DETECTING AI-ASSISTED CHEATING IN ONLINE EXAMS THROUGH BEHAVIOR ANALYTICS.
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| Title: | DETECTING AI-ASSISTED CHEATING IN ONLINE EXAMS THROUGH BEHAVIOR ANALYTICS. |
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| Authors: | Akçapınar, Gökhan1 |
| Source: | Proceedings of the IADIS International Conference on Cognition & Exploratory Learning in Digital Age. 2025, p401-404. 4p. |
| Subject Terms: | *Academic fraud, *Test scoring, K-means clustering, Clustering algorithms, Deception, Pattern perception |
| Abstract: | AI-assisted cheating has emerged as a significant threat in the context of online exams. Advanced browser extensions now enable large language models (LLMs) to answer questions presented in online exams within seconds, thereby compromising the security of these assessments. In this study, the behaviors of students (N = 52) on an online exam platform during a proctored, face-to-face exam were analyzed using clustering methods, with the aim of identifying groups of students exhibiting suspicious behavior potentially associated with cheating. Additionally, students in different clusters were compared in terms of their exam scores. Suspicious exam behaviors in this study were defined as selecting text within the question area, right-clicking, and losing focus on the exam page. The total frequency of these behaviors performed by each student during the exam was extracted, and k-Means clustering was employed for the analysis. The findings revealed that students were classified into six clusters based on their suspicious behaviors. It was found that students in four of the six clusters, representing approximately 33% of the total sample, exhibited suspicious behaviors at varying levels. When the exam scores of these students were compared, it was observed that those who engaged in suspicious behaviors scored, on average, 30-40 points higher than those who did not. Although further research is necessary to validate these findings, this preliminary study provides significant insights into the detection of AI-assisted cheating in online exams using behavior analytics. [ABSTRACT FROM AUTHOR] |
| Copyright of Proceedings of the IADIS International Conference on Cognition & Exploratory Learning in Digital Age is the property of International Association for Development of the Information Society (IADIS) 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.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 190457796 AccessLevel: 6 PubType: Conference PubTypeId: conference PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: DETECTING AI-ASSISTED CHEATING IN ONLINE EXAMS THROUGH BEHAVIOR ANALYTICS. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Akçapınar%2C+Gökhan%22">Akçapınar, Gökhan</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Proceedings+of+the+IADIS+International+Conference+on+Cognition+%26+Exploratory+Learning+in+Digital+Age%22">Proceedings of the IADIS International Conference on Cognition & Exploratory Learning in Digital Age</searchLink>. 2025, p401-404. 4p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Academic+fraud%22">Academic fraud</searchLink><br />*<searchLink fieldCode="DE" term="%22Test+scoring%22">Test scoring</searchLink><br /><searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Deception%22">Deception</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+perception%22">Pattern perception</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: AI-assisted cheating has emerged as a significant threat in the context of online exams. Advanced browser extensions now enable large language models (LLMs) to answer questions presented in online exams within seconds, thereby compromising the security of these assessments. In this study, the behaviors of students (N = 52) on an online exam platform during a proctored, face-to-face exam were analyzed using clustering methods, with the aim of identifying groups of students exhibiting suspicious behavior potentially associated with cheating. Additionally, students in different clusters were compared in terms of their exam scores. Suspicious exam behaviors in this study were defined as selecting text within the question area, right-clicking, and losing focus on the exam page. The total frequency of these behaviors performed by each student during the exam was extracted, and k-Means clustering was employed for the analysis. The findings revealed that students were classified into six clusters based on their suspicious behaviors. It was found that students in four of the six clusters, representing approximately 33% of the total sample, exhibited suspicious behaviors at varying levels. When the exam scores of these students were compared, it was observed that those who engaged in suspicious behaviors scored, on average, 30-40 points higher than those who did not. Although further research is necessary to validate these findings, this preliminary study provides significant insights into the detection of AI-assisted cheating in online exams using behavior analytics. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Proceedings of the IADIS International Conference on Cognition & Exploratory Learning in Digital Age is the property of International Association for Development of the Information Society (IADIS) 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: 4 StartPage: 401 Subjects: – SubjectFull: Academic fraud Type: general – SubjectFull: Test scoring Type: general – SubjectFull: K-means clustering Type: general – SubjectFull: Clustering algorithms Type: general – SubjectFull: Deception Type: general – SubjectFull: Pattern perception Type: general Titles: – TitleFull: DETECTING AI-ASSISTED CHEATING IN ONLINE EXAMS THROUGH BEHAVIOR ANALYTICS. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Akçapınar, Gökhan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2025 Type: published Y: 2025 Titles: – TitleFull: Proceedings of the IADIS International Conference on Cognition & Exploratory Learning in Digital Age Type: main |
| ResultId | 1 |
