究 (NSF 23-097)，由学生和教师创新技术体验 (ITEST) 计划资助，该计划支持旨在加深对实践、计划要素、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 (ICT) 职业的知识和兴趣的背景和过程。该研究采用了大量分层随机样本基于概率的全国代表性样本将从 NORC AmeriSpeak 小组中抽取，涵盖年龄、性别、社会经济地位和地理位置。该调查包括侧重于使用的开放式和封闭式问题。问题将探讨青少年如何参与生成式人工智能以及教育中更传统的人工智能形式，例如个性化和自适应学习，调查中的嵌入式实验操作将测试参与者如何评估人工智能。人工智能驱动的教育平台与非人工智能驱动的教育平台定性访谈增强了调查结果，依靠远程参与来确保在很短的时间内获得广泛的地理代表性，研究材料将作为在线工具包的一部分提供，使多个调查人员能够跨区域使用它们。这项大规模混合方法研究将收集他们的研究数据，以供未来分析，从而直接为有关人工智能互动中青少年安全、隐私和福祉的政策指南提供信息。为人工智能产品开发人员制定优先考虑儿童的设计指南该奖项反映了 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。