Academic Journal
Probability is not proof: Why AI detection differs from plagiarism detection in academic misconduct cases.
| Τίτλος: | Probability is not proof: Why AI detection differs from plagiarism detection in academic misconduct cases. |
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| Συγγραφείς: | Ison, David |
| Πηγή: | Advances in Online Education: A Peer-Reviewed Journal; Autumn-Fall2026, Vol. 5 Issue 1, p20-34, 15p |
| Θεματικοί όροι: | False positive error, Procedural justice, Burden of proof, Ethics, Algorithmic bias, Plagiarism prevention, Academic fraud |
| Περίληψη: | 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/. [ABSTRACT FROM AUTHOR] |
| Copyright of Advances in Online Education: A Peer-Reviewed Journal is the property of Henry Stewart Publications LLP and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Probability is not proof: Why AI detection differs from plagiarism detection in academic misconduct cases. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ison%2C+David%22">Ison, David</searchLink> – Name: TitleSource Label: Source Group: Src Data: Advances in Online Education: A Peer-Reviewed Journal; Autumn-Fall2026, Vol. 5 Issue 1, p20-34, 15p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22False+positive+error%22">False positive error</searchLink><br /><searchLink fieldCode="DE" term="%22Procedural+justice%22">Procedural justice</searchLink><br /><searchLink fieldCode="DE" term="%22Burden+of+proof%22">Burden of proof</searchLink><br /><searchLink fieldCode="DE" term="%22Ethics%22">Ethics</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithmic+bias%22">Algorithmic bias</searchLink><br /><searchLink fieldCode="DE" term="%22Plagiarism+prevention%22">Plagiarism prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+fraud%22">Academic fraud</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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/. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Advances in Online Education: A Peer-Reviewed Journal is the property of Henry Stewart Publications LLP and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 20 Subjects: – SubjectFull: False positive error Type: general – SubjectFull: Procedural justice Type: general – SubjectFull: Burden of proof Type: general – SubjectFull: Ethics Type: general – SubjectFull: Algorithmic bias Type: general – SubjectFull: Plagiarism prevention Type: general – SubjectFull: Academic fraud Type: general Titles: – TitleFull: Probability is not proof: Why AI detection differs from plagiarism detection in academic misconduct cases. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ison, David IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Autumn-Fall2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 27551377 Numbering: – Type: volume Value: 5 – Type: issue Value: 1 Titles: – TitleFull: Advances in Online Education: A Peer-Reviewed Journal Type: main |
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