ENG 3XXX: Reading and Making New Media
Course Information
- Term: Spring 2027
- Class Meeting Days: Mondays and Wednesdays
- Class Meeting Times: 9 AM - 10:50 AM
- Class Meeting Location: Classroom Building 1, Room 100
- Modality: Face-to-face
- Credit Hours: 3
Instructor Information
- Name: Glenn S. Ritchey III
- Office Location: Office Building 1, Room 120
- Virtual Office Hours: Mondays, 11:00 AM - 1:00 PM; also available by appointment
Course Description
This course examines literature as an interdisciplinary field where students study the intersection of textuality and technology by using digital humanities tools and critical making research practices. It introduces humanities students to basic concepts surrounding digital tools and positions digital literacy as a skill that enhances scholarship through analysis and creation practices that allow students to explore texts through low-demand computational practices.
Through scholarly readings, multimedia engagement, and analysis of media assigned to the student and made by them, students develop critical frameworks for understanding how the humanities maintain relevancy in an increasingly digital world.
Student Learning Outcomes
By the end of the course, students will be able to:
- Read and interpret cultural objects at the intersection of image and text, situating them within their conditions of production and the interpretive frameworks used in humanistic study.
- Participate in sustained critical discourse about media objects, using assigned theory as support to situate original readings within and against existing conversations.
- Produce a critical creative artifact that reorients academic analytical and compositional skill into making, working within deliberate constraints rather than toward predetermined ends.
- Adapt and extend an existing generative or hypertext system into a new media artifact, documenting what is preserved, altered, and/or subverted.
- Interrogate generative AI as an object of critical inquiry — its labor, its exclusions, its assumptions — and make deliberate, documented decisions about whether and how to use it in their own work.
- Reflect on their own making and trace how a finished object circulates within the larger conditions of media proliferation, accounting for where interpretive, creative, and technical knowledge meet in it.
Required Course Materials
- Burdick Anne et al. Digital_Humanities, 2012, MIT Press. Open-Access Text here.
- Johnson, Emily K. and Anastasia Salter. Critical Making in the Age of AI, 2025, Amherst College Press. Open-Access Text here.
- I will provide other PDFs and readings links on an as-needed basis.
Software and Tools
All tools used in this course are free and, with the single exception of Handy, run entirely in a browser — meaning they are fully available on campus library computers. No paid subscriptions are required.
- Duck.AI (DuckDuckGo’s privacy-mediated AI interface) — our AI environment for the course
- Firefox with the Simple Translate extension — recommended browser and reading-access tool
- Voyant Tools — browser-based text analysis
- Twine / SugarCube — interactive/hypertext authoring
- The course procedural pipeline (Reduce · Edit Vocabulary · Generate) — provided by the instructor
- Handy (optional) — offline, open-source speech-to-text for drafting
AI Policy
TL;DR
- Unauthorized AI use (no credit, no documentation of process) results in an automatic 15% deduction.
- This isn’t a gotcha—I want to understand your how and why.
- A visit to office hours is required, or the student accepts the grade.
- Do not upload a copyrighted work or a peer’s work to an LLM under any circumstances.
- If English isn’t your first language, or you find outlining/drafting difficult, AI-assisted translation and tools like Handy (an offline transcription tool) are legitimate process work when it comes to outlining and drafting.
- This clause specifically concerns writing, not translating texts for readability (more in the policy below)
- We use Duck.AI in this course so you can compare models and so I can review your process via its export feature.
- All required tools except Handy run in-browser and are available on library computers.
- Match the scale of the tool to the scale of the task—a quick question deserves a quick answer, not an extended session.
Unauthorized AI Use
Unauthorized AI use will result in an automatic 15% deduction from the maximum possible grade for any given assignment. Unauthorized use of AI essentially refers to anything reasonably suspected to be authored by an LLM with no proper credit given and no documentation of the process.
Documentation means the Duck.AI transcript (the .txt file DAI provides on export) along with a few representative screenshots of key interactive moments—enough for me to see the shape of the exchange, not a full transcript of every message.
This is not a “gotcha” clause, but to uncritically use AI undermines a lot of the point of this specific course. I want to understand the how and why of your AI usage because I am more interested in growth and process development than I am in outright penalty. Still, you will be expected to either prepare a discussion during an office hours appointment in a way that is not just in defense, or to accept the grade.
