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

学龄儿童每年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。家长和老师必须考虑如何将这些学习工具快速融入他们的日常生活。然而，人们对这些关键利益攸关方目前对人工智能的使用和看法知之甚少。这个时间敏感的快速项目将确定家长、教师和青年在K-12教育中使用这些人工智能驱动的技术方面出现的机会和挑战。具体地说，它将确定教育环境中生成性人工智能的风险，并探索支持未来设计和参与的机会，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。结果还将支持政策制定者努力建立指导方针和法规，以在人工智能互动的背景下保护年轻人的隐私、安全和福祉。理想情况下，这项工作将告知如何使用人工智能来缩小而不是扩大年轻人现有的差距和学习差距。这项建议是为了回应亲爱的同事信(DCL)：在正式和非正式环境下迅速加快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计划(ITEST)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。该研究采用了大量分层随机抽样的父母、教师和不同年龄、性别、社会经济地位和地理位置的年轻人。基于概率的全国代表性样本将从NORC ameriSak小组中抽取。这项调查包括开放式和封闭式问题，重点是对人工智能系统的使用、感知和信任。问题将探索年轻人如何参与生成性人工智能以及教育中更传统的人工智能形式，如个性化和适应性学习。调查中嵌入了一个实验操作，将测试参与者如何在有用性、专业知识和信任方面对人工智能支持的教育平台和非人工智能支持的教育平台进行评估。定性访谈增强了调查结果，依靠远程参与确保在很短的时间窗口内具有广泛的地域代表性。研究材料将作为在线工具包的一部分提供，使多名研究人员能够在他们的研究中使用这些材料，并通过开放科学的方法为未来的分析汇集数据。这项大规模的混合方法研究将产生知识，直接为关于年轻人在人工智能互动中的安全、隐私和福祉的政策指导方针提供信息。它还有助于为人工智能产品开发人员建立设计指南，将儿童的福祉、学习和发展放在首位。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。

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.