Probability is not proof: Why AI detection differs from plagiarism detection in academic misconduct cases
Abstract
This conceptual synthesis examines why artificial intelligence (AI) text detection cannot be treated as equivalent to plagiarism detection in cases of academic misconduct. Plagiarism detection tools operate forensically: they identify specific passages, match them to verifiable sources, and produce evidence that both instructors and students can examine and rebut. AI detectors operate statistically: they estimate the probability that a text resembles AI-generated writing, without identifying a source, act, or comparator. Drawing on independent evaluations showing no tool exceeds 80 per cent accuracy,1 false-positive rates above 61 per cent for non-native English writers in a Test of English as a Foreign Language (TOEFL)-essay testing condition,2 and journalistic survey evidence reporting disproportionate false-accusation experiences among Black students,3 the paper applies three lenses. First, evidentiary: AI scores are not falsifiable and suffer base-rate problems that make predictive value unknowable in real classrooms. Secondly, procedural: under Goss v. Lopez (1975) and Mathews v. Eldridge (1976), students are entitled to notice and a meaningful opportunity to respond, a safeguard undermined when the evidence is an opaque probability. Thirdly, fairness: applying Rawlsian justice, rational agents behind a veil of ignorance would reject a system whose errors fall most heavily on non-native speakers and on groups that survey evidence suggests may face disproportionate risk of accusation. Recent reporting on one federal preliminary ruling suggests discipline is more defensible when institutions rely on corroborating evidence, not scores alone. The paper concludes that detection outputs should never serve as standalone proof and recommends process-based assessment, bias audits, and transparent policies. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
The full article is available to subscribers to this journal (subscription is free).
Author's Biography
Dr David Ison, PhD, is an aviation planner, researcher, and higher education scholar whose work examines the intersection of emerging technology, public policy, safety, and institutional decision making. His professional background spans flying as an airline pilot, teaching at various higher education institutions, aviation system planning, airport land use, drones, advanced air mobility, alternative fuels, and integrating new technologies into aviation and education. In his applied planning work, he focuses on translating complex technical developments into practical guidance for public agencies, local governments, airports, and community stakeholders. David’s research interests include artificial intelligence (AI), academic integrity, aviation innovation, technology governance, and evidence-based policy. His recent scholarship has examined how generative AI challenges traditional assumptions about authorship, misconduct, assessment, and institutional responsibility. Across his professional and scholarly work, David emphasises clarity, accountability, and responsible implementation. He is especially interested in how institutions can adopt emerging technologies without weakening due process, equity, or public trust. His writing often bridges technical, legal, ethical, and operational perspectives, making complex issues accessible to decision makers who must act under uncertainty. David holds a PhD in higher education administration from the University of Nebraska – Lincoln. He has contributed to research and applied policy discussions involving aviation, education, and emerging technology. His current work focuses on helping institutions use AI responsibly while preserving the human judgment essential to fair and defensible decisions across academic, operational, public sector, and policy contexts, both nationally and internationally.
Citation
Ison, David (2026, September 1). Probability is not proof: Why AI detection differs from plagiarism detection in academic misconduct cases. In the Advances in Online Education: A Peer-Reviewed Journal, Volume 5, Issue 1. https://doi.org/10.69554/CHSD5714.
Publications LLP