# English Literature — steal this assignment
_EL3vate 2026 · Day 8 · Part 10 of 15 · build 3dfcaff_

## Try it Tuesday (90 minutes)
90 minutes, one short passage. Minutes 0–25: students write a close reading of an assigned passage by hand, no AI. Minutes 25–60: they generate the AI's close reading with the prompt and annotate it against the text — marking what it genuinely noticed, what it invented, and what it could not see. Minutes 60–90: pairs compare annotations and argue one reading the AI missed that the text supports. The deliverable is the annotated AI reading; it grades the student's ability to distinguish evidence-based interpretation from fluent invention.

## The assignment
**Make it (Hana recording studio).** A produced literary podcast episode or recorded close reading. Audio rewards the things the discipline actually teaches: pacing, emphasis, the pause before the word that matters. It is also assessment that resists generated submission.

**Build it (AI chat · no code).** A machine reading of a passage, generated deliberately, then dismantled in writing. Students document where the AI produced a plausible reading that the text does not support. This is a career argument as much as a literary one.

## 4-week plan
- **Wk 1.** Close reading of an assigned passage, written by hand, submitted before any AI use.
- **Wk 2.** Generate the AI reading of the same passage. Students annotate it against the text: what it noticed, what it invented, what it could not see.
- **Wk 3.** Record the podcast episode in the studio, arguing the reading the AI missed.
- **Wk 4.** Publish the episode with the annotated comparison as show notes. The artifact is portfolio-ready, which is the point.

## What this replaces
- **Replaces:** The take-home close-reading essay.
- **What is lost:** The sustained written argument developed over days.
- **What is gained:** The strongest case for literary study in an AI era — a student showing exactly where the machine reads badly — becomes the assignment itself; the grade rewards catching plausible-but-unsupported reading, which take-home essays cannot assess now that AI can write them.

## Where AI is bad at this
A model asked to close-read invents textual evidence with the cadence of real analysis — quoting phrasing the passage does not contain, attributing a device to a line that does not use it, and building confident interpretive claims on that fabricated ground. Because the prose sounds exactly like competent criticism, the invention is invisible unless the reader checks every claim against the actual words on the page.

## Rubric
| Criterion | Weight | What it assesses |
|---|---|---|
| Evidence verification | 35% | Every claim in the AI reading is checked against the passage; fabricated or misattributed evidence is caught and marked. |
| Discernment of real insight | 25% | Credits what the AI genuinely noticed rather than dismissing the whole output; distinguishes insight from filler. |
| The reading it missed | 25% | The student advances an interpretation the text supports and the AI did not reach, grounded in specific language. |
| Close-reading craft | 15% | The student's own annotations demonstrate attention to diction, syntax, and form. |

## Starter prompt
> Give me a close reading of this passage: [paste passage]. Be specific and confident. Make claims about what the language is doing and why it matters. Do not hedge and do not tell me interpretations vary. Then, separately, list the three claims in your reading that you are least able to support with direct textual evidence.

## Budget & logistics
- **Instructor prep:** 1 hours
- **Class time:** 90 minutes
- **Per-student cost:** $0 for the Tuesday version; Hana studio time is free for UH faculty in the four-week version.
- **Fabrication file due:** No fabrication dependency; schedule studio recording in week 3.
- **Calendar dependency:** There is no fabrication turnaround; the only scheduling constraint is booking Hana studio slots for the recorded episode.

## Three sizes
- **One session:** The 90-minute hand-reading-then-annotate session producing an annotated AI reading; no fabrication.
- **4 weeks:** The seeded four-week plan: hand close reading, AI-comparison annotation, studio recording, then a published episode with the annotated comparison as show notes.
- **One semester:** A portfolio — repeated hand close readings, AI-comparison annotations across several texts, and a produced podcast episode arguing the readings the machine missed, published with the annotated comparisons.

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_PACE · Shidler College of Business · University of Hawaiʻi at Mānoa · pace.shidler.hawaii.edu/maker_
_All fifteen assignments, the demos and every handout: https://el3vate.vercel.app_