Bringing AI/ML and HPC to High School Science Teachers Using Jupyter Notebooks – TACCster 2026

James Newland, Kelsey Beavers, Lena Duplechin Seymour, Elaine Anita de Melo Gomes Soares
Texas Advanced Computing Center, University of Texas at Austin

Abstract

Many scientific disciplines have become driven by data science methodology, requiring both statistical thinking and computation. Teachers can learn about the use of HPC, which can be transferred to classroom activities through Python and Jupyter notebooks. The use of Jupyter notebooks as computational essays in teacher professional development allows educators to experiment with and learn about HPC, data science, and machine learning algorithms using Python in various science contexts. Cloud-based platforms exist that allow access to HPC resources like multi-core processing and GPU usage for AI/ML contexts. The Jupyter notebook environment allows narrative and code to exist side by side to introduce learners to HPC computing concepts while also running code blocks that can run models or data visualizations. For those who are new to using computer programming, the notebooks can be written using research-backed computer science scaffolding pedagogy like worked examples, sub-task labeling, and minimally working programs. The use of Jupyter notebooks in educational contexts emulates HPC professional development techniques used with students and early-career scientists, like training sessions held at the Texas Advanced Computing Center (TACC). By using scaffolded techniques for educators built on successful professional learning designed for HPC users, teachers can bring authentic data science and machine learning concepts into their own classrooms. Specific examples will be explored related to biology, astronomy, and usage of LLMs in a computer science education context. The authors will explore HPC concepts like parallel data processing, machine-learning applications, and large data structure usage.

Background

The TACC life sciences team has been providing professional learning for biologists for a while. The Expanding Pathways in Computing team has been doing the same for teachers. This project is an attempt to create teacher professional development content by scaffolding the ML for Life Sciences @ TACC training designed for practicing scientists.

Poster

coming soon

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