Personalized story reading enhances child literacy by aligning content with individual interests, backgrounds, and developmental needs. However, implementing such systems presents challenges, including data privacy concerns, the need for culturally diverse materials, limited resources, and balancing personalization with standardized benchmark objectives. To address these challenges, we introduce StoryLab, a multimodal system designed for K-2 teachers and students. The system leverages advanced generative AI to integrate students’ personal interests with teacher-defined learning objectives to generate comprehensive learning materials, including story text, illustrated figures, vocabulary support, and a consistent narrative voice. A teacher-in-the-loop design ensures pedagogical alignment and trust. Evaluations demonstrate StoryLab’s effectiveness and usability, positioning it as a promising and scalable tool for personalized literacy instruction.