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This paper explores the role of object detection technologies, particularly using YOLO architecture, in education and assessment contexts. It highlights their applications for automation and improved accuracy in evaluations, discusses challenges like detecting small objects, and offers recommendations for enhancing model robustness. Additionally, insights into future trends and implementation strategies for educational advancements are provided.