TY - JOUR

T1 - Faculty as street-level bureaucrats

T2 - discretionary decision-making in the era of generative AI

AU - Alsharefeen, Rami

PY - 2025/8/13

Y1 - 2025/8/13

N2 - Introduction: This study examines how university faculty members at an internationalized higher education institution in the UAE navigate the challenges of generative artificial intelligence (Gen-AI) plagiarism through the theoretical lens of Michael Lipsky’s Street-Level Bureaucracy (SLB) framework. Methods: Drawing on qualitative data from semi-structured interviews with 17 faculty members at an internationalized university in the UAE, this paper analyzes how faculty members exercise discretion when confronted with suspected AI-generated content in student work. Results: The findings of the study 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 also 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. Discussion: This paper argues that institutional policies should acknowledge and accommodate faculty discretion rather than attempt 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.

AB - Introduction: This study examines how university faculty members at an internationalized higher education institution in the UAE navigate the challenges of generative artificial intelligence (Gen-AI) plagiarism through the theoretical lens of Michael Lipsky’s Street-Level Bureaucracy (SLB) framework. Methods: Drawing on qualitative data from semi-structured interviews with 17 faculty members at an internationalized university in the UAE, this paper analyzes how faculty members exercise discretion when confronted with suspected AI-generated content in student work. Results: The findings of the study 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 also 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. Discussion: This paper argues that institutional policies should acknowledge and accommodate faculty discretion rather than attempt 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.

KW - academic integrity

KW - street-level bureaucracy

KW - faculty discretion

KW - generative AI

KW - plagiarism

KW - higher education policy

U2 - 10.3389/feduc.2025.1662657

DO - 10.3389/feduc.2025.1662657

M3 - Journal article

VL - 10

JO - Frontiers in Education

JF - Frontiers in Education

SN - 2504-284X

M1 - 1662657

ER -