The goal of the paper is to address the linguistic gap faced by young Egyptian Arabic speakers through translating children stories from Modern Standard Arabic to the Egyptian Cairo dialect. Claude is used for initial translation, and a fine-tuned AraT5 model is used for backtranslation. The translation quality is assessed using semantic similarity and BLUE scores to compare the original texts and the translations. The resulting corpus contains 130 stories which were revised by native Egyptian speakers who are professional translators. The strengths of this paper are multiple: working on a less-resourced variety, addressing an important social issue, creating a dataset with potential real-life applications, and ensuring the quality of the produced dataset through human validation.