arXiv subjects

Gökhan Akçapınar

Publications and source records attributed to Gökhan Akçapınar.

2 recordsLinked to original sources

arXiv2026

Early Prediction of AI-Assisted Cheating Risk in Online Exams Through Learning Analytics

AI-assisted cheating has become an important threat to the security of online exams. This study examines whether the risk of AI-assisted cheating in the final exam can be predicted using students' digital traces in the learning management system (LMS) during the first eight weeks of the semester. The sample comprised 52 first-year undergraduates enrolled in a bachelor's program in Computer Education and Instructional Technology and taking an Introduction to Programming course at a public university in Turkiye. Students were labeled as low- or high-risk based on suspicious behaviors recorded in the final-exam logs, including copy, focus-loss, and right-click events. Of the 52 students, 23 (44.2%) were labeled as high-risk in a proctored, face-to-face exam. Group membership was then predicted using five features selected from 27 candidates extracted from students' digital traces. Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting algorithms were used to build the prediction models. Model performance was evaluated using leave-one-out cross-validation (LOOCV) with fold-specific preprocessing and feature selection. Logistic Regression achieved the best performance (Accuracy = 73.1%). The results indicate that LMS interaction data can provide an early signal of AI-assisted cheating risk. Course-module views, assignment submissions, and the number of days on which course videos were accessed were the most consistently selected features across the LOOCV folds. These predictions are intended to support timely academic guidance, not to establish misconduct or initiate disciplinary action.

cs.CY↗

arXiv2025

Detecting AI-Assisted Cheating in Online Exams through Behavior Analytics

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.

cs.HC↗