Sketchbook Tags
A way to browse the AI Sketchbook laterally rather than by section. Useful when the pattern you care about is something like writing, source fabrication, archives, or source evaluation rather than whether a sketch started in teaching or research.
Below are the tags currently in use across sketchbook post pages. As the sketchbook grows, this should become a more useful way to move across related ideas.
Showing all sketchbook posts with tags.
AI Integration Ladder
Key questionHow can a course distinguish between different levels of acceptable AI use and make those levels usable on assignments?
What it clarifies- AI use is not binary, but depends on the task
- different levels of AI use require different forms of accountability
- assignment labels are clearest when they say what AI may do, what it may not replace, and what students should make visible
AI Use Notes
Key questionHow can students disclose AI use in a way that supports learning?
What it clarifies- AI use can be documented as part of process
- disclosure should distinguish assistance from substitution
- students are accountable for accepting, rejecting, and revising AI output
Argument Audit
Key questionHow can AI help sharpen writing skills instead of replace them?
ActivityStudents use AI-generated objections to test whether a thesis is vague, vulnerable, or genuinely persuasive.
What students learn- the difference between tone and analytical precision
- what makes an objection substantive vs. generic
- how vague writing produces vague critique
Citation Test
Key questionHow can AI output help students learn scholarly integrity?
ActivityAsk students to verify AI-generated citations so fabricated sources become a concrete lesson about evidence, authority, citation accuracy, and why LLMs can produce sources that sound real but do not exist.
What students learn- why polished prose is not evidence of accuracy
- how hallucination happens and why it's convincing
- verification is a scholarly habit that connects classroom work with library expertise
Differentiate Yourself From AI
Key questionWhat if the focus is on the product, not the process?
What it clarifies- AI use does not remove responsibility for the final work
- generic AI-like work may not provide enough evidence of learning
- students are expected to go beyond what AI can produce for free
Generate 3D Prints from 2D Drawings
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- generated 3D-printable files from 2D historical images
- reconstructed material culture objects for research
- incorporated tactile elements into research presentations
Photos to Map Pins
ExperimentCreate an interactive map with pins for hundreds of photos, using GPS metadata already embedded in your phone's images — in under an hour.
Results- extracted GPS metadata from image files
- built a map visualization with AI-assisted coding
- presented geolocated data in a public-facing format
Pipelines for Medieval Handwriting Recognition
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- built an agentic pipeline for bulk document processing
- combined multiple LLMs to improve transcription accuracy
- enabled full-text search of handwritten archival sources
Remixing Plato
Key questionHow can AI help translate ideas into contemporary culture?
ActivityStudents translate, reshape, or re-perform a Platonic dialogue through AI — then analyze what changed and why.
What students learn- what AI can and cannot preserve in philosophical argument
- how form and genre reshape meaning
- that prompting requires the same clarity as writing
Same Prompt, Different History
Key questionThe same prompt to ChatGPT produces different histories depending on whether you're logged in or not — and that difference is the lesson.
ActivityThe same prompt to ChatGPT produces different histories depending on whether you're logged in or not — and that difference is the lesson.
What students learn- how context shapes historical interpretation
- what filter bubbles look like in practice
- the difference between pronouncing and puzzling about sources
What Does Cilantro Taste Like?
Key questionHow to introduce students to the basics of AI output differences?
ActivityA hands-on demo to show how model size and settings change what AI says — using one simple, relatable question.
What students learn- AI is a spectrum of models, not one fixed thing
- how temperature, token limits, and sampling shape output
- what training data has to do with what a model knows
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