Real-Time AI Integrity: Enhancing Cheating Detection through Neural Network-CNN Data Analysis

Real-Time AI Integrity: Enhancing Cheating Detection through Neural Network-CNN Data Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Real-Time AI Integrity: Enhancing Cheating Detection through Neural Network-CNN Data Analysis Husam Yaseen, Abdel-Aziz Saleh Mohammad, Najwa Ashal, Hesham Abusaimeh, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8263346/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The rising demand for online learning and the high academic reliance on artificial intelligence (AI) have led to the need for the development of an automated system that detects and mitigates instances of student cheating, especially in large settings, to protect the academic process. The dramatic shift towards online learning, especially after COVID-19, has urged the development of such systems to ensure exam integrity. This research aims to develop an advanced Neural Network-CNN framework to detect and predict cheating in online and large-scale lab assessments. The proposed framework aimed to detect cheating through specific measures and patterns depending on students’ actions or responses, such as eye blinking, attention, lip movement, and time-automated data series. This research employed various data inputs through a complex neural network architecture during assessments, and the framework uses a multi-modal approach that analyzes all the interactions of the students and their patterns. The results show that the AI framework reduces up to 41.2% of cheating among n students and can predict cheating up to 35.3% among 100 students in one exam. Artificial Intelligence Neural Network-CNN Data Analysis. Online Examination Exam Cheating Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8263346","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556154300,"identity":"599a8db2-2777-48aa-ae71-e1f3cd7c4f22","order_by":0,"name":"Husam Yaseen","email":"","orcid":"","institution":"Middle East University","correspondingAuthor":false,"prefix":"","firstName":"Husam","middleName":"","lastName":"Yaseen","suffix":""},{"id":556154304,"identity":"0239c93e-03ce-46f7-acd1-1681beb3cc63","order_by":1,"name":"Abdel-Aziz Saleh Mohammad","email":"","orcid":"","institution":"Middle East 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