Course Description

Theory and practice of designing interdisciplinary courses and curricula for the humanities. For graduate students who plan to seek faculty positions or who want to strengthen their teaching practice.

Engages foundational questions of pedagogy — how people learn, how to design effective assignments and assessments, how to build inclusive and student-centered courses — while addressing the challenges and opportunities of generative AI in the humanities classroom.

Over Twelve Weeks

• Explore interdisciplinary humanities approaches to pedagogy.
• Design a course from the ground up (or redesign an existing one).
• Engage with agentic AI tools (Anthropic’s Claude).
• Develop professional materials, including a teaching statement.

NEH Workshop Integration

Connected to the NEH project Building a Digital Humanities Generative AI Learning Community. Optional live workshops, biweekly at CHDR, 10 AM – noon:

Course Objectives

1. Engage with humanities and DH approaches to pedagogy.
2. Make informed decisions about teaching strategies, including AI policies.
3. Compose a meaningful teaching statement.
4. Design student-centered in-person and online courses.
5. Explore best practices for accessibility and universal design.
6. Critically evaluate generative AI tools in humanities pedagogy.
7. Use generative AI tools (Claude Artifacts, Claude Code Web) to create and assess pedagogical materials.

Texts

No required textbook. Readings are open-access articles, blog posts, and PDFs. We draw heavily on practitioners. Consider following:

• Ethan Mollick — One Useful Thing (AI in education)
• Simon Willison — simonwillison.net (AI tools, vibe coding)
• Dan Cohen — Humane Ingenuity (AI, DH, cultural heritage)
• Ted Underwood — The Stone and the Shell (distant reading, AI)

Anthropic Subscription

A paid subscription to Anthropic’s Claude is required for hands-on exercises throughout the semester.

• Claude Artifacts — used in the first half of the course.
• Claude Code Web — used in the second half.

A paid (Pro) subscription is needed to access the full functionality the course assignments rely on.

Grading

Total: 1000 points. Late work accepted without penalty for one week after the listed deadline.

Teaching as Invisible Labor

Croxall & Jakacki, citing Katharine Harris

“Teaching is often invisible labor, [even] though it’s the largest part of our jobs. Teaching and learning are hard to see not because of their size but instead because the institutions that employ us have chosen to value research and its outputs (publications, chiefly) rather than pedagogy and its results (spread of knowledge, chiefly, and often enlarged and even changed minds). This undervaluing of educational effort continues despite the fact that it is the students, through their tuition dollars, who make possible the time faculty have to research.” — Croxall & Jakacki, citing Katharine Harris

DH Pedagogy as Essential Liberal Arts

Brandon Locke — four learning objectives

“The framework employed in LEADR consists of four flexible learning objectives that can be packaged and built upon to meet the needs of the course and the desired outcomes of the faculty member and partners. The objectives are 1) Information Literacy, 2) Digital Literacy, 3) Data Literacy, and 4) Computational Analysis.” — Locke, DHQ 11.3 (2017)

Information Literacy

ACRL Framework — six frames

“An overarching set of abilities in which students are consumers and creators of information who can participate successfully in collaborative spaces.” — ACRL 2015, via Locke

• Authority Is Constructed and Contextual
• Information Creation as a Process
• Information Has Value
• Research as Inquiry
• Scholarship as Conversation
• Searching as Strategic Exploration

Digital Literacy

ALA definition — cognitive plus technical

“The ability to use information and communication technologies to find, understand, evaluate, create, and communicate digital information, an ability that requires both cognitive and technical skills.” — ALA 2013, via Locke

Locke notes substantial overlap with information literacy, and that academic libraries play a key role — undergraduate students are not nearly as digitally literate as often assumed.

Computational Analysis

Methods grounded in disciplinary methodology

“A wide-ranging set of tools and methodologies that rely upon computational processes to assist in the asking and exploring of research questions, including but not limited to text mining, network analysis, GIS and web mapping, 3D modeling, desktop fabrication, and topic modeling. These lessons are grounded in disciplinary methodology, and illustrate the ways in which scholars are using computationally-aided methods to ask new questions of their sources, as well as exploring more traditional questions.” — Locke, DHQ 11.3 (2017)

Program or Be Programmed

Douglas Rushkoff — do you have to learn to program?

“And while I always answer, ‘Yes, you do have to learn to program,’ the real answer is probably no. You don’t. You can get by without becoming a literate participant of the digital age. You may not know what’s going on, you may not have much of an impact on the future of our species, and you may begin to feel like technology knows more about you than you know about it — but no, you don’t have to learn to program.” — Rushkoff & Purvis, Program or Be Programmed (2011), 7–8

Without Training, Everyone Uses AI Wrong

Ethan Mollick — on bad first prompts

“I have been hearing reports from teachers about how they are seeing lots of badly-written AI essays, even though ChatGPT is capable of quite good writing. I think I know why. Almost everyone’s initial attempts at using AI are bad. Almost everyone’s first prompts were very straightforward. They usually pasted in the assignment directly, something like generate a 5 paragraph essay on selecting leaders. Sometimes they went a little further: use an academic tone or write it for an MBA class. The result was almost always a mediocre C− essay. I think this is what most teachers are seeing, and why a lot of people underestimate what ChatGPT can do as a writing tool.” — Mollick, One Useful Thing

Students Understand Accuracy and Bias Issues

Ethan Mollick — on hallucinations in student work

“I have seen lots of educators concerned about the fact that the AI lies, frequently and well. But, seeing my students’ work, I think this is less of a problem than many think. Students understood the unreliability of AI very quickly, and took seriously my policy that they are responsible for the facts in their essays. It was clear that they carefully checked the assertions in the AI work (another learning opportunity!), and many reported finding the usual hallucinations — made up stories, made up citations — though the degree to which these problems were overt varied from prompt to prompt.” — Mollick, One Useful Thing

AI Is Everywhere Already

Ethan Mollick — the world of teaching is now more complicated

“Even if I didn’t embrace AI, it is also clear that AI is now everywhere in classes. For example, students used it to help them come up with ideas for class projects, even before I even taught them how to do that. As a result, the projects this semester are much better than previous pre-AI classes. This has led to greater project success rates and more engaged teams. On the downside, I find students also raise their hands to ask questions less. I suspect this might be because, as one of them told me, they can later ask ChatGPT to explain things they didn’t get without needing to speak in front of the class. The world of teaching is now more complicated in ways that are exciting, as well as a bit unnerving.” — Mollick, One Useful Thing

This Week

• Activity Verification — due Tuesday, May 19.
• Discussion: Introduce yourself — due Sunday, May 17.
• Optional NEH Workshop 1 — Tuesday, May 13, 10 AM–noon, CHDR.

See weeks/week-01.md on Canvas for readings, links, and the discussion prompt.