Advancing STEM Student Success Through AI Literacy in a Gateway Chemistry Course at a Two-Year College

This project aims to serve the national interest by increasing student persistence in gateway science courses at two-year colleges, preparing more students for careers in technical and scientific fields. General Chemistry I carries national drop-fail-withdraw rates near 30 percent, climbing to 42 percent for online students, making it one of the most consistent degree- completion barriers in undergraduate science. Two-year college students bring considerable assets to these courses: practical work experience, clear career goals, and familiarity with solving concrete problems under time and resource constraints. The importance of this project is its plan to build on those strengths by embedding structured Artificial Intelligence literacy into chemistry coursework, training students to use AI for self-checking and reflection rather than answer retrieval. This Track 1 project plans to deploy an original instructional framework called A-PAVR (Abstract, Plan, Attempt, Verify, Reflect) across General Chemistry I and Introductory Chemistry at Salt Lake Community College, including sections taught through Concurrent Enrollment at partner high schools. The project also plans to build sustainable institutional capacity by equipping faculty with the pedagogy and open educational resources needed to teach responsible AI use at scale. The goals of this project center on reducing drop-fail-withdraw rates in General Chemistry I, strengthening students' self-regulated learning skills, and producing nationally replicable evidence on how structured AI use affects learning in two-year college science pathways. The scope spans General Chemistry I and Introductory Chemistry, including Concurrent Enrollment sections, reaching approximately 700 students per semester across roughly 30 sections. The A- PAVR framework structures each study cycle into AI-assisted phases (Abstract and Verify) and strictly analog phases (Plan and Attempt), preserving the cognitive load of problem-solving while using AI feedback to support metacognitive reflection. The project plans to measure outcomes through learning analytics, student surveys, and longitudinal course progression data across the three-year project period. A parallel faculty development strand plans to train instructors through Faculty Learning Communities and an asynchronous eLearning course, certifying them in outcomes-aligned AI pedagogy and producing an open educational resource (the STEM AI Literacy Handbook) available for adoption by other institutions. The project plans to disseminate findings through the Journal of Chemical Education, Canvas Commons, and the REAL CHEM Community of Practice. The NSF IUSE: Innovation in Two-Year College STEM Education (ITYC) Program seeks to accelerate and advance knowledge about the impact of emerging and evidence-based practices in undergraduate STEM education at two-year colleges. 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.