@article{4617,
  author = {Qian Yang,  Guoqiang Li},
  title = {AI-Driven Electronic Vision for Formative Classroom Educational Assessment},
  journal = {Journal of Electronic Systems},
  year = {2025},
  volume = {15},
  number = {4},
  doi = {https://doi.org/10.6025/jes/2025/15/4/187-194},
  url = {https://www.dline.info/jes/fulltext/v15n4/jesv15n4_1.pdf},
  abstract = {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.},
}