Crafting Culturally Aligned Narratives: Large Language Models for Arabic Children's Story Generation

Traditional storytelling plays a crucial role in child development and cultural transmission, fostering imagination, empathy, and moral understanding of moral values. This is particularly true in the Arab culture, where oral and written narratives have long served as tools for transmitting cultural heritage and ethical frameworks. Despite its importance, the computational generation of culturally and morally aligned Arabic children's stories remains an underexplored area. To address this gap, we present a novel system for Arabic story generation that leverages Large Language Models (LLMs) with an integrated cultural alignment mechanism. Our primary goal is to produce engaging narratives that are not only linguistically coherent but also deeply rooted in Arab cultural and moral frameworks. For development and training, we introduce a custom dataset of 714 Arabic children's stories, meticulously annotated for age ranges, moral lessons, and thematic topics. We fine-tuned several LLMs, including Noon, Jais, SILMA, and Gemini 2.0, to assess their capabilities. The effectiveness of our approach was rigorously evaluated through both automated metrics and expert human assessments, with a focus on cultural and moral alignment as core design goals. Our results demonstrate the strong potential of our system in generating linguistically coherent, age-appropriate, and culturally relevant stories. This work not only contributes a novel resource and benchmark for Arabic Natural Language Processing but also highlights the significant role of LLMs in creating impactful Arabic educational content.