The rapid rise of online education in the context of the pandemic, and following it, has introduced a remarkable change in testing methodology from onsite conventional exams to digital platforms . This shift has indeed offered increased accessibility and scalability, however, there are concerns for fairness, academic integrity and the trust issues . Studies have found that a large number of students have admitted cheating in these online tests, which makes the trust level of online examination lower compared to traditional examination method . Technical problems like unreliable connections and security concerns. In response to these challenges, this work suggests an AI-enhanced remote proctoring framework, which combines multiple modes of monitoring. The design base is essentially: facial recognition for person identification and intruder detection audio analysis for background conversations detection (it includes references ) and behavioral monitoring of facial gazing, head posture, and eye track movements. Furthermore, monitoring screen and tab activity might raise a red flag that something fishy is going on in the digital life Line. Dynamic cheating score measures abnormal behavior and produces automated logs, assisting in decision making of examiners. Acknowledging ethical issues, privacy protection, encryption procedures, and transparent policies as part of the framework in order to alleviate student worries and meet data protection requirements . Fairness-aware AI models implemented to mitigate bias amongst different student groups . Arresting the pendulum between innovation and ethics, this research highlights the promise of sophisticated AI-powered proctoring systems for increasing the credibility, equity and trustworthiness of online assessment. The solution offers institutions a scalable, trusted solution that upholds the integrity of the academic process while treating students with respect.
P. P. Gupta (Fri,) studied this question.