Schedule of Readings & Activities

General Info

• Regular bullet points listed under each session indicate what you should READ BEFORE CLASS
• Readings are listed in the order that I think works best
• All readings are available online or through Zotero.
• Most sessions emphasize hands-on activities over lecture

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1: Mar 23–27

1.1: Introduction and Orientations

• Navigating course materials
• Canvas post on AI keywords:
  • what is INTELLIGENCE?
  • what is THINKING?
  • what is UNDERSTANDING?
  • what is EXPERTISE?
  • Can AI be original?
  • What is the difference between historian history and AI history?

Demonstration: the power of tokens

• What does cilantro taste like? (via https://playground.allenai.org/)

1.2: Intelligence and Machines

• Michael I. Jordan, “Artificial Intelligence—The Revolution Hasn’t Happened Yet”, Harvard Data Science Review 1, no. 1 (2019), 1-8. A computer scientist’s argument that what we call “AI” is mostly statistics and that the real intellectual challenges are about human-machine systems, not autonomous intelligence. Note this is from 2019, which is almost premodern AI.
• Ethan Mollick, Co-Intelligence, Chapter 1: Creating Alien Minds, 3–26.
• Why AI isn’t going to make art. This is also in the Zotero library,

Relevant

• Melanie Mitchell, Artificial Intelligence: A Guide for Thinking Humans, Chapters 1–3 (~45 pp.)

Discussion

• Has the revolution actually happened?
• Where on the “jagged frontier” does your own expertise begin?
• What makes good prompts?

Information

• Brief overview of how LLMs actually work—tokenization, attention, prediction—just enough to understand that AI generates probabilistically, not through comprehension.

Demonstration — Prompts and models

Prompt engineering is not as much a technical skill as a humanistic one. To write good prompts, you need context, awareness, and empathy. These are fundamental to the intellectual work of the humanities.

2: Mar 30–Apr 3

2.1: What is thinking?

• Remember to look ahead to the discussion questions so your reading can be a bit more focused.
• Alan Turing, Computing Machinery and Intelligence, Mind 59, no. 236 (1950): 433–460. This is as classic as an AI article gets, and we’re skimming/sampling more for flavor than detail. Don’t sweat the technicalities, but focus on what he’s saying about human thinking vs computer thinking.
• Wikipedia: Computing Machinery and Intelligence. This article helps unpack what might be stylistically difficult language. Be familiar with the various ways thinking is defined here and where those definitions come from.
• Alva Noë, “Rage Against the Machine”
• Plato, Phaedrus, excerpts, highlighted in green.

Discussion: The nature of thinking, historically

• What is Turing concerned with?
• What are the various ways we might define thinking?
• What does thinking and knowing and understanding mean?
• What kinds of “thinking” are easiest to fake? Hardest? What does that tell us?

Discussions: The Dangers of Writing

• What is Phaedrus about?
  • How is the dialogue form used?
  • Why isn’t this just an op-ed?
  • Is it really just generic anti-tech?
  • What’s essential for remixing?

2.2: NO CLASS!

3: Apr 6–10

3.1: Technological Resistance

• Calestous Juma, Innovation and Its Enemies: Why People Resist New Technologies, Ch. 1: Gales of Creative Destruction, 11–43.
• Calestous Juma, Innovation and Its Enemies: Why People Resist New Technologies, Ch. 3: Stop the Presses, 68–94.

Resistance Discussion

• According to Juma, why do people resist new technologies?
• How is this relevant to AI?
• How does the case study about printing the Koran illustrate Juma’s general points? How is it extraneous?

Workshop

• Dialogue REMIX pair and share via Canvas
• How can Juma help us think about Phaedrus?

Our collaborative project

• Introduction to course project: Disruptive Expertise

3.2: Getting Dumber

You don’t need to read either of these super closely–they are repetitive enough that you’ll get the idea. But read enough that you understand where the authors are coming from. What assumptions are they bringing into their books? Do the arguments make sense, or is it just ranting?

• Neil Postman (1985), Amusing Ourselves to Death, 3–29.
• Tom Nichols (2017), The Death of Expertise: The Campaign against Established Knowledge and Why It Matters, 1–39.

Discussion

• What is expertise?
• What are the similarities between these two writers?
• Where is their thinking flawed?
• How much of either applies to AI?

Prompt Workshop: Creating a new discipline

• General prompting skills are applicable everywhere–let’s experiment!
• Create a new academic discipline and interrogate it
  • What questions do we need to ask in response?
  • How can you see AI’s “jagged frontier”?
    • For more on this, see Mollick’s Centaurs and Cyborgs

4: Apr 13–17

4.1: AI critique

• Safiya Umoja Noble, Algorithms of Oppression, Chapter 1 (“A Society, Searching”) (~30 pp.) Question to keep in mind: If these problems still exist, won’t they eventually disappear anyway? Is this still relevant?
• Emily Bender et al., “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?”, FAccT ‘21 (2021): 610–623. This paper argues LLMs produce text without understanding. Does this matter?
• Kate Crawford, The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence (2021), Introduction (~20 pp.). On the material costs—labor, resources, environmental—behind AI systems.

Discussion

• What are algorithms of oppression? Isn’t math neutral?
• Are we beyond algorithms?
• Stochastic Parrot WTF? Do humans understand anything?
• What are present dangers of AI? How have they changed?
• What are other dangers not mentioned here?

