Integrating Urban Mining Concepts Through AI-Generated Storytelling and Visuals: Advancing Sustainability Education in Early Childhood
Ruei-Shan Lu, Hao-Chiang Koong Lin, Yong-Cih Yang, Yo-Ping Chen
Abstract
This study investigates integrating sustainability and urban mining concepts into early childhood education through AI-assisted storytelling and visual aids to foster environmental awareness. Using ChatGPT-generated narratives and AI-drawn visuals, interactive stories explore complex sustainability themes like resource conservation and waste management. A quasi-experimental design with 60 preschoolers divided into experimental and control groups compared structured and unstructured storytelling. Structured stories followed teacher-designed frameworks, including thematic and narrative elements such as settings, character development, and resolutions. Observations showed the structured group demonstrated greater comprehension, engagement, and narrative ability, indicating enhanced cognitive and communication skills. The digital system interface featured animations and images for engagement, while tutorial-driven navigation allowed young learners to interact freely with sustainability-focused story options. The findings highlighted structured storytelling’s ability to improve language and narrative skills, alongside fostering digital and environmental literacy. Limitations include a small sample size and a focus on specific themes, restricting generalizability. Despite this, this study adds value by showcasing how AI tools combined with structured frameworks can effectively teach sustainability while reducing the reliance on paper, promoting sustainable educational practices. Overall, this research underscores the potential of AI storytelling in shaping young learners’ understanding of environmental issues, advocating for the thoughtful integration of technology to inspire deeper learning.