A Documentation Catalog for the AI Ecosystem: Design of the ICICLE AI Training Catalog

Volume 17, Issue 2 (November 2026), pp. 39–45

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@article{jocse-17-2-7,
  author={Amit Vyas and Carlos Guzman and Neelima Savardekar and Mary Thomas and Michelle Miller and Alfonzo Morales and Joe Stubbs and Christian Garcia and Beth Plale},
  title={A Documentation Catalog for the AI Ecosystem: Design of the ICICLE AI Training Catalog},
  journal={The Journal of Computational Science Education},
  year=2026,
  month=nov,
  volume=17,
  issue=2,
  pages={39--45},
  doi={https://doi.org/10.22369/issn.2153-4136/17/2/7}
}

As artificial intelligence becomes increasingly important across scientific disciplines, there is a growing need for scalable platforms that support training, knowledge sharing, and workforce development. The ICICLE AI Institute, funded by the National Science Foundation, focuses on democratizing artificial intelligence and cyber infrastructure through open education, community engagement, and shared software infrastructure. This paper presents the design and architecture of the ICICLE Training Catalog and demonstrates how the platform supports broader community engagement and workforce development through practical use cases and user personas. The catalog aggregates educational materials, tutorials, and technical documentation from multiple institutions into a centralized and structured repository. We describe the website design, documentation design principles, and early usage evidence from privacy-preserving Umami analytics. We also discuss how the Training Catalog supports workforce participation development and how it can be leveraged by different user groups, including students, researchers, educators, and research software engineers. Finally, we highlight lessons learned from developing a collaborative training platform across institutions and discuss future directions for expanding its impact.