Workflow experiments, analysis techniques, and methodological notes: ways AI has proven useful, uneven, or surprisingly limited in actual scholarship.
How to read these
Treat each research sketch as a field note from an experiment. The point is not that a tool solved the problem, but what it made possible, what it got wrong, and what expertise was still needed around archives, metadata, data ethics, accessibility, privacy, method, or research computing.
ExperimentTo create an AI agent to work with Gemini and Claude to bulk process 300 images of archival documents and enable full-text search of medieval handwriting.
Results
ExperimentThis sketch demonstrates how to construct a prompt for a high quality annotated bibliography.
ResultsExperimentCreate an interactive map with pins for hundreds of photos, using GPS metadata already embedded in your phone's images — in under an hour.
Results
ExperimentThis sketch shows the difference in response quality baseed on the LLM model tier.
Results
ExperimentWith one prompt get entries in Zotero from a book of essay chapters.
Results
ExperimentAI can transform a historical line drawing into a 3D-printable file, adding a tactile dimension to research that images alone can't provide.
Results
ExperimentAsk AI to review a draft article separately as each of the specific readers it will actually face — subfield expert, adjacent specialist, volume editor, fellow contributor.
Results