About These Materials
The materials on this site grew out of a series of in-person workshops for humanities and social science researchers, designed and taught by the author, with James Capobianco and Tracy Stuber, as part of the programming of the Digital Scholarship Group at Harvard University. The idea for the workshops first came up in September 2025, in conversations with Gio DiRusso and Andy Chen, about how AI was being discussed at Harvard, and the need for a better-informed approach.
The author later taught a redesigned version as a seminar at the United States Holocaust Memorial Museum. The materials from that seminar were then adapted for self-study, as the three units on this site.
Acknowledgements
- The letters were transcribed by the contributors to Wikisource.
- Jeremy Guillette scraped the letters from Wikisource, and split and parsed them into text files and metadata, in the
stevenson_lettersrepository, created for an earlier workshop on web scraping. - Place coordinates come from OpenStreetMap via the Nominatim geocoder (© OpenStreetMap contributors, ODbL).
- The case study uses the named-entity recognition models
Davlan/bert-base-multilingual-cased-ner-hrlanddslim/bert-base-NER, via Hugging Face Transformers. - Unit 1 uses Teachable Machine (Google) and Hack Merlin, Unit 2 uses Gemini Notebook (Google). These are unaffiliated, third-party services.
AI Assistance
These materials were prepared with the help of an AI assistant (Claude, by Anthropic), used to edit an early draft of the text for clarity and to test code. The author reviewed and corrected all of it, and the design, the arguments, the sources, and the final content are the author’s own.
Feedback
If you find an error, a broken link, or instructions that no longer match a tool, please open an issue on GitHub (you’ll need a free GitHub account). The third-party tools used in the activities change often, so reports of outdated steps are especially welcome.
Licence
- Code is licensed under the MIT License.
- Written materials are licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). You may share and adapt them for any purpose, including in your own teaching, as long as you give appropriate credit.
- Data in
data/comes from other sources with their own terms, see the data README.
Citation
If you use or adapt these materials, please cite them as:
Pizzorno, Gabriel H. Understanding AI: A Practical Workshop for Humanities and Social Science Researchers. Version 1.0.0. Zenodo, 2026. https://doi.org/10.5281/zenodo.23324651