ENG 123 8.W: Worknets and/as Research Annotations

Today’s Plan:

  • Group Share
  • Review Try This! Assignment
  • Worknet: Bibliographic Pass
  • For Next Session

Group Share

Ten minutes on the clock. Share with your group mates one of the key terms you pulled from your article. Yes, I know you have all read different articles. Now we are testing to see if key terms appear across articles, if we can start to identify disciplinary vocabulary.

For those who completed 122, academic disciplines are highly specialized discourse communities, and most have developed very complex and nuanced vocabularies. I’m a video gamer, and as such have a pretty developed gaming vocabulary including terms like “meta,” “PvE,” “GG,” “DPS,” “carry,” “power creep,” “nerf,” and “lfg,”(which means something very different than how professional athletes use LFG!!!”). Academic disciplines are similar in that each one has its own vocabulary used by insiders, and given the sophistication of these vocabularies, it can be disorienting to new comers.

Review Try This! Assignment

Let’s take a look at the assignment.

As I’ve said in the email, the goal of this assignment is to help you develop strategies for dealing with those specialized (and at times obtuse and arcane) vocabularies you will discover in your chosen field. Whatever the field, you are going to come across specialized language and terms. Even in the field I’ve been studying for 25+ years, I come across new terms with which I am unfamiliar.

The worknet is Derek Mueller’s approach to helping initiates familiarize themselves with research in their field, and with reading professional disciplinary research. I like the way that Mueller starts with vocabulary because it is something specific we can target; the goal here is to provide strategies that help you feel more confident when reading texts that will likely make you feel overwhelmed or anxious.

Monday’s assignment simply asked you to identify some key terms and attempt, based on context and prior knowledge, to define them. Now we want to dig a bit deeper. Let’s look at the follow-up assignment from the book (page 48):

Look through the list of terms you highlighted for today (or identify one now). Look for an important term–one repeated throughout the text. Per the assignment above, does the author define the term? Or do they assume you are already familiar with it? Can you identify the likely definition from context?

For Next Session

For homework, I’d like you to select two terms. I want you to research each term. For each of them:
  1. Look them up in the Oxford English Dictionary. To easily do this, you should use a computer on campus (I recommend the 1240 lab downstairs or the library), because you’ll want to be logged into the OED (see top-right corner). Don’t just look at the result page, make sure you are looking at the whole entry. Check the etymology, scroll through some of the historic definitions, get a sense of how the word has traveled.
  2. Search for your word in Google Scholar. Make your search something like: [term] and [discipline or context]. For instance, procedural rhetoric and video games. Or: pedagogy and college writing. Scroll through the responses and get a sense for how that word shows up in the titles of research articles.
  3. After you have done both searches for a term, take about 10 minutes and write about 100-200 words defining the term and describing its use. Do this for two terms (so 200-400 words of writing). In the computer lab on Friday, I’m going to walk you through setting up a work log Google Doc. All the work you do for the next 6 weeks will take place in that document. The first two assignments will be these two Try This assignments (the first semantic sweep to identify terms and this follow-up to dig into them some more).
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ENG 123 7.F: A Quick Guide to Prompt Engineering

Today’s Plan:

  • Proposals
  • Resource
  • A Quick Guide to Prompt Engineering
  • For Next Week

Proposal / Resource

From McIntyre and Fernandes,First, a list of Resources on Refusing, Rejecting, and Rethinking Generative AI in Writing Studies and Higher Education

A Quick Guide to Prompt Engineering

As you are aware, I’ve been doing quite a bit of research and writing with ChatGPT this semester. And, as you are also aware, I’m not necessarily incredibly happy about doing that research. But… Many of your research proposals are incorporating ChatGPT into your project. To help facilitate that work, I would like to go over some fundamental principles for working with ChatGPT. I have about 20 minutes to write this, so let’s see what I can do in twenty minutes. First and foremost, remember Ethan Mollick’s advice: “treat AI like a human but tell it what kind of person it is” (Co-Intelligence. That is, do not sit down and merely ask it to do something. Sit down and have a conversation about what it is you want to do. Let’s examine two sample prompts Mollick shares in the book:
  • You are an expert at marketing. When asked to generate slogan ideas you come up with ideas that are different from each other, clever, and interesting. You use clever wordplay. You try not to repeat themes or ideas. Come up with 20 ideas for marketing slogans for a new mail-order cheese shop, make them different from each other, and make them clever and creative.
  • I am stuck on a paragraph in a section of a book about how AI can help get you unstuck. Can you help me rewrite the paragraph and finish it by giving me 10 options for the entire paragraph in various professional styles? Make the styles and approaches different from each other, making them extremely well written.
Note: some of what Mollick writes here seems useless and vague to me, but the general principle is smart. Let’s examine how I and some of my students prompted ChatGPT while writing our own papers. What I hope you see from these examples is that getting quality material from ChatGPT requires you teach it how to write. Writing with GPT requires quite a bit of revision (as reprompting), and guiding that revision requires that you already know quite a bit about writing. This is one reason why having a rubric is helpful (whether one I supplied or one you researched and designed)–because you have to know what to tell the machine. Telling it to write something “extremely well” is really, really useless. I think I have found a resource to help with some prompt engineering. I stumbled upon this yesterday while researching how AI is being integrated into college-level writing classes. From Ranade et al, 2024, “Using rhetorical strategies to design prompts: a human-in-the-loop approach to make AI useful”:

