Project Profile

This project responds to the growing recognition that learners of all ages should have the opportunity to engage with and learn about artificial intelligence (AI). Of AI's many subfields, natural language processing (NLP) is one of the fastest growing. NLP focuses on how to automatically understand spoken or textual data, and billions of these textual or spoken exchanges are recorded online every day. Historically, using NLP required extensive coding expertise and a deep understanding of machine learning, but recent advances and work by the project team have laid the foundation for bringing authentic, situated NLP learning into middle school classrooms. The project foregrounds how to teach NLP with ethics and ethical reasoning applied to relevant local issues and will investigate how to integrate AI learning through NLP into middle school science on weather systems and cycles through problem-based learning. The research team will carry out participatory co-design with teachers and will provide potentially transformative experiences to twelve teachers and over one thousand students, leveraging existing partnerships within a multi-district, two-state effort in Florida, and Indiana in diverse schools with approximately 50% African American students and the majority of students eligible for free or reduced lunch. This project is 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.

Researchers will investigate the ways in which teacher professional development and participatory co-design can be tailored to support teachers' competencies and sense of preparedness to bring authentic, problem-based NLP experiences into science classrooms. For students, the project’s hypothesis is that these innovative experiences will foster competencies and attitudes toward STEM careers. The project is guided by the following research questions. (1) (a) What aspects of participatory co-design are helpful for supporting science teachers' development of AI education and NLP proficiencies? (b) In what ways can existing professional development strategies support AI-integrated science learning? (c) What new strategies emerge throughout the process? (2) (a) What kinds of AI-integrated science learning activities emerge from participatory co-design with teachers? (b) What specific adaptations are necessary to ensure activities are personally, locally, and culturally relevant for students? (3) How does integrated NLP and science support learner competencies (knowledge and skills in NLP and science) and ethical reasoning?; and (4) How does integrated NLP and science support students in establishing high quality engagement and fostering positive attitudes toward STEM careers (interest, identity, and intention to persist)? The project will investigate these questions using a mixed-methods design: qualitative data from teachers and students will include narrative accounts, observations of teaching practices, and interviews with teachers about their preparedness and self-efficacy for teaching AI-integrated science; quantitative data will include knowledge and skills assessments for AI-integrated science administered to both teachers and students. The project's deliverables will include (1) empirical findings from the above research questions and (2) an innovative NLP curriculum integrated into NGSS standards-aligned middle school science. The curriculum will be the outcome of iterative refinement over three years of the project through co-design with teachers. This curriculum, accompanying materials, and learning environment will be made publicly and freely available.

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.