The rapid expansion of e-learning and distance education has posed significant challenges to ensuring academic integrity and fairness in electronic assessments. Traditional exam monitoring methods, which rely heavily on human supervision, have shown limited effectiveness in detecting sophisticated forms of academic dishonesty, particularly in large-scale online examination environments. As a result, higher education institutions have increasingly turned to artificial intelligence (AI)-based proctoring systems to enhance the security and credibility of online examinations. This study aims to examine the role of AI-driven exam monitoring systems in ensuring academic integrity, with a comparative analysis between traditional monitoring methods and intelligent AI-based proctoring solutions. The study highlights how advanced techniques such as machine learning, computer vision, and behavioral analytics enable real-time detection of abnormal student behavior. Findings indicate that AI-based monitoring systems offer greater accuracy, scalability, and objectivity than traditional approaches. Despite their technical advantages, the adoption of AI-based proctoring systems raises critical ethical and legal concerns regarding data privacy, personal data protection, algorithmic transparency, and potential bias in automated decision-making.