I want to look inside the article at their use of Lloyd Bitzer’s (1968) research on “the rhetorical situation,” that will help us parse out the image above.

For Next Week

In your proposal, you developed a calendar of work that has to get done. Start doing work. Next Friday, I will ask you to turn in a link to your week’s work product and give me a quick overview of what you accomplished.
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ENG 429 7.R: Graham Project Response Paper

Today’s Plan:

  • A few resources I have found.
  • Canvas: Online Education Consortium Survey
  • Graham Project Response Paper(s)

A Few Resources I Have Found

From McIntyre and Fernandes,First, a list of Resources on Refusing, Rejecting, and Rethinking Generative AI in Writing Studies and Higher Education

Second, on the other end of the spectrum, two articles (the first cites the second):

From Ranade et al:

Graham Project Response Paper

As I indicated last class, I am going to compress two different projects together–the AI-Generated Paper process reflection and the Graham response. Here I am going to try and layout requirements and possibilities.
What Goes in This Paper?
First, I have a few headings I want you to use in the paper:

  • On Writing With AI
  • On Assessing AI Writing
  • How This Experience Resonates with Something(s) Else I Have Read
  • What I Would Say to Graham
  • A Final Thought Or Question

Those headings by and large grew out of the suggestions 7 (as of 12:42) people made in the Brainstorming space. What those headings mean and exactly what they cover is, I think, ambiguous enough to give you room to think and wander.

Let me share the first five responses to the question “

#1: I believe we should focus on why it took AI so long to be able to cite quotes or come up with quotes to insert into an essay. This was my biggest struggle in my essay which has made me believe that this type of technology is incredibly hard to prompt. I think it would be interesting to look over everyone’s AI-prompting questions concerning to the quotes and see what everyone did differently.

#2: I am really curious to see what prompting strategies worked over others. Is there a trend where more prompting made for better papers? Or was it quality? Or perhaps something in between?

#3: Essays from The Third Culture: Roger Schank in “Information is Surprises” advises that “the problem is the idea that knowledge is represented as a set of facts. It’s not. You might want to know those facts, but it’s not the knowing of the facts that’s important. It’s how you got that knowledge, the things you picked up on the way to getting that knowledge, what motivated the learning of that knowledge. Otherwise what you’re learning is just an unrelated set of facts. Knowledge is an integrated phenomenon; every piece of knowledge depends on every other one.” (Unfortunately) He turns this argument toward the possibility (necessity?) of the implementation of “computer programs that can do the kind of one-on-one teaching that a good teacher could do if [they] had the time to do it.” (173-4) It unsettles me that he is suggesting AI to do the kinds of “fact-based teaching” that he warns against in describing the cultivation of knowledge. His suggestion also accompanies Mollick’s suggestions that AI can teach individual students the course material, so that students can have more class time dedicated to engaging in the topic with their teacher. Increasing class sizes hinder this idealized system; even if students in fact do the work outside of class, the time allowed for meaningful interaction of the subject with teacher sin the classroom is divided among each student, and students in larger classes are less likely to speak up, to interact and actively engage with the discussions or activities at hand because “others are already doing it.”

#4: Could be good to reflect on the different topics Graham vs Santos asked us to combat: What does it mean to have AI look at a local issue that may be harder to pull info for as opposed to a broad topic that has a lot of emerging info in the field? What types of essays/assignments might AI provide learning benefits for, and where is it doing the exact opposite? How could this assignment have been different if we would have done the same exact thing Graham did?

