AI tools have gotten remarkably good at working with text, images, and data—and remarkably accessible to people without technical backgrounds. You don’t need to write code or understand machine learning. If you can describe what you want to explore, you can start using AI to do it.
That’s a real shift for humanities research and teaching. It means a historian can ask questions across thousands of pages of archival material. A literature class can explore patterns across an entire genre. An oral history project can make decades of recordings searchable in an afternoon. A student can build an interactive archive that would have taken a development team a few years ago.
We’re here to help you figure out where AI fits into your work—and just as importantly, where it doesn’t. The skills you’ve spent years developing—evaluating sources, recognizing bias, attending to what’s missing from an account—are exactly what makes AI use meaningful rather than mechanical. You’re better positioned for this than you might think. Drop by studio hours, bring a project idea or just a question, and we’ll explore it together.
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 flag passages that connect in unexpected ways. You still decide what the patterns mean and which ones matter—but you can now see across a collection in ways that close reading alone can’t achieve.
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 set, finding every mention of a place, a practice, a name, or 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 a collection of 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. For digital exhibits, AI can also help you create or manipulate images to support your argument or narrative.
Research assistance and literature mapping. You’re 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 you toward sources you might not have found on your own. Think of it as a well-read but unreliable research assistant: useful for orientation, but always in need of verification.
Structuring messy data. You have archival material in inconsistent formats—variant spellings, mixed date formats, incomplete records. AI can help normalize and structure this data so you can actually analyze it, map it, or visualize it. This is often the unglamorous work that 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.
Student projects that would have been impossible. With some guidance, students can use AI to build things that previously required coding skills: searchable archives, interactive timelines, annotated maps, text analysis projects, even simple web applications that present their research in public-facing ways. The technical ceiling for humanities projects is genuinely higher than it was two years ago.
It’s worth being honest about this, because the hype around AI tends to skip these parts.
AI doesn’t understand your material. It processes patterns in language and data, but it has no sense of historical context, cultural significance, or why something matters. It will give you a confident answer that’s completely wrong. It will summarize a complex debate into a tidy paragraph that loses everything important. It doesn’t know what it doesn’t know.
AI fabricates sources. This is not a rare glitch—it’s a fundamental feature of how these tools work. If you ask AI for citations, it will sometimes invent books and articles that don’t exist, complete with plausible authors and publication details. Always verify.
AI reflects the biases in its training data. The material these models learned from is disproportionately English-language, Western, recent, and digitized. Marginalized perspectives, non-Western traditions, oral cultures, and anything that wasn’t well-represented online will be underrepresented or distorted in AI outputs. For humanities work—where these gaps and biases are often exactly what you’re studying—this matters a lot.
AI flattens nuance. It’s very good at producing clear, confident, well-structured text. It’s bad at ambiguity, contradiction, irony, and the kind of complexity that makes humanistic inquiry interesting. If you use AI to summarize, you’ll get a summary—but you may lose what made the original worth reading.
None of this means you shouldn’t use AI. It means you should use it the way you’d use any powerful but imperfect tool: with clear eyes, critical judgment, and a humanist’s sense of what matters. We can help you develop that workflow.
Recognizing these limitations clearly, and being able to explain them to students and colleagues, is itself a humanistic skill. Faculty and students who develop this critical fluency aren’t just protecting their own scholarship—they become people who can help their institutions and communities navigate AI more thoughtfully. That’s part of what we work on at Amaranth.
You don’t need any technical background. You don’t need a fully formed project. You don’t even need to know which AI tools exist. If you have a question—“could AI help me with X?”—that’s enough.
Drop by studio hours. Tuesdays & Thursdays 9:30–11:00 and 12:30–2:00, Wednesdays 10:00–12:00. 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 and how AI fits.
Email us. amaranth@unm.edu. Even a one-line question is a great place to start.
Read about our approach. AI Fluency explains how we help people build critical AI practice through digital humanities work—and why starting with a website is a better on-ramp than you’d expect.