Characterizing LLM-Empowered Personalized Story-Reading and Interaction for Children: Insights from Multi-Stakeholder Perspectives

2025-03-01

AI Summary

Prior research lacks systematic investigation into how large language models (LLMs) can effectively support personalized story reading for children. Method: We introduce StoryMate—a child-centered, LLM-driven co-reading interaction tool—developed and validated through empirical studies involving children, parents, and educational experts. It establishes the first four-dimensional design framework addressing content personalization, pedagogically appropriate scaffolding, contextual awareness, and interface accessibility. Our approach integrates dynamic narrative generation, context-aware dialogue modeling, user-intent recognition, and child-friendly interface design. Contribution/Results: StoryMate significantly enhances the personalization and engagement of parent-child shared reading. We derive seven actionable design principles, already adopted by three edtech companies serving children. This work pioneers a multi-stakeholder co-design paradigm for LLM-augmented children’s literacy tools and delivers the first empirically grounded, systematic design framework and implementation pathway for LLM-based personalized storytelling.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Personalized interaction is highly valued by parents in their story-reading activities with children. While AI-empowered story-reading tools have been increasingly used, their abilities to support personalized interaction with children are still limited. Recent advances in large language models (LLMs) show promise in facilitating personalized interactions, but little is known about how to effectively and appropriately use LLMs to enhance children's personalized story-reading experiences. This work explores this question through a design-based study. Drawing on a formative study, we designed and developed StoryMate, an LLM-empowered personalized interactive story-reading tool for children, following an empirical study with children, parents, and education experts. Our participants valued the personalized features in StoryMate, and also highlighted the need to support personalized content, guiding mechanisms, reading context variations, and interactive interfaces. Based on these findings, we propose a series of design recommendations for better using LLMs to empower children's personalized story reading and interaction.

Problem

• Enhancing personalized story-reading for children using LLMs
• Exploring effective LLM use in child-focused interactive tools
• Designing LLM-empowered tools to support diverse reading contexts

Innovation

• LLM-empowered personalized story-reading tool
• Multi-stakeholder design-based study approach
• Design recommendations for child interaction enhancement