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Cited by 10Academic integrity and plagiarismArtificial Intelligence in EducationLegal Issues in Education

Faculty as street-level bureaucrats: discretionary decision-making in the era of generative AI

Open access
Alsharefeen, Rami·2025·Frontiers in Education
DOI 10.3389/feduc.2025.1662657
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Abstract

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.

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  • Faculty who treat AI detection flags as prompts for discussion rather than final verdicts produce fairer and more educationally meaningful outcomes.

    “Alsharefeen (2025) highlights that faculty who treat AI detection flags as prompts for discussion rather than final verdicts produce fairer and more educationally meaningful outcomes.”

    From Harms and Ethical Risks of AI Detection Tools in Education: False Positives, Bias, Surveillance, and Student Rights (2026) · cites this paper as Alsharefeen (2025)

    An interviewee describes using a Turnitin flag to start a conversation with a student and offer a second chance. Section 5.1 describes such interventions as educational, and section 6.4 recommends that detection tools complement faculty judgment. The paper does not establish that this practice produces fairer outcomes than treating flags as final verdicts; it presents fairness as a concern and a goal. (LLM, conf=0.85)

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Authors
Alsharefeen, R.
Affiliations
Rabdan Academy (AE)
Corresponding author
Alsharefeen, Rami
ORCID
Alsharefeen, Rami
Venue
Frontiers in Education
Publisher
Frontiers Media SA
Published
2025-08-13
Volume
10
ISSN
2504-284X (ISSN-L)
DOI
10.3389/feduc.2025.1662657
OpenAlex
W4413163147
Cited by
7
References
48
Citations by year
2026: 7
FWCI
7.32
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Education (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Lancaster EPrints (Lancaster University) (submittedVersion)
  • Rabdan Academy Digital Repository (Rabdan Academy) (submittedVersion, cc-by)
Topics
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Law › Legal Issues in Education
Keywords
street-level bureaucracy, generative AI, academic integrity, AI detection tools, policy implementation, faculty workload, program chairs, institutional governance, faculty development

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