The Narrative Analysis assignment is one of the course's two major papers, due at the end of Week 7. You have been using the four-question framework since Week 1 — this assignment asks you to take that method into a text you choose yourself and work through it in sustained, written form. You are encountering this page in Week 3 because the most important decision you will make about this assignment is which text to analyze. That decision shapes everything that follows. The earlier you make it, the better your paper will be.
Before you begin writing anything, you need to submit a brief text proposal and receive instructor approval. This is not a bureaucratic hurdle — it is the same practice we used for the gallery case study: a moment of deliberate choice and public justification before the analytical work begins.
What the Assignment Asks
You will select one narrative text — written or visual/media — and analyze it as a form of anticipatory social analysis about AI, automation, or algorithmic systems. Using the four-question framework, you will make a sustained argument about what the text reveals that purely technical or policy analysis cannot see. You will connect your analysis to at least one real AI system or policy case discussed in the course.
INSTRUCTIONS
Format
- requirements, length, and submission details — are on the Narrative Analysis Assignment page in the Week 6 module. Read those instructions before submitting your proposal.
What Texts Are Eligible
Your text must engage — directly or obliquely — with AI, automation, algorithmic systems, or the idea of machine intelligence. It does not need to use those words. What matters is that it opens a question about intelligence, agency, prediction, labor, accountability, or what it means to be classified by a system.
| WRITTEN / TEXTUAL TRACK | VISUAL / MEDIA TRACK |
|---|---|
| Short stories, novellas, novel excerpts, poetry sequences, graphic novels, comics | Films, television series (one season or a sustained arc), podcasts, video game narratives, documentary films |
| Note on scope: If you choose a novel, focus your analysis on a specific section or set of scenes rather than attempting to analyze the full text in 1,200–2,000 words. | Note on scope: If you choose a TV series, focus your analysis on one season, one episode, or a specific narrative arc — not the full series. |
On Course Texts
Texts we analyzed together in class — Yáñez Cossío's "La IWM 1000," Chiang's "Exhalation," and Borges' "The Library of Babel" — are not eligible. The assignment asks you to do original analytical work, not extend our class discussion.
Texts assigned to the course but not fully analyzed in class — Dick's "The Minority Report" and Le Guin's "The Ones Who Walk Away from Omelas" — are eligible, but only with a strong justification explaining what angle your analysis will take that goes beyond anything we touched on in class.
A Note for Visual and Media Track Students
The four-question framework was developed primarily for written fiction. Visual and media narratives carry meaning through additional channels — cinematography, editing, sound design, mise-en-scène, music — that the four questions do not explicitly address. If you choose a film, series, podcast, or other media text, you will use a supplemental set of visual analysis prompts alongside the four questions. Those prompts are included on the full assignment page in Week 6.
Choosing a visual text is not easier or harder than choosing a written one — it is methodologically different. Make sure you are choosing a visual text because it is the right text for your question, not because you find it more accessible. In your proposal, explain specifically how the visual or sonic elements of your text are doing analytical work that a written text could not.
Your Proposal: Three Parts
Submit your proposal as a private text entry in the Canvas assignment below. Your proposal should be 150–250 words total, organized around three questions. There are no right answers — what the proposal is looking for is evidence that you have made a deliberate choice and that you can articulate why it matters.
| 1 | Identify your text. Give the full title, author or creator, medium, and year. If it is a visual/media text, specify what you are focusing on (a single film, one season of a series, a podcast episode, etc.). |
| 2 | Justify your choice. In two to four sentences: what does this text engage — however indirectly — about AI, automation, algorithmic systems, or machine intelligence? Why is it the right text for this assignment and not just a text you happen to like? |
| 3 | Name your opening question. What is one question the text raises for you — about intelligence, agency, accountability, labor, or what it means to be classified by a system — that you want to pursue in your analysis? This does not need to be your final argument. It is the question you are starting with. |
Proposal Deadline
Submit your text proposal with your Week 3 Stakeholder Map. Both are due before Week 4 Session A.
You will receive instructor feedback within 3–5 business days. Do not begin drafting your analysis until your text has been approved. If you are redirected to a different text, you will have enough time to adjust before the Week 6 workshop.
