Faculty as Street-Level Bureaucrats: Discretionary Decision-Making in the Era of Generative AI

Rami Alsharefeen^*

• Rabdan Academy, Abu Dhabi, United Arab Emirates

This study examines how university faculty members navigate the challenges of generative artificial intelligence (Gen-AI) plagiarism through the theoretical lens of Michael Lipsky's Street-Level Bureaucracy (SLB) framework. Drawing on qualitative data from semi-structured interviews with 17 faculty members at an internationalized university in the UAE, we analyze how faculty members exercise discretion when confronted with suspected AI-generated content in student work. Our findings reveal that faculty, as street-level bureaucrats, develop various coping strategies to manage the additional workload associated with Gen-AI detection, including preventive education, discretionary intervention, and modified assignment designs. Faculty decisions are influenced by tensions between empathy and policy enforcement, skepticism about detection tools, and concerns about institutional processes. The study highlights a significant gap between institutional expectations and faculty practices, with program chairs critiquing discretionary approaches while faculty defend them as essential for addressing nuanced student contexts. We argue that institutional policies should acknowledge and accommodate faculty discretion rather than attempting to eliminate it, emphasizing prevention and education over detection and punishment. This research contributes to understanding how front-line academic integrity enforcers shape policy implementation in practice, with significant implications for institutional governance, faculty development, and academic integrity in higher education.