Copyright Concerns
The one place AI use overlaps directly with a copyrighted text is the Textual Analysis project, and we’ve avoided that problem by design: our shared text is Lewis Carroll’s Alice’s Adventures Under Ground, available through Project Gutenberg and in the public domain. There’s no rights conflict in the source material itself. What matters here isn’t ownership of the text but whether the analysis is yours. Documentation requirements apply the same way they do everywhere else in this policy.
With that said, one should not upload a copyrighted work or a peer’s work to an LLM under any circumstances.
A Practical Case
“Practical” has different meanings based on the individual, so I want to highlight what it means to me. Let’s say I have at least one book to read during my week for classes. Would it make sense for me to just have a Large Language Model (LLM) like Claude or Chat GPT read the text for me and return uninspired information? No. While it seems like it would be practical to do so amid my busy schedule, I am not receiving authentic knowledge by reading what Claude or Chat GPT summarizes.
What I mean by authentic knowledge is the recognition of how the author’s writing relates to my experiences, worldview, and values. When we read, we are investing our time into a laborious process with high reward. An LLM cannot accomplish authentic thinking for me because they are trained on massive datasets that predict statistically probable outputs that might resemble a “reasonably best effort”; in essence, think of when LLMs “drift” or “hallucinate.” Think of it like this: The customer is always right; this is part of why LLMs have a reputation for being sycophantic.
The distinction I keep returning to is between deference and material shaping. Deference means outsourcing your judgment—asking an LLM to write your analysis. Material shaping means using AI to work with thinking that is already yours.
Translation and Access
I first want to specify that the following section is under no circumstances encouraging one to upload copyrighted work to AI. This section refers solely to the process of writing and using your own words. The only instance of uploading a text unauthored by the student to an LLM in this course is covered in Copyright Concerns.
This part of the course’s AI policy specifically addresses students communicating in English as a second language who might face difficulties in brainstorming and outlining (see Santos Neto 2012) as certain ideas are not easily translatable (if at all) between native and secondary language, or there may be differences regarding certain references or idioms.
Likewise, I generally encourage students to first outline their written work in their first language. Then they may input that writing into an LLM (like Claude) to translate that writing to English. This aspect of writing is not cheating but represents prosthesis. Students who use AI to translate from their first language to English are using a prosthetic tool to access and participate in a linguistic context that was not designed with them in mind at the institutional level.
The concern about AI in this course is cognitive replacement and not linguistic capabilities when it comes to writing. To discourage the aid of AI as a tool in writing represents a continuation of deficit thinking (i.e., that English is the premier language) and dresses it in new technological anxiety. This is not a problem AI created but a structural condition that reminds us of historical suppression of diversity in language.
The same access logic extends to reading. If you would benefit from reading a course text in your first language, start with the UCF library—published translations exist for most of our assigned readings, and interlibrary loan can usually get the rest. A translator’s choices are often part of the scholarship itself, so a published translation is worth more to you than a machine one where it exists.
For texts without an available translation, I recommend Firefox with the Simple Translate extension. It translates a passage you highlight and shows it in a popup, leaving the page itself intact—full-page translation tends to break formatting on PDFs and journal layouts, and it replaces the original rather than sitting beside it.
With that said, uploading a copyrighted work, or any other work written by another author, to an LLM represents a tension with ethics and legality, i.e., plagiarism or piracy.
Even if the issue is not communicating in English as a second language, many native English communicators still have issue with things like outlining or drafting; I am one of them! A lot of folks have an easier time speaking through their ideas rather than writing them down. If this seems like the case for you, or you want to experiment with this mode of composition, then I recommend Handy.
So, feel free to speak through your stream of consciousness and see how you may shape your text; though, I would not recommend spending too much time with this process if it seems unproductive. This is legitimate process work that many writers, including ones who do not have the same motor functions as others, use. I want to bring this up, too, because a lot of students seem to defer to AI when they feel like they are up against the wall on a deadline; I think you might find it removes some of the pressure to use AI.
The AI Model(s) We Will Use
The tools I recommend in this course lean toward open-source and privacy-respecting options wherever good ones exist. That is a pedagogical position as much as a practical one: part of understanding these technologies is understanding who owns them, what they do with your data, and whether you can see inside them.