What Approval Looks Like
The instructor will respond to your proposal in one of three ways: approved (proceed with your text); approved with a refinement (your text works, but focus your question in a specific direction); or redirected (this text is not the right fit, with a specific suggestion for why and where to look instead). There is no penalty for being redirected — it is part of the process of finding the right analytical entry point.
What Comes Next
Once your text is approved, you have Weeks 4 and 5 to read or view it carefully and begin making analytical notes. In Week 6 Session B, there is a dedicated workshop where you will work with a partner on your draft or outline using the four-question framework. The full assignment is due at the end of Week 7.
The full assignment instructions, the four-question framework, the visual track supplement, formatting requirements, and submission details are all on the Narrative Analysis Assignment page. Bookmark it now.
Narrative and Cultural Analysis Assignment1,200–2,000 words · Creative Commons licensed · Individual assignment
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This assignment asks you to do what we have been practicing since Week 1: read a narrative text as a form of anticipatory social analysis. Stories about intelligent machines, automated systems, and algorithmic futures are not predictions. They are cultural laboratories — ways of thinking through questions that purely technical or policy analysis cannot reach. This assignment asks you to demonstrate that you can use narrative as a genuine analytical method, not as illustration or decoration.
You will apply the four-question framework introduced in Week 2 to the text you selected and had approved in Week 3. You will connect your analysis to at least one real AI system or policy case from the course. The result should be an argument — a specific, evidence-based claim about what your text reveals that a policy document or technical report cannot.
The Assignment's Central Question
What does this narrative make visible about AI, automation, or algorithmic systems that a technical or policy analysis cannot see — and what real-world system or case does that insight illuminate?
The Four-Question Framework
Your analysis must engage all four questions. They do not need to appear as labeled sections in your paper — strong papers weave them into a continuous argument. But every question must be addressed, and each should be grounded in specific moments from your text, not in general claims about what the text is "about."
| 1 | What kind of intelligence does the system in this story exercise — and what kind does it lack? This question asks you to characterize the system's cognitive capacities with precision. Avoid vague terms like "smart" or "limited." What specifically can it do — classify, predict, optimize, generate? And what specifically is absent — judgment, context, accountability, the ability to be wrong in a human sense? |
| 2 | Who benefits from the system, and who bears its costs? Map the story's distribution of power and harm. Who commissioned the system, who operates it, who is subject to it, and who is absent from the frame entirely? The most interesting answers often involve characters or groups the story does not name directly. |
| 3 | What human capacity does the system replace or displace? This question is not asking what the system automates in a technical sense. It is asking what form of human presence, judgment, or relationship is lost when the system takes over. Memory, care, discernment, witness, accountability — these are the kinds of capacities worth examining. What does the story suggest cannot be recovered once they are gone? |
| 4 | What does the story's resolution suggest about accountability — and who is absent from it? How does the story end — and what does that ending encode about responsibility, consequence, and repair? Does the story imagine accountability as possible? As institutional, individual, or structural? Pay attention to who is not in the room when accountability is — or is not — achieved. |
Visual and Media Track — Supplemental Prompts
If your approved text is a film, television series, podcast, graphic novel, video game, or other media narrative, you must engage all four questions above and address at least two of the following supplemental prompts. These are not separate sections — they are additional analytical lenses to weave into your argument where they sharpen it.
Visual and sonic elements are not decoration. They are argumentative. A camera angle, a musical cue, a scene cut, a color palette — these make claims about power, knowledge, and accountability just as a narrator's voice does in written fiction. Your job is to show how.
| Supplemental Prompt | What It's Asking |
|---|---|
| How does the visual or sonic language construct the AI system's point of view — and whose perspective does it privilege or exclude? | Camera position, framing, and sound design encode epistemic authority. Who sees, and how? |
| What does the text's visual or sonic grammar suggest about the relationship between speed and power? Between visibility and control? | Editing pace, surveillance aesthetics, and the look of data often encode assumptions about who controls whom. |
| Where does the text use visual or sonic ambiguity — moments that resist clear interpretation — and what does that ambiguity do analytically? | Ambiguity in visual texts is often where the most interesting analytical work lives. Don't resolve it prematurely. |
| How does the text use the body — human or machine — to make an argument about what AI can and cannot know? | Embodiment, gesture, and physical presence often carry the argument that dialogue alone cannot. |
Connecting to a Real-World Case
Your analysis must connect to at least one real AI system, policy case, or governance context discussed in the course. This connection is not a footnote — it is a structural requirement of the argument. The narrative analysis is the lens; the real-world case is what the lens illuminates.
