AI has opened research territory that used to be out of reach for most humanists. We work with UNM faculty and students to explore it, from a first question to a finished project.
Work that once required a team of technical specialists is now within reach of a single researcher or a class. You can explore patterns across thousands of pages of archival material without writing code. An oral history project can have searchable draft transcripts of its whole collection in days rather than months. A student can build an interactive archive as a course project. That changes not just what’s accessible, but which questions are worth asking in the first place.
None of this happens on its own. AI output needs checking, workflows need designing, and you remain responsible for the question, the evidence, and the interpretation. We work with you to build AI-assisted workflows that keep you in the editorial seat.
Exploring patterns across texts. You have hundreds of newspaper articles, letters, government documents, or literary works. AI can help you identify recurring themes, trace how language shifts over time, compare rhetorical strategies, or surface connections you might not have found through close reading alone. You still decide what the patterns mean and which ones matter—but you can now see across a collection at a scale that changes what’s possible to ask.
Transcribing and searching oral histories. You have hours—maybe dozens of hours—of recorded interviews. AI can transcribe them quickly and let you search across the full collection: every mention of a place, a practice, a name, an emotion. The transcripts need human review (AI stumbles on names, accents, and specialized terms), but starting from a draft rather than silence saves enormous time and makes large collections usable in ways they weren’t before.
Analyzing images and visual collections. You’re working with photographs, artworks, maps, or material objects. AI can help you identify visual patterns, compare compositions, tag and categorize at scale, or generate descriptions that make visual collections searchable by text. Those descriptions need checking, especially where cultural context matters. For digital exhibits, AI can also help you create or manipulate images to support your argument or narrative.
Research assistance and literature mapping. Starting a new project or entering an unfamiliar field? AI can help you map the intellectual landscape—identifying key debates, summarizing major positions, suggesting search terms, and pointing toward sources you might not have found on your own. Think of it as a well-read but unreliable research assistant: useful for orientation, always in need of verification. It will sometimes invent books and articles that don’t exist, so check every citation.
Structuring messy data. Archival material often arrives in inconsistent formats—variant spellings, mixed date formats, incomplete records. AI can help normalize and structure that data so you can actually analyze it, map it, or visualize it. The results need spot-checking, but this unglamorous work is often what makes a digital project possible.
Thinking and writing tools. You have a rough argument and want to pressure-test it. AI can help you identify gaps in your reasoning, suggest counterarguments, reframe your claims for different audiences, or help you restructure a draft. This isn’t about AI writing for you—it’s about using AI to think more rigorously about what you’re trying to say.
We document what we learn from projects like these in the AI Sketchbook: what worked, what didn’t, and where the edges are.
We want to be part of the research conversation early, not brought in for technical cleanup at the end. Come with a half-formed question, a dataset you’ve never figured out how to approach, or a sense that your materials could do more than they currently do. We’ll think with you about what’s possible, design a workflow that fits your materials and questions, iterate together, and get the work into a form that can be shared and preserved.
We start where you are. Many collaborations begin with something technical and low-stakes: building or updating a website, formatting documents, troubleshooting a project structure. Technical work with clear feedback loops—you can see whether the font changed, whether the site builds—is a good way to get comfortable working with AI before the intellectual stakes are higher. Once that comfort is established, the step into research workflows feels much smaller.
Class projects. With some guidance, students can use AI to build things that used to require significant coding skills: searchable archives, interactive timelines, annotated maps, text analysis projects, even small web applications that present research to real audiences. We work with instructors to design class projects where students direct the work, evaluate what AI produces, and take responsibility for the argument.
Interdisciplinary and community collaborations. The questions humanists ask rarely stay within departmental lines, and neither do we. If you’re working with colleagues from another field, or with a community partner whose history and materials deserve scholarly attention, we want to hear about it. Oral history collections in community hands can become searchable archives, and materials sitting in folders can be analyzed, published, and shared.
Sensitive materials. Community oral histories, Indigenous collections, unpublished archives, and other sensitive materials shouldn’t go into whatever AI tool happens to be convenient. Talk with us before uploading anything. We’ll help you think through who has a stake in the material, what permissions matter, and which tools fit, including the local AI workstation we’re building.
The goal of working with AI isn’t to hand off intellectual work. It’s to expand what you can do and ask—and to build the judgment to know when AI is helping and when it’s misleading you.
Faculty who work through AI-assisted research projects develop the kind of critical fluency that shapes how they design courses and advise students. Students leave with skills and confidence that transfer well beyond a single assignment. And the documented workflow can itself count as a research output, not just the article or exhibit at the end.
That judgment matters because AI has real limits. It can be confidently wrong, it sometimes invents sources, it reflects the gaps and biases in its training data, and it flattens nuance. Humanists are well equipped to catch these problems, and the field needs them to.
You don’t need a technical background. You don’t need a fully formed project. You don’t even need to know which AI system to use. A question like “Could AI help me with this?” is enough, and we’ll work through the rest together.
Drop by studio hours. Bring your laptop, or use ours. Bring a question, a dataset, a hunch. We’ll explore it together.
Book a consultation. If you’d rather talk through your project before diving in, book a consultation and we can figure out whether AI fits, how it fits, and what the project might become.
Email us. amaranth@unm.edu. Even a one-line question is a great place to start.