A Case for Rules-Based AI Reminders: The SUNY Educational Opportunity Program at the University at Buffalo

As artificial intelligence (AI) tools become increasingly integrated into higher education, their impact depends on how they are implemented and for whom they are designed. For underserved students participating in the State University of New York’s Educational Opportunity Program (EOP) at the University at Buffalo, belonging is a central condition for persistence and success. Many EOP students are first-generation college students from low-income backgrounds who face structural barriers, educational trauma, and unfamiliar academic systems. This case examines the use of low-stakes, rules-based AI systems, specifically automated and personalized reminders delivered through the learning management system, as an equity-oriented instructional practice. The paper argues that AI-mediated reminders can reduce cognitive and logistical burdens, make expectations explicit, and provide structured next steps following missed assignments. When grounded in transparency, consistency, and care, these reminders communicate institutional support, normalize help-seeking, and reinforce accountability paired with flexibility. While AI is neither inherently equitable nor inherently harmful, its thoughtful implementation can scaffold responses to student behavior and support belonging rather than exacerbate inequality.