CAREER: AI-Guided, Authentic, Ag-STEM Inquiry for Cultivating Computational Thinking in Rural Middle Schools

Artificial intelligence and data analytics are reshaping how food is grown, monitored, and managed across rural communities, yet rural students, who live closest to these changes, often have limited opportunities to learn the data-driven computational practices behind modern agriculture. This gap matters because agriculture increasingly needs workers who can interpret data, work with automated systems, and make evidence-based decisions, while many rural schools have limited ways to build those capacities within existing coursework. This CAREER project will address that gap by designing, implementing, and studying an AI-guided agriculture STEM (Ag-STEM) learning model for rural middle school classrooms in which students will investigate local sustainability problems, build and use low-cost environmental sensors, analyze and model data, and develop evidence-based recommendations for farmers and community stakeholders. Grounded in place-based learning and authentic role participation, the project will contribute knowledge about how computation- and data-rich STEM learning can be made practical in everyday rural classrooms, with implications for workforce preparation in agriculture and in other applied STEM fields that rely on data and human-AI collaboration. The project will partner with agriculture teachers and approximately 200-250 middle school students each year in two rural Texas districts across five annual design-based research cycles to iteratively develop, study, and refine a 16-lesson Ag-STEM learning ecology, including classroom activities, teacher materials, and a classroom-safe AI peer mentor. The research will examine how students develop disciplinary computational thinking (CT) as they use variables, datasets, rules, models, and decision tools to investigate local agricultural questions and design, justify, and communicate evidence-based solutions for stakeholders. Student CT development will be studied as a multi-indicator construct through pre/post self-report and scenario-based performance measures, repeated rubric scores from team artifacts, and coded classroom observations and recordings, interviews, and AI interaction logs. Multilevel growth models and mixed-effects analyses will examine change over time and links between team-level enacted computational thinking and individual gains, while classroom observations, recordings, interviews, and AI interaction logs will investigate the instructional, technological, and social mechanisms that support student learning, teacher adaptation, and the curriculum and AI maturation. The project plans to yield empirical evidence, theory, and design principles for human-AI-supported disciplinary CT in rural Ag-STEM settings, along with open-access curricular materials, teacher supports, and an AI prompt library for classroom use. This is a Faculty Early Career Development Program project responsive to a National Science Foundation-wide activity that offers the most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in research and education. 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. 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. NSF Award ID: 2541320 | Program: 1300PYXXDB H-1B FUND, EDU, NSF | Principal Investigator: Tugce Aldemir | Institution: Texas A&M University, COLLEGE STATION, TX | Award Amount: $952,970 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2541320 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2541320.html