From Cover to Story: AI-Driven Genre Classification and Illustrated Narrative Creation for Children's Literature

Storytelling is a fundamental pillar of childhood development, where visual narratives play a crucial role in enhancing engagement and cognitive processing. While Generative Artificial Intelligence (GAI) has revolutionized content creation, its application for automated story generation from book covers remains largely unexplored. This study presents an innovative pipeline that combines computer vision for genre classification with GAI to create tailored illustrated stories. After evaluating four deep learning architectures widely used in image classification tasks, ConvNeXt-Tiny was selected as the final model, achieving a Weighted F۱-score of ۰.۶۸۹۸ in categorizing children's books into ۱۳ distinct genres through cover image analysis. To address the lack of benchmark datasets, we compiled and rigorously validated a specialized collection of ۴,۰۸۵ Persian children's book covers. The proposed system leverages both cover design elements and predicted genre features within structured prompts to generate coherent illustrated stories through LLMs and image-synthesis models. A sample of ۲۶ generated stories was qualitatively evaluated by three child psychologists based on narrative coherence, genre alignment, age appropriateness, character continuity, and visual congruence. This research makes significant contributions to both Persian literary analysis and AI-driven creative systems, demonstrating how machine learning can enhance educational storytelling while preserving cultural authenticity.

کلیدواژه ها:

Genre Classification ، Narrative Creation ، Deep Learning ، Large Language Model (LLM) ، Generative Artificial Intelligence (GAI) ، Children literature