I want us all to use Duck.AI for the AI work we accomplish in this course. Duck.AI is privacy-focused browser and search engine DuckDuckGo’s AI instance. It uses many different models and acts as a mediator between you and LLMs (like ChatGPT, Claude, and Gemini). This is so your data is not used to train their models. I want you to use Duck.AI so you may experiment with different AI models to see how they differ in application. Also, Duck.AI has a simple export feature built into its interface that allows me to evaluate how you are using AI for course work.
All required tools for this course, except Handy, run entirely in a browser, so campus library computers give you full access to everything you need.
Using AI Strategically
Part of developing real AI literacy is learning to match the scale of the tool to the scale of the task. A single, bounded question deserves a single, bounded answer—asking Duck.AI “Summarize the plot and history of Lewis Carroll’s Alice’s Adventures Under Ground briefly” and reading the response once is no different from consulting an encyclopedia entry before a lecture. That is, provided you can confirm that summary against your understanding of the text.
What I want you to avoid is the mismatch: running an extended, multi-step AI session to answer what a single search would resolve, or generating five draft variations of a paragraph you could write once and revise yourself. Underneath the deference/material-shaping distinction is also this—a resource question. These models take real computational and human labor to run and train. Using more tool than a task needs isn’t just a habit that weakens your thinking; it’s a cost you’re incurring without a reason.
Final Statement
Everyone continues to evolve their understanding of AI’s potential and limitations. I have been wrong about LLM functionality and use cases in the past and probably will be again. This policy is a living document but the principles undergirding it are unwavering: I want this course to help develop your skills when it comes to critical thinking and expression. Any tool that helps to serve that goal is worthy of serious consideration, and so is the ability to understand how to properly use any tool.
Assignments Overview
Textual Analysis Reflection (150 pts). Working from our shared public-domain text, Lewis Carroll’s Alice’s Adventures Under Ground, you will move between close and distant reading — attending to the text as a made object while also using computational tools (Voyant) to see its patterns at a scale reading alone can’t reach. Your reflection documents both what the tools reveal and what they flatten, and situates your own reading within the interpretive frameworks we build in Unit 1. (SLOs 1, 2)
Visual Analysis Reflection (150 pts). Using your own original photographs, you will run a structured, multi-phase analysis with an AI chatbot (via Duck.AI), then evaluate the gap between what the machine reports and what you know to be in the image. The reflection is less about the photograph than about the tool’s reading of it: where it is useful, where it fails, and what those failures tell us about how these systems see. (SLOs 1, 5)
New Media Object (200 pts). The course’s capstone. You will remediate your earlier work into an interactive new media artifact in the format you choose — zine, print, or Twine/SugarCube — adapting and extending an existing generative or hypertext system rather than building from nothing. The finished object is submitted as a self-contained file; no public deployment is required. A separate design rationale documents what you preserved, altered, and subverted, and traces how your object sits within the larger conditions of media it responds to. This assignment is scaffolded across the drafting and peer-review work of Weeks 13–16. (SLOs 3, 4, 6)
Reading Reflections (5 × 40 pts = 200 pts). Short, recurring responses to the week’s reading, due across Unit 1 and the opening of Unit 2. These are where assigned theory stays load-bearing: rather than summarizing, you put a reading to work on a specific object or question, situating your own view within and against the conversation. (SLO 2)
Pipeline Reflections (2 × 30 pts = 60 pts). Two brief making-reflections tied to the procedural pipeline — one after Reduce (Week 8), one after Generate (Week 11). Each asks you to account for a decision you made at that station and what the tool made visible or lost. (SLOs 3, 5)
Format Commitment / Concept Finalization (40 pts). At Week 13, having worked hands-on with both the procedural pipeline and Twine, you commit to a format for your New Media Object and finalize its concept — arguing the choice against the minimal-computing stakes we read that week rather than defaulting to the easiest path. (SLOs 4, 6)
New Media Object Draft & Peer Review (60 pts + 40 pts). A working draft of the capstone (Week 14) and structured peer review of a classmate’s draft (Week 15). Both are assessed as process work: the draft on its evidence of genuine iteration, the review on the usefulness of the feedback you give. (SLOs 3, 6)
Grading Policy
| Major Assignments — 50% | Participation — 30% | Drafting & Peer Review — 20% |
|---|---|---|
| Textual Analysis Reflection (150 pts) | Attendance (100 pts) | Pipeline Activity 2 Reflection (30 pts) |
| Visual Analysis Reflection (150 pts) | Reading Reflections (5 × 40 = 200 pts) | Pipeline Activity 1 Reflection (30 pts) |
| New Media Object (200 pts) | Format Commitment / Concept Finalization (40 pts) | |
| New Media Object Draft (60 pts) | ||
| New Media Object Peer Review (40 pts) |
1,000 Points Total
Late Work Policy
I do not offer makeup work or extra credit beyond what is already assigned in the course and outlined in the “Missed/Late Assignments” policy below.
If you are concerned about not submitting an assignment on time, I encourage you to contact me. Late work may be considered in some instances if you have been in contact with me in advance to discuss an alternative due date. In most cases, late work will not be accepted, except in extenuating, documented circumstances.
Assignments build on each other in this course. Falling behind creates compounding difficulty. The late policy exists for genuine emergencies, not routine time management.
Grading Scale
| Letter Grade | Percentage | Grade Point Value |
|---|---|---|
| A | 94–100% | 4.00 |
| A- | 90–93% | 3.75 |
| B+ | 87–89% | 3.25 |
| B | 84–86% | 3.00 |
| B- | 80–83% | 2.75 |
| C+ | 77–79% | 2.25 |
| C | 74–76% | 2.00 |
| C- | 70–73% | 1.75 |
| D+ | 67–69% | 1.25 |
| D | 64–66% | 1.00 |
| D- | 61–63% | 0.75 |
| F | 0–60% | 0.00 |
Course Schedule
| Week (Dates) | Topic | Read Before Class | Due Friday 11:59 PM |
|---|---|---|---|
| Unit 1 · Computational Analyses | |||
| 1 (1/11–15) |
Introductions; DH in the English department | Monday:
Wednesday:
|
Academic Engagement Activity (University Requirement) |
| 2 (1/18–22) |
Methods, genres, reading at a distance | Monday:
Wednesday:
|
Reading Reflection #1 |
| 3 (1/25–29) |
Reading the image; the social life of the field | Monday:
Wednesday:
|
Reading Reflection #2 |
| 4 (2/1–5) |
Provocations: down the rabbit hole & making as method | Monday:
Wednesday:
|
Reading Reflection #3 |
| 5 (2/8–12) |
Critical making; distant-reading Alice | Monday:
Wednesday:
|
Major Assignment 1: Textual Analysis Reflection |
| Unit 2 · E-Lit + Procedural Authorship | |||
| 6 (2/15–19) |
Alice as data; the remediation model | Monday:
Wednesday:
|
Reading Reflection #4 |
| 7 (2/22–26) |
Procedural poetics; the grammar; reading code | Monday:
Wednesday:
|
Reading Reflection #5 |
| 8 (3/1–5) |
Reduce: the photograph as data | Studio — no new reading. Pipeline Activity 2 (Reduce): reduce your own photographs; sit with the "Data rebuild" loss. |
Pipeline Activity 2 Reflection |
| 9 (3/8–12) |
Vocabulary and critical generation | Studio — no new reading. Pipeline Activity 3 (Edit Vocabulary): name your extracted colors; fork the instructor grammar or start empty. |
Major Assignment 2: Visual Analysis Reflection |
| 10 (3/15–19) |
Spring Break | No class | |
| 11 (3/22–26) |
Generate & curate | Monday:
Wednesday:
|
Pipeline Activity 1 Reflection · Withdrawal Deadline |
| 12 (3/29–4/2) |
Twine as remediation | Monday:
Wednesday:
|
|
| 13 (4/5–9) |
Format choice; remediation paths | Monday:
Wednesday:
|
Format Commitment / Concept Finalization |
| 14 (4/12–16) |
Workshop | Studio — build. | New Media Object Draft |
| 15 (4/19–23) |
Workshop → handoff | Studio — assembly. | New Media Object Peer Review |
| 16 (4/26–30) |
Wrapping up | Monday:
|
New Media Object (final) |