Real-Time Exam Monitoring and AI Usage Detection 

  The increasing use of digital systems in educational assessments has led to a need for advanced monitoring tools to ensure academic integrity during exams. This paper presents a real-time exam monitoring software system designed to track and detect suspicious activities, such as unauthorized copy-paste actions, the use of AI tools, suspicious typing patterns, and changes in active windows.

     The system is implemented using Python and integrates libraries such as smtplib, pyperclip, psutil, pynput, and Pillow. It features multiple monitoring modules: clipboard monitoring, AI tool detection, active window monitoring, suspicious typing detection & system information gathering.

     The system is designed with built-in email integration to send real-time alerts, attaching screenshots and relevant metadata to ensure prompt responses to potential violations. It employs multi-threading to simultaneously execute multiple monitoring tasks without performance degradation.

     This software can be applied in online exam settings to prevent cheating and maintain academic integrity. While it offers powerful monitoring capabilities, ethical considerations and compliance with privacy regulations must guide its deployment to prevent misuse and ensure respect for students' rights.

     This study emphasizes the role of automated monitoring systems in ensuring fair assessment practices, combining real-time activity tracking with effective alert mechanisms to safeguard the integrity of digital examinations.