NSF 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 educational systems. In the context of developing an AI chatbot, this RAPID project will research the ways in which teachers can plan universally designed and culturally relevant and responsive K12 STEM learning activities and environments. This research will contribute to an improved LLM that will incorporate novel methods to incorporate community data and reinforce knowledge from user communities into the LLM that is largely missing from large text corpora on which LLMs are usually trained. The AI chatbot will increase teacher capacity to create more inclusive STEM activities and support career pathways by facilitating the inclusion of more underrepresented learners in STEM careers. 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.<br/><br/>The project will convene a series of focus groups to elicit training data for the chatbot, adopting a community-based participatory action research approach. This approach recognizes that AI can foster and grow community well-being by including the community in the design, orienting the AI to address community issues, and adopting an interdisciplinary and systems-based stance. The AI training will be sensitive to cultural nuances and techniques like sentiment analysis will help to understand the context and ensure culturally appropriate responses. Human-centered AI methods will be used to continuously incorporate user feedback by deploying the chatbot and actively seek responses from the diverse set of participants. This process will reinforce knowledge from user communities that is largely missing from content on which AIs are typically trained, producing an AI system that will generate more culturally aware text. Importantly, the project will create a model for developing community sourced AI LLMs that can continue to be refined and researched. A beta-level chatbot will be made available for teachers to improve their lesson plans, activity structures, and learning environments by the end of the project year for further research and development.<br/><br/>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.
