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

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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.