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In May 2023, NSF issued a Dear Colleague Letter (DCL) inviting AI education researchers to submit Rapid Response Research (RAPID) proposals focusing on data-driven research for solutions in addressing the challenges faced by formal and informal K-12 educational settings and systems transformed by the unprecedented speed of advancements in machine learning (ML), generative artificial intelligence (AI), and large language models (LLM). The nature of learning, teaching, and assessment is rapidly evolving. Developing AI tools and environments to advance age-appropriate equitable learning and inclusive teaching, and integrating generative AI in education in an ethical, responsible, and effective way are of paramount importance.
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On January 14, 2025, STELAR hosted a summit for the awardees of the DCL to share their time-sensitive research findings. The below table captures the projects, research white papers, and recordings of project presentations, as well as the agenda for the event.
| RAPID Project(s) | Related Links and Resource(s) |
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| A Career-Driven AI Educational Program in Smart Manufacturing for Underserved High-school Students in the Alabama Black Belt Region https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338987 Award# 2338987 The integration of artificial intelligence (AI) into advanced manufacturing has promising potential to revolutionize productivity and generate new jobs in smart manufacturing. There is an urgent need to investigate "what to teach" and "how to teach" AI in order to prepare future ... See more PI(s): Jia Liu |
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| A Community-Inclusive AI Chatbot to Support Teachers in Developing Culturally Focused and Universally Designed STEM Activities https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334631 Award# 2334631 Large language models (LLM) represent a new and rapidly changing technological advancement for K12 STEM learning. It is critical at this point in time to investigate and provide pathways for including justice, equity, inclusion, and community cultural capital and wealth in designing LLM-based educat... See more PI(s): Jeremy Price |
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| Artificial Intelligence Curriculum and K-12 Teacher Agency: Barriers and Opportunities https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333393 Award# 2333393 AI-powered tools have the potential to transform education, both in formal and informal settings. The immense potential for AI to address challenges in education has created an urgent need to characterize how K-12 education may leverage these powerful tools safely, ethically, and equitably. While th... See more PI(s): Karin Jensen |
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| Constructing Understandings of Generative AI and Machine Learning with High School Youth https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335926 Award# 2335926 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 t... See more PI(s): Antero Garcia |
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| Data Driven Approaches to Integrating AI in K-12 Education Using Social Media Analysis https://www.nsf.gov/awardsearch/showAward?AWD_ID=2332306 Award# 2332306 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, leavi... See more PI(s): Hari Kalva |
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| Empowering Future Teachers with Generative AI Education: Understanding Applications, Risks, and Limits https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333675 Award# 2333675 Generative artificial intelligence (AI) tools have the potential to reshape education and may change teaching practices in K-12. As such, teachers must prepare students to have the necessary skills that will allow them to learn about these tools and use them productively for their lives and educatio... See more PI(s): Aman Yadav |
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| Empowering Math Teachers with an AI Tool for Auto-Generation of Technology-Enhanced Assessments https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335834 & https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335835 Award# 2335834, 2335835 The proliferation of artificial intelligence (AI) and powerful Large Language Models (LLMs) has evoked excitement and confusion among K-12 teachers regarding AI's impact on teaching, assessments, and student work. It is vital for researchers with expertise in human-centered teaching and learnin... See more PI(s): Shuchi Grover, Corrin Clarkson |
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| Empowering Teachers to Collaborate with Generative AI for Developing High-Quality STEM Learning Resources https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335975 Award# 2335975 The rapid advances in large language models (LLMs) have presented tremendous opportunities to create interactive, personalized learning resources on a large scale. To fully harness the educational potential of these technologies, it is crucial that teachers - who are at the forefront of daily stude... See more PI(s): Xu Wang |
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| Engaging High School Youth in Algorithmic Justice Through Audits of Designed and Everyday Machine Learning Applications https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333469 Award# 2333469 Rapid developments in artificial intelligence (AI) and machine learning (ML) applications have led to nation-wide calls for supporting youth in the development of artificial intelligence literacy, competencies needed to effectively interact with and critically evaluate artificial intelligence. Most ... See more PI(s): Yasmin Kafai |
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| Exploring an AI Literacies Framework for Young Children: A Delphi Study https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334829 Award# 2334829 Artificial intelligence (AI) is rapidly transforming society, and it is becoming increasingly clear to educators and researchers that K-12 students must be prepared for an AI-driven future. One crucial area that requires research is early childhood education (ECE) for children aged 5 to 8 years old.... See more PI(s): Xiaohui Wang |
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| Integrating Culturally Relevant Project-based AI Learning into High School STEM Education https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333098 & https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333099 Award# 2333098, 2333099 The exponential expansion of artificial intelligence (AI) has created a significant demand on future AI workforce development. Several key challenges exist hindering the widespread incorporation of AI into K-12 curricula, including the lack of teacher development in AI and a comprehensive pedagogic... See more PI(s): Shenghua Zha, Woei Hung |
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| Non-digital Hands-on AI Learning Resources for Middle-School Students https://www.nsf.gov/awardsearch/showAward?AWD_ID=2343693 Award# 2343693 Young learners need opportunities to build critical awareness surrounding AI. To rapidly expand inclusive access to AI education, K-12 educators need activities that (a) are low-cost, (b) do not require specialized technology, (c) can be led with little prior knowledge of AI and used in settings wit... See more PI(s): Duri Long |
https://vimeo.com/1036001954?fl=pl&fe=vl |
| Responsible, Ethical, and Effective Acceptable Use Policies for the Integration of Generative AI in US School Districts and Beyond https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334525 Award# 2334525 The rapidly evolving space of artificial intelligence (AI) is requiring school and district leaders to make sense of how emerging technology applications, including those that use generative AI (GenAI), are being integrated in schools and districts across the United States. Much uncertainty exists a... See more PI(s): Patricia Ruiz |
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| Scaffolding Automated Feedback for Teachers https://www.nsf.gov/awardsearch/showAward?AWD_ID=2337772 Award# 2337772 While Artificial intelligence (AI) has the potential to improve both science, technology, engineering and mathematics (STEM) teaching practice and students' overall classroom experiences, it is critical to better understand how teachers can more easily adapt it within their classrooms. In parti... See more PI(s): Dora Demszky |
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| The Development of a Digital Platform for Evaluating and Using AI-Generated Content for Academic Purposes https://www.nsf.gov/awardsearch/showAward?AWD_ID=2337969 Award# 2337969 The recent development of artificial intelligence (AI) tools such as ChatGPT and Bard present new opportunities and challenges for learners in the elementary and middle grades. Despite their potential for facilitating and supporting scientific reading and writing, there are pressing concerns about h... See more PI(s): Amy Hutchison |
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| Understanding and Supporting K-12 School Leaders' AI-related Decision-making https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333764 Award# 2333764 The 2022 launch of ChatGPT has accelerated the need for school leaders (at both the district and building level) to make important and time-sensitive decisions related to the use of artificial intelligence in their schools. Given the rapid development of artificial intelligence, there are few establ... See more PI(s): David Miller |
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| Understanding Perceptions and Use of AI in K-12 Education Using a Nationally Representative Sample https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334172 Award# 2334172 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 o... See more PI(s): Candice Odgers |
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| Unlocking the Potential of Generative AI for Equity and Access in Robotics Education https://www.nsf.gov/awardsearch/showAward?AWD_ID=2341190 Award# 2341190 In the context of educational robotics, this RAPID project examines the educational equity potential of a task-oriented generative artificial intelligence (AI) tool to assist with creative productivity tasks in engineering design. This time-sensitive project will investigate how to support lower-per... See more PI(s): Ross Higashi |
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