Teaching AI Through Narrative Data: A Practical Framework for Data Science and Retrieval-Augmented Generation

Volume 17, Issue 1 (March 2026), pp. 50–56

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@article{jocse-17-1-7,
  author={Charlie Dey and Susan Lindsey},
  title={Teaching AI Through Narrative Data: A Practical Framework for Data Science and Retrieval-Augmented Generation},
  journal={The Journal of Computational Science Education},
  year=2026,
  month=mar,
  volume=17,
  issue=1,
  pages={50--56},
  doi={https://doi.org/10.22369/issn.2153-4136/17/1/7}
}

Artificial intelligence (AI) and machine learning (ML) education has traditionally been split between technical model-building and data literacy. While these skills are often taught separately, the emergence of large language models (LLMs) offers an opportunity to unify them through narrative-driven, human-readable data transformation. This approach enables learners to query structured data using natural language while still engaging deeply with the underlying analytical processes. We present a hands-on educational framework, debuting at the 2025 Big Data School in Costa Rica that grounds AI learning in real-world data by transforming a single, richly structured dataset into narrative text that LLMs can ingest and reason over.