In a world increasingly shaped by data, developing data literacy at an early age is essential. This paper introduces a visionary framework that reimagines how primary school students can develop data literacy through AI-generated incremental and adaptive data storytelling (IADS). Teachers leverage a User-Rendered Context-Augmented (URCA) AI model to craft personalised, evolving educational narratives that align with classroom context and student progress. These data-driven stories adapt over time to reinforce misunderstood concepts and scaffold increasingly complex data ideas, transforming learning into a dynamic, inclusive, and emotionally engaging experience. By uniting generative AI with pedagogically informed narrative design, we propose a novel path for technology-enhanced learning that fosters accessibility, inclusion, and deeper understanding. This paper sets the foundation for a new generation of AI-driven educational tools and outlines future directions for empirical validation, with the potential to reshape how we teach data, support teachers, and bridge educational divides.

AI-Generated Incremental Adaptive Data Storytelling for Young Learners

Buzzi, Marina;Leporini, Barbara;Duca, Angelica Lo;Rotelli, Daniela
2025-01-01

Abstract

In a world increasingly shaped by data, developing data literacy at an early age is essential. This paper introduces a visionary framework that reimagines how primary school students can develop data literacy through AI-generated incremental and adaptive data storytelling (IADS). Teachers leverage a User-Rendered Context-Augmented (URCA) AI model to craft personalised, evolving educational narratives that align with classroom context and student progress. These data-driven stories adapt over time to reinforce misunderstood concepts and scaffold increasingly complex data ideas, transforming learning into a dynamic, inclusive, and emotionally engaging experience. By uniting generative AI with pedagogically informed narrative design, we propose a novel path for technology-enhanced learning that fosters accessibility, inclusion, and deeper understanding. This paper sets the foundation for a new generation of AI-driven educational tools and outlines future directions for empirical validation, with the potential to reshape how we teach data, support teachers, and bridge educational divides.
2025
9783032164476
9783032164483
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1365947
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