An Explainable Neuro-Symbolic Framework for Online Exam Cheating Detection
Yazarlar (2)
Prof. Dr. Turgut ÖZSEVEN Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Beyza Esin Özseven Republic Of Turkey Ministry Of National Education, Türkiye
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Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Applied Sciences Switzerland (Q2) | ||
| Dergi ISSN | 2076-3417 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2026 |
| Cilt / Sayı / Sayfa | 16 / 6 / 1–22 | DOI | 10.3390/app16062884 |
| Makale Linki | https://doi.org/10.3390/app16062884 | ||
| UAK Araştırma Alanları |
Bulanık Mantık
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| Özet |
| With the proliferation of online examination systems, protecting academic integrity and reliably detecting cheating have become significant research problems. Current AI-based online monitoring systems can achieve high accuracy by analyzing visual behavioral cues; however, their often black-box nature limits their explainability, reliability, and legal compliance (e.g., GDPR). In contrast, while rule-based approaches are interpretable, they are insufficient for generalizing complex and ambiguous human behaviors. This study proposes an explainable neuro-symbolic framework combining data-driven learning with symbolic reasoning for cheating detection in online exams. The proposed framework comprises three main layers: a neural perceptron layer that generates a suspicious behavior score; a symbolic reasoning layer comprising ANFIS and ILP methods to increase explainability and manage ambiguity; and a neuro-symbolic fusion layer that integrates these two layers. The success of the proposed framework for plagiarism detection was evaluated using a dataset containing visual--behavioral features such as gaze behavior, head pose, hand-object interaction, and device usage, along with the XGBoost method at the neural perceptron layer. Experimental results show that the proposed approach achieves high detection success and supports decision-making using logical rules, thereby reducing false positives. In this respect, the study offers an ethical, transparent, and reliable solution for online exam security. |
| Anahtar Kelimeler |
| academic integrity monitoring | Adaptive Neuro-Fuzzy Inference System | behavioral analytics | confidence-based fusion | Inductive Logic Programming | logical constraint learning |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 1 |
| Google Scholar | 1 |