Artificial intelligence, large language models (LLMs), and machine learning (ML) are reshaping research across disciplines. This two-day workshop offers humanities and social science researchers a clear, accessible introduction to contemporary AI systems: what they are, how they differ from traditional computing, what they can—and cannot—do, and how to critically assess their outputs. Through guided demonstrations and hands-on exercises participants will gain a grounded understanding of the concepts behind today’s AI tools.

Day 1 focuses on conceptual foundations and hands-on exploration with off-the-shelf tools. Participants will see how training data, model limitations, and evaluation methods shape AI behavior, and will practice identifying common failure modes. Day 1 also introduces practical ways to use LLMs to support programming and technical work as a bridge to the more hands-on workflows of Day 2.

Day 2 shifts toward programming and applied workflows using open-source Python tools and pre-trained models (e.g., via Hugging Face), with an emphasis on choosing appropriate tasks, understanding what a model is doing, and evaluating results.

A self-paced online introduction to Python will be available for participants who want to learn or brush up on programming basics between the two workshop days.