How do you keep up with this after the course ends?
DH Now’s Editors’ Choice items showcase scholarship in various forms that drives the digital humanities field forward: a place to look for cutting-edge examples you might like to adapt, and thoughts on AI that might help you plan for the future.
A key goal is to surface important gray literature (white papers, presentations, research reports, and essays that may not otherwise have a formal venue for publication), making that range of sources more visible.
DH Now in the Classroom
Cohen: curation as pedagogy
Another option is to have students explore: when thinking about what something like Claude Code can make possible, current DH projects provide specific examples and models.
The Guest Editor model makes visible the labor of curation and the decisions that shape what a field pays attention to, which is useful for courses that teach curation.
DH Now also shows the range of things that happen under DH. It has limits (it’s not the entire field, just a pretty decent slice), but it’s got some nice breadth.
Cohen’s project is interested in reorienting toward “small language models and non-generative use cases for AI, to AI that isn’t about producing any kind of text on the fly but on the many other aspects of research and learning.”
— Dan Cohen, “Books, AI, and the Public Good”
He raises some interesting use cases:
What can book-informed AI do for the creation of library metadata and comprehensive search?
How can AI help locate works for careful human reading rather than summarization?
Can AI help with the use of special collections that are not yet indexed?
The Project’s Own Questions
Cohen: limitations he recognizes
Cohen recognizes the project’s own limitations, with key questions like:
How do we sustainably fund such a commons, including a model for long-term maintenance, transformation, and growth of the corpus over time?
How to respect the interests of authors and rightsholders by accounting for concerns about consent, credit, and compensation?
A Model for Thinking About AI Governance
Cohen: who controls the data, who is served?
The example of this grant offers students a useful model for thinking about AI governance: who controls training data, whose interests are served, and what institutional structures produce more equitable outcomes.
The public-interest commons proposal is a concrete example of an alternative to commercial AI development, and the list of stakeholders (authors, publishers, librarians, technologists, scholars, students) helps understand broadly who is impacted.
This is a way to embrace the possibilities of AI while remaining skeptical of AI as a substitute for human creativity (as our students rightly are!).
Is Cohen’s Project a Perfect Solution?
No.
Philanthropy can’t substitute for actual regulation of these companies for the public good.
As we’ve talked about before, knowledge institutions also have biases and limitations.
But it is an interesting reframing of the issues.
Willison on the Power and Potential of LLMs
“2025: The Year in LLMs”
Willison gets into a lot of what we’ve been showing you as the power and potential of these tools:
Reasoning models are quite good at producing and debugging code.
“Terminal commands with obscure syntax … are no longer a barrier to entry when an LLM can spit out the right command for you.”
Vibe coding is extremely useful for prototyping, though risky for production.
The Big Advance: Models Driving Tools
Willison: agents, with a caveat
The big advance is that models can increasingly drive tools, enabling multi-step planning, execution, and course correction.
BUT this is only as good as the oversight around it, so build in habits of verification, not just prompting.
Willison on the Risks: Slop
Volume is the new problem
Slop is a genuine problem: the internet has always had low-quality content, but AI dramatically increases volume.
BUT it is a great example from which to teach curation and critical evaluation.
“Normalization of Deviance”
Willison: when risky stops feeling risky
A term from sociology about how “repeated exposure to risky behaviour without negative consequences leads people and organizations to accept that risky behaviour as normal.”
Sociologist Diane Vaughan’s example: the faulty O-ring that caused the Challenger disaster was a known problem, but it hadn’t broken yet, so they stopped paying attention to the risk.
Slopsquatting: “where an LLM hallucinates an incorrect package name which is then maliciously registered to deliver malware.”
More Risks: Trust, Injection, Environment
Willison: the fine print
Many LLMs will also snitch on you if they think you’re doing something wrong, and what it thinks is wrong and what you think is wrong might not match!
Prompt injection and the “lethal trifecta”: “where malicious instructions trick an agent into stealing private data on behalf of an attacker.”
The water usage argument about data centers is probably overblown, but the energy and carbon footprint issues aren’t.
Thinking and Playing Locally
Beyond frontier models and other people’s servers
Both Willison and Cohen offer us reasons to think more about local models and tools going forward.
In this week’s workshop, we’ll demo how you can work outside of frontier models and other people’s servers.
After the class, students can request access to the Spark in CHDR for local model experiments and digital humanities projects.
Your Portfolio
The final project: agentic tools, your pedagogy
The final project in this class is an opportunity to use the agentic tools we’ve been exploring towards showcasing your approach to pedagogy.
Plan to revisit the workflow for using Claude Code (Web, Desktop, or CLI) and GitHub Pages to deploy a portfolio.
Don’t worry about getting the portfolio perfect; we’re looking to see that you understand the tools we’ve been working with, and that you can position your own pedagogy in relationship to these rapidly changing technologies!
This Week
No discussion post this week. Focus on completing your Teaching Statement.
Teaching Statement (150 points, due Sunday, July 26). A 1-2 page teaching philosophy for job applications reflecting your approach to AI and the humanities.
Readings: Cohen, “The Reboot of Digital Humanities Now” and “Books, AI, and the Public Good” (Humane Ingenuity); Willison, “2025: The Year in LLMs” (simonwillison.net).
See weeks/week-11.md on Canvas for the full guidelines and reading links.