The connection should be argumentative, not decorative. You are not saying "this story reminds me of facial recognition." You are saying something like: "this story's account of delegated judgment without accountability maps precisely onto the opacity problem that Citron describes in automated benefits systems — and the story reveals something about that problem that Citron's legal argument cannot reach." The narrative does analytical work the policy text cannot. Show that work.
What Strong Analysis Looks Like — A Model
The paragraph below is a model of how one of the four questions might be addressed in a paper analyzing E.M. Forster's 1909 story "The Machine Stops" — a text not used in this course. Read it as an example of analytical depth and textual grounding, not as a template to imitate structurally.
Model paragraph — Question 3: What human capacity does the system displace?
"What Forster's Machine displaces is not labor but orientation — the human capacity to locate oneself in physical space and to know the world through the body's encounter with it. When Vashti refuses to look out the airship window, calling the earth 'just dust and muck' and retreating to the comfort of her hexagonal cell, the story is not staging a failure of curiosity. It is staging the systematic atrophy of a perceptual faculty that the Machine has made unnecessary. The Machine does not merely automate travel; it renders the experience of place irrelevant. This is precisely what contemporary location-inference systems accomplish at scale: not by physically confining users, but by making the data-processed abstraction of a place — its review score, its foot-traffic index, its algorithmic rank — more actionable than the place itself. Forster's story makes visible what machine learning's optimization logic cannot name: that what is lost when orientation is automated is not a convenience but a form of knowing."
Note how the paragraph moves: specific moment in the text → precise characterization of what is displaced → connection to a real system → claim about what the narrative reveals that the real-world system's own logic cannot articulate. That movement — text to system to insight — is the argumentative spine of this assignment.
Format and Submission
| Length | 1,200–2,000 words, excluding your AI Use Disclosure Statement and references. Papers significantly under or over this range will be asked to revise before grading. |
| Format | Double-spaced, 12pt font, 1-inch margins. MLA or APA citation format — be consistent. Include full bibliographic information for your primary text and all course materials cited. |
| License | Submit under a Creative Commons Attribution 4.0 (CC BY 4.0) license. Include the license declaration on your cover page: "This work is licensed under CC BY 4.0. The author permits reuse, adaptation, and redistribution with attribution." |
| AI Disclosure | Attach a brief AI Use Disclosure Statement at the end of your paper. Describe which tools you used, how, and how AI-generated outputs were revised or rejected. This is required regardless of whether you used AI tools — if you did not, say so. |
| Submission | Upload to the Canvas assignment by end of Week 7. Submit as a .docx or .pdf file. Papers submitted for library deposit will be formatted by the instructor after grading. |
| Weight | 15% of final course grade. |
Due · End of Week 7
Submit via Canvas by the end of Week 7. The Week 6 Session B workshop is your last structured opportunity for peer feedback before submission.
Your text must have been approved before you begin drafting. If you have not yet received approval, contact the instructor immediately.
What the Paper Is Evaluated On
| Criterion | What It Means |
|---|---|
| Textual grounding | Claims are supported by specific moments, scenes, images, or passages from the text — not by general assertions about what it is "about." |
| Analytical depth | All four questions are meaningfully addressed. The analysis goes beyond plot summary or thematic description to make a claim about what the text reveals. |
| Real-world connection | The connection to a real AI system or policy case is argumentative — it shows what the narrative reveals that the real-world system's own documentation cannot. |
| Clarity of argument | The paper has a central claim — a specific, defensible argument — that a reader can identify and evaluate. It is written in clear, accessible language. |
| Responsible AI use | AI use is documented honestly and completely. The intellectual core of the argument — the analysis, the claim, the connection to the real world — is the student's own. |