RAPID: DRL AI: Data Driven Approaches to Integrating AI in K-12 Education Using Social Media Analysis

大型语言模型，特别是ChatGPT，已经看到了指数级增长，并已显示出改变教学和学习的早期潜力。鉴于这个快速变化的领域，关于学生和教师如何使用这些新的生成式AI工具的系统研究有限，这使得学校几乎没有数据来帮助他们将AI整合到K-12教育中。这个RAPID项目旨在开发一个通用框架和相应的计算工具，以了解学生和教师如何使用生成式AI工具。通过深入了解教师和学生的热情和担忧，该项目旨在让人工智能集成团队更深入地了解人工智能在教育环境中的使用。这种理解将有助于确定机会，并为K-12教育的变革奠定基础。由此产生的数据分析将提供有关教师在课堂上采用人工智能时的使用、看法、恐惧和挫折感的丰富信息。它还将为学生的使用，趋势，兴趣水平提供新的见解，并初步了解他们对人工智能的责任感和道德使用。结合起来，这将向公众通报教师、学生和学区的数字准备情况，包括在课堂上负责任/道德地使用人工智能，以及学生为人工智能职业做好准备。该项目还将为K-12教育中的人工智能标准做出贡献。此外，该项目的研究成果将被广泛传播，其主要目的是提供可纳入教育实践的实用指南。该项目提出了一种数据驱动的方法，以了解高中教师和学生如何开始使用AI工具。该项目将从社交媒体平台收集时间敏感的数据，并开发方法来识别推动学生和教师探索和使用人工智能工具的主题和趋势。该项目的主要任务如下：1）识别社交网络和相关社区，2）开发工具以收集相关的过去和当前帖子，3）进行数据分析以确定主题，趋势，并得出在教学中有效使用AI工具的关键挑战和机遇。数据分析工作流程将用于确定学生和教师群体的趋势，主题，问题和关注点。主题建模和用户活动分析将用于开发学生和教师的观点。本提案是对亲爱的同事信（DCL）的回应：在正式和非正式环境中快速加速人工智能在K-12教育中的研究（NSF 23 - 097），并由学生和教师创新技术经验（ITEST）计划资助，该计划支持建立对实践，计划要素，有助于增加学生对科学，技术，工程，信息和通信技术（ICT）该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Large language models, especially ChatGPT, have seen exponential growth and have demonstrated early potential to transform teaching and learning. Given the rapidly changing field, there are limited systematic studies on how students and teachers are engaging with these new generative AI tools, leaving schools with little to no data to help them integrate AI in K-12 education. This RAPID project aims to develop a general framework and accompanying computational tools to understand how students and teachers are engaging with generative AI tools. By gaining insights into both the enthusiasm and concerns from teachers and students, the project seeks to equip AI integration teams with a deeper understanding of AI usage in educational settings. This understanding will help identify opportunities, and lay a foundation for transformative changes to K-12 education. The resulting data analytics will provide rich information about teachers' use, perceptions, fears, and frustration in adopting AI in the classroom. It will also offer new insights into students' use, trends, level of interest, and an initial view of their sense of responsible and ethical use of AI. Combined, this will inform the public on the digital readiness of teachers, students, and school districts, including responsible/ethical use of AI in the classroom and student preparation for careers in AI. The project will also potentially contribute to the AI standards in K-12 education. Furthermore, the project's findings will be broadly disseminated, with the primary objective of providing practical guidelines that can be incorporated into educational practices.This project proposes a data-driven approach to understand how high school teachers and students have begun using AI tools. The project will collect time-sensitive data from social media platforms and develop methods to identify topics and trends driving the exploration and use of AI tools by students and teachers. The key tasks in this project are as follows: 1) identifying social networks and related communities, 2) developing tools to collect relevant past and current posts, and 3) performing data analysis to identify topics, trends, and to elicit key challenges and opportunities for effectively using AI tools in teaching and learning. The data analysis workflows will be used to identify trends, topics, questions, and concerns among student and teacher groups. Topic modeling and user activity analysis will be used to develop student and teacher perspectives. 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.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.