Journal of Electronic Systems Qian Yang, Guoqiang Li 15 4 2025 https://doi.org/10.6025/jes/2025/15/4/187-194 https://www.dline.info/jes/fulltext/v15n4/jesv15n4_1.pdf The paper explores the application of AI-driven computer vision to enhance formative assessment in classroom settings. It emphasizes how technologies such as face detection, face recognition, human pose estimation, and facial expression analysis can objectively evaluate student engagement, attendance, and emotional states. The authors propose a multi column convolutional neural network architecture combined with sliding window fusion techniques to improve object and scene recognition accuracy. Experimental results on datasets like MNIST, MIT, and SUN397 demonstrate the model’s superior performance, achieving reduced error rates and enhanced generalization. The study concludes that integrating AI and computer vision into teaching evaluation provides richer, real time data for educators, supports pedagogical improvements, and enables more interactive and responsive classroom environments. Despite promising outcomes, challenges remain in data validity and system design, underscoring the need for further research to refine video based evaluation frameworks.