Academic integrity continues to face the persistent challenge of examination cheating. Traditional invigilation relies on human observation, which is inefficient, costly, and prone to errors at scale. Although some existing AI-powered monitoring systems have been deployed and trusted, many lack transparency or require multi-layered architectures to achieve the desired performance. To overcome these challenges, we propose an improvement over a simple two-stage framework for exam cheating detection that integrates object detection and behavioral analysis using well-known technologies. First, the state-of-the-art YOLOv8n model is used to localize students in exam-room images. Each detected region is cropped and preprocessed, then classified by a fine-tuned RexNet-150 model as either normal or cheating behavior. The system is trained on a dataset compiled from 10 independent sources with a total of 273,897 samples, achieving 0.95 accuracy, 0.94 recall, 0.96 precision, and 0.95 F1-score - a 13\% increase over a baseline accuracy of 0.82 in video-based cheating detection. In addition, with an average inference time of 13.9 ms per sample, the proposed approach demonstrates robustness and scalability for deployment in large-scale environments. Beyond the technical contribution, the AI-assisted monitoring system also addresses ethical concerns by ensuring that final outcomes are delivered privately to individual students after the examination, for example, via personal email. This prevents public exposure or shaming and offers students an opportunity to reflect on their behavior. For further improvement, it is possible to incorporate additional factors, such as audio data and consecutive frames, to achieve greater accuracy. This study provides a foundation for developing real-time, scalable, ethical, and open-source solutions.

翻译：学术诚信持续面临考试作弊这一持久挑战。传统监考依赖人工观察，效率低下、成本高昂，且在大规模应用中容易出错。尽管现有一些基于人工智能的监控系统已被部署并获得信任，但许多系统缺乏透明度，或需要多层架构才能达到预期性能。为克服这些挑战，我们提出了一种对简单两阶段考试作弊检测框架的改进方案，该方案利用成熟技术整合了目标检测与行为分析。首先，采用最先进的YOLOv8n模型定位考场图像中的学生。每个检测到的区域被裁剪并预处理，然后由微调的RexNet-150模型分类为正常行为或作弊行为。该系统在由10个独立来源汇总而成的数据集上训练，共计273,897个样本，实现了0.95的准确率、0.94的召回率、0.96的精确率和0.95的F1分数——相比视频作弊检测基线准确率0.82提升了13%。此外，该方法的平均推理时间为每样本13.9毫秒，展现出在大规模环境中部署的稳健性和可扩展性。除技术贡献外，该AI辅助监控系统还通过确保考试后以私密方式（例如通过个人电子邮件）将最终结果发送给个别学生，解决了伦理关切。这避免了公开曝光或羞辱，并为学生提供了反思自身行为的机会。为进一步改进，可以整合额外因素（如音频数据和连续帧）以实现更高准确率。本研究为开发实时、可扩展、符合伦理且开源解决方案奠定了基础。