Demonstration: NotebookLM

Demonstration of Google NotebookLM as a source-grounded research tool.

• Note primary and secondary analysis distinctions!
• What is a trusted source?

Workshop: Start your notebooks!

• Find and upload 2–3 primary sources on technology disruption topic
• Investigate how NotebookLM’s responses are anchored to those specific documents—in contrast to the “anything goes” nature of general chatbots.

With your sources, inquire:

• “What perspectives are missing from this account?”
• “Rewrite this from the perspective of [specific marginalized group]”
• “What sources would I need to find to tell a fuller story?”
• “What do we NOT know about this topic, and why?”

Discussion

Let’s evaluate AI’s responses. Are the “missing perspectives” it generates substantive or tokenistic? Does it cite real sources? How do you distinguish genuine historiographic insight from AI-generated platitudes about “diverse perspectives”?

4.2: Teach the Teachers

• Ethan Mollick and Lilach Mollick, “Assigning AI: Seven Approaches for Students, with Prompts” (Wharton, 2023). Skim and come prepared to discuss whether you agree with these seven approaches. Are they really different? Are they too broad? How many approaches should there be in your opinion?
• Mollick, Co-Intelligence, Ch. 3: Four rules for co-intelligence, 36–63.

Noteworthy

• Bowen and Watson, Teaching with AI, Ch. 11: Feedback, roleplaying and tutors, 209–230.

Activity — AI-assist some other course

“Convert” an exercise from another class. How could AI help you LEARN MORE?

• Use AI in a specific, bounded way
• Have a clear learning objective
• Include a metacognitive reflection component
• Be testable in class next Tuesday
• Good AI assignments make the student’s thinking MORE visible, not less. If AI can complete the assignment without the student learning anything, the assignment has failed.
• Check out the AI assignment guide

5: Apr 20–24

5.1: Disruption Assumptions I

• Adrian Johns, The Nature of the Book: Print and Knowledge in the Making, Ch. 1: Introduction, 1–40. Johns demonstrates that the advent of the printing press in Europe didn’t automatically create reliable knowledge, as had been assumed. Trust in printed texts had to be constructed through social and institutional practices. A powerful historical parallel to our current moment with AI!

Discussion: Unpacking Truth

• Critique AI version of “significance of printing press”

Workshop: Peer testing AI assignments

• Trade assignments and try to learn with AI
• Document what works, what’s confusing, where AI helps, where it distracts.

Postmortem

• Each group reports on the assignment they tested.
• What makes an AI-integrated assignment good vs. gimmicky?
• How do you prevent the AI from doing the thinking FOR the student?

Demo: Website setup

• How to set up your own website for your AI assignment and disruptive expertise contribution
• Disruptive Expertise URL TBP
• Xanthan

5.2: Disruption Assumptions II

• Steven Shapin, A Social History of Truth: Civility and Science in Seventeenth-Century England (University of Chicago Press, 1994), Chapter 1 (“The Great Civility”), pp. 3–41. Shapin shows that even scientific knowledge depends on trust, social standing, and credibility—frameworks now disrupted by AI. Shapin argues truth has always been social. If we start trusting AI outputs as “knowledge,” what social contract is being rewritten?

Discussion

Disruptive expertise Workshop

Using NotebookLM loaded with 20 sources, develop the historical narrative for your contribution to the collective site. AI helps with synthesis, identifying connections, suggesting additional angles.

Workshop: AI as sounding board

• Ask AI to support the thesis, then to challenge it
• Ask AI for counterevidence, alternative interpretations, and potential objections
• Revise the thesis based on the exchange
• AI is most useful not when it gives you answers, but when it gives you better questions.

Workshop: AI as sounding board

• “What perspectives are missing from this account?”
• “Rewrite this from the perspective of X”
• “What sources would I need to find to tell a fuller story?”
• What historical context is missing but useful?
• “What do we NOT know about this topic, and why?”

6: Apr 27–May 1

6.1: Historical Narratives

• Michel-Rolph Trouillot, Silencing the Past, Ch. 1: The Power in the Story, 1–30.

6.2: Presentations and Critiques

Lightning Presentations on rupture

Come prepared to present your rupture:

• Brief overview of topic and context
• What was most interesting about it?
• How did you find sources and evaluate for credibility
• How did AI help? How did you direct it?
• What uncertainties linger?

Peer Critique Response

Peer feedback that evaluates both historical quality and AI-critical reflection.

• What’s the story?
• How does it establish credibility?
• Does it sound human?
• Targeted improvements
• Is AI use thoroughly and clearly documented?

7: May 4–May 8

7.1: Conclusions

Collective Site Review

• What story does the collective site tell about the history of learning?
• What’s missing?
• How can we connect our stories?

Discussion — Ethics of AI Research

Now that you have spent 5 weeks using AI for research and writing, you have experiential grounding for an ethics conversation:

Ethically

• Can AI be directed to performn as a true expert?
• When should AI use be disclosed? Always? Sometimes? Never?
• Does “scholarly expertise” matter anymore? Is the value declining?
• Does AI-assisted work count as “your” work?

Institutionally

• How should academic integrity policies change?
• How should academic assessment change?
• What should students know about AI when they graduate?
• What should UNM provide to you in terms of AI (access, education, workshops, etc)

The Futures Question

Return to the course’s driving question: How can AI help us think more critically, develop skills, and produce higher-quality work?

7.2: No Class!

Final Course Reflection