This study explores the potential to enhance the performance of convolutional neural networks (CNNs) for automated scoring of kinematic graph answers through data augmentation using Deep Convolutional Generative Adversarial Networks (DCGANs). By developing and fine-tuning a DCGAN model to generate high-quality graph images, we explored its effectiveness compared to other models. The augmented dataset was used to train the CNN model, and "k"-fold cross-validation demonstrated performance improvements. To ensure data quality, defective images generated by the DCGAN were identified and filtered using the CNN model before augmentation. We compared two approaches--augmenting data with and without filtering--and found superior performance when defective images were removed. Additionally, an analysis of the amount of augmented data used revealed a point at which further augmentation no longer significantly improved performance. These findings underscore the importance of specialized DCGAN models and careful dataset curation in improving automated graph scoring systems.