Workshops Archive

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

Workshop: 12th SC Workshop on Best Practices for HPC Training and Education

Authors: Charlie Dey and Susan Lindsey (Texas Advanced Computing Center (TACC); University of Texas, Austin)

Abstract: By centering the workshop on a single, richly structured dataset, participants gained technical skills while developing deeper data intuition and problem-solving abilities across the AI/ML pipeline. Moving seamlessly from data familiarization to feature engineering, model selection, and evaluation, learners explored how algorithmic choices interact with dataset characteristics and research questions. The integrated hackathon reinforced these concepts, allowing teams to pose their own questions, identify necessary features, select appropriate models, and iterate on solutions within a realistic, end-to-end workflow. This continuity reduced cognitive load, encouraged reflection on successes and failures, and highlighted the trade-offs inherent in different analytical approaches. Together, these outcomes demonstrate how a project-based, single-dataset framework fosters holistic understanding, preparing participants to apply AI/ML methods thoughtfully and effectively. This approach sets the stage for discussing the broader novelty and pedagogical impact of the workshop.

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