Understanding AI: A Practical Workshop for Humanities and Social Science Researchers

Abstract

Teaching materials that introduce researchers in the humanities and social sciences to how contemporary AI systems work, what they can and cannot do, and how to use them critically in research. They start from a single idea: AI models learn patterns from data, and both their usefulness and their characteristic failures follow from it. The materials are organized in three units. Unit 1 (Key Concepts) explains how models learn, and why large language models produce fluent text that can be confidently wrong, with activities in Teachable Machine and Hack Merlin. Unit 2 (Working with LLMs) covers prompting, judging how far output can be trusted, and documenting and acknowledging AI use, with an activity in Gemini Notebook. Unit 3 (Programmatic Approaches) builds a Python pipeline that extracts the people, places, and organizations from 462 letters by Robert Louis Stevenson with pre-trained named-entity recognition models, turns them into charts, a network, a timeline, and a map, and evaluates the results against human annotations. Throughout, the question is not only whether a tool gives a good answer, but whether you could check it, repeat it, and report it. The materials are designed for self-study, and no technical background is assumed. A facilitator guide supports teaching them as in-person workshops, with schedules, notes for each activity, and discussion questions. This deposit is a snapshot of the GitHub repository at release v1.0.0: the website's source (Quarto), the case study notebook and data-preparation scripts, the data, and the facilitator guide. A second file contains the built website, which can be opened locally in a browser. The materials are published as a website at https://gpizzorno.github.io/understanding-ai/. Licences: the written materials are licensed under CC BY 4.0, and the code under the MIT License. The letters are in the public domain, from Wikisource's transcriptions (CC BY-SA 4.0). The place coordinates come from OpenStreetMap via Nominatim (ODbL). See the data README in the deposit for the sources and terms of each data file.