#5: Graham says that “Ultimately, higher education is going to have to come to grips with AI text generation. At present, most of the efforts to engage these concerns seem to gravitate either toward AI evangelism or algorithmic despair.” (5) It feels like the two sides are at war a bit: either give students all the tools or deprive them from AI entirely. Where does the middle ground lie between giving students assignments to experience and understand the pitfalls of AI, while simultaneously exposing them to this quickly advancing technology? Is there a version of this assignment that could be helpful for high schoolers/college freshman to learn about what AI can do for them, while not encouraging them to use tools like prompt generation to get around actually writing a paper?

#6: “This isn’t ‘writing’ in the same way that line drills aren’t basketball. But that doesn’t mean there isn’t a useful pedagogical role here.” – I wrote my reflection already but this is my only direct quote from the article. I mention Graham a few times indirectly.

Ultimately, yes, I would want to read all of those things, but you probably cannot do all of those things in one paper. So let me move on to….

How Long Should this Paper Be?
This is a doozy. So, my original idea was that the AI reflection would be the longer piece (say 2400 words) and the Graham response the shorter piece (say 1200 words). So that adds up to 3600 words, or 11 pages double-spaced. And that feels about right to me. Say 3200 to 4000 words.

But in writing your paper, I would like you to use all of those headings above. Let’s say at least 250 words a heading? That allows you to give a few of them a quick pass and really focus your attention on one or two of them.

Sources?
I mean, we’ve read a bunch of stuff for class. I’d like it if you bring in one thing from outside of class? But don’t force it. If you’ve been reading stuff in other places that you can bring to bear on these questions, great. If not, then think with some of the things we’ve read here–particularly the Mollick and the Graham.

Paper Format?
Most of you know I prefer single-spaced, block formatted writing. That’s my jam. But you do you. If you turn in something that is double-spaced, I’ll just single-space it.

I don’t really care about APA or MLA format. I do care that I know what sources you are using and can potentially look them up if I need to read more. That means putting some AP (journalist) style context information in the body of your paper. If you don’t want to do that, they feel free to put on a more formal Works Cited or References at the end of the paper. Whatever makes you happy and lets me find the stuff you are citing/paraphrasing/thinking through.

When Should This Paper Be Due?
Another tricky one. Look, I am in grading hell right now and drowning in papers that I don’t have the energy or focus to read. Your papers will undoubtedly be a lot more fun to read, and I am looking forward to that, but I’m going to be realistic that I won’t even fucking touch them until the 22nd (I have an article submission due the 15th and a sabbatical application due the 19th, and some 301 job reports to grade and a whole set of first-year project proposals to review before I can get to these papers). So, a natural due date seems to be the 21st at midnight.

But that leaves us with the question of what to do in class next week. My answer is that you read the shorter version of papers. We do 8 on Tuesday and 8 on Thursday. We can call it sharing works in progress if that helps? Think of this as a Write-Up, a one-page, single spaced paper you’ll read to the class. You will get 8 minutes. Timer running. I would ask that you transform the selection of your paper into something that has, as Aristotle would say, a beginning, middle, and an end. Something that doesn’t just feel ripped out of the middle of a longer piece.

Tuesday readers:
  • Amber
  • Rose
  • Sam
  • Jaiden
  • Jocab [Sorry]
  • Carly
  • Luna
  • Matt
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ENG 301 7.W: Introduction to Grant Writing

Today’s Plan:

Today’s Plan:

  • Parts of a Grant
  • General Strategies
  • Grant Research / Tools
  • Revising a Grant Application for Concision and Readability

What Do Grant Writers Do?

Being a grant writer generally involves doing 4 things:

  • Meeting with organizations to determine their needs
  • Researching funding opportunities for specific organizations
  • Develop grant proposals with funding organizations
  • Developing and even implementing grant assessment strategies with organizations to inform funders of a project’s outcomes (and hopefully success!)

Parts of a Grant

Let’s take a look at the Community Resource Center’s Common Grant Application. While there:

  • User’s Guide
  • Tips
  • Length of a Narrative: 4 pages, single-spaced

Grant writing 101: A Few Introductory Tips

  • Invention/Organization: Always be sure to read an RFP / application form extremely carefully and provide exactly [only] what the app / prompt is asking for
  • Invention/Diction: Always scan an organizations website and promotional materials for language and terms
  • Research: It is easier to find funds for “new” projects than for “general operating costs”
  • Style: Your prose must be concise, yet detailed and engaging. Every word or sentence has to count (because)
  • Organization/Style: Your reader is under no obligation to read your entire proposal. They will likely skim. We have to do everything we can to make the most important part of our application the most prominent and accessible. We cannot waste words or lose attