False Positives in AI Writing Detection: A Small-Scale Empirical Study Using Authentic Filipino Student Essays
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| Title: | False Positives in AI Writing Detection: A Small-Scale Empirical Study Using Authentic Filipino Student Essays |
|---|---|
| Language: | English |
| Authors: | Mhel Cedric D. Bendo (ORCID |
| Source: | Online Submission. 2026. |
| Peer Reviewed: | N |
| Page Count: | 8 |
| Publication Date: | 2026 |
| Document Type: | Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Essays, Student Writing Models, Undergraduate Study, Foreign Countries, English (Second Language), Non English Speaking, Writing Evaluation, Test Bias, Computer Managed Instruction, Reliability, Automation, Artificial Intelligence |
| Geographic Terms: | Philippines |
| Abstract: | This research note reports a small-scale exploratory study into how two AI writing detectors, ZeroGPT and Copyleaks, classify authentic student essays. A total of ten anonymised college-student essays were analysed to observe misclassification patterns, particularly false positives, where human-written content has been incorrectly flagged as AI-generated. The assessment was conducted focusing on essays produced by Filipino undergraduates whose nonnative English writing may have features that could lead to misclassification by the detectors. Results show that both detectors inconsistently classified the same set of essays: five essays were labelled as "AI-generated," while the other five were labelled as "Human," when in fact all the texts were authentically written by students. These results point to the potential misclassification risks when AI detection tools are used within educational contexts, where it is commonplace for teachers to make important decisions about academic integrity based on the outputs of detectors. The present study underlines the need to validate the outputs of AI detectors with human judgment and advises educators against the use of these tools as sole evidence of misconduct. Implications for ICT-supported assessment practices and policies of academic honesty are discussed, together with recommendations for a more responsible integration of AI detection tools in educational contexts. The results described above have implications for the practice of academic integrity in all countries, but also specifically for those countries with multilingual student bodies whose writing styles may differ from the writing style used to train the detectors. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | ED681015 |
| Database: | ERIC |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED681015 Name: ERIC Full Text Category: fullText Text: Full Text from ERIC |
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| Items | – Name: Title Label: Title Group: Ti Data: False Positives in AI Writing Detection: A Small-Scale Empirical Study Using Authentic Filipino Student Essays – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mhel+Cedric+D%2E+Bendo%22">Mhel Cedric D. Bendo</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0004-6873-3910">0009-0004-6873-3910</externalLink>) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Online+Submission%22"><i>Online Submission</i></searchLink>. 2026. – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: N – Name: Pages Label: Page Count Group: Src Data: 8 – Name: DatePubCY Label: Publication Date Group: Date Data: 2026 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Reports - Research – Name: Audience Label: Education Level Group: Audnce Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink> – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Essays%22">Essays</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Writing+Models%22">Student Writing Models</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Study%22">Undergraduate Study</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22English+%28Second+Language%29%22">English (Second Language)</searchLink><br /><searchLink fieldCode="DE" term="%22Non+English+Speaking%22">Non English Speaking</searchLink><br /><searchLink fieldCode="DE" term="%22Writing+Evaluation%22">Writing Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Bias%22">Test Bias</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Managed+Instruction%22">Computer Managed Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Reliability%22">Reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink> – Name: Subject Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Philippines%22">Philippines</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This research note reports a small-scale exploratory study into how two AI writing detectors, ZeroGPT and Copyleaks, classify authentic student essays. A total of ten anonymised college-student essays were analysed to observe misclassification patterns, particularly false positives, where human-written content has been incorrectly flagged as AI-generated. The assessment was conducted focusing on essays produced by Filipino undergraduates whose nonnative English writing may have features that could lead to misclassification by the detectors. Results show that both detectors inconsistently classified the same set of essays: five essays were labelled as "AI-generated," while the other five were labelled as "Human," when in fact all the texts were authentically written by students. These results point to the potential misclassification risks when AI detection tools are used within educational contexts, where it is commonplace for teachers to make important decisions about academic integrity based on the outputs of detectors. The present study underlines the need to validate the outputs of AI detectors with human judgment and advises educators against the use of these tools as sole evidence of misconduct. Implications for ICT-supported assessment practices and policies of academic honesty are discussed, together with recommendations for a more responsible integration of AI detection tools in educational contexts. The results described above have implications for the practice of academic integrity in all countries, but also specifically for those countries with multilingual student bodies whose writing styles may differ from the writing style used to train the detectors. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2026 – Name: AN Label: Accession Number Group: ID Data: ED681015 |
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| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 8 Subjects: – SubjectFull: Essays Type: general – SubjectFull: Student Writing Models Type: general – SubjectFull: Undergraduate Study Type: general – SubjectFull: Foreign Countries Type: general – SubjectFull: English (Second Language) Type: general – SubjectFull: Non English Speaking Type: general – SubjectFull: Writing Evaluation Type: general – SubjectFull: Test Bias Type: general – SubjectFull: Computer Managed Instruction Type: general – SubjectFull: Reliability Type: general – SubjectFull: Automation Type: general – SubjectFull: Artificial Intelligence Type: general – SubjectFull: Philippines Type: general Titles: – TitleFull: False Positives in AI Writing Detection: A Small-Scale Empirical Study Using Authentic Filipino Student Essays Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mhel Cedric D. Bendo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Type: published Y: 2026 Titles: – TitleFull: Online Submission Type: main |
| ResultId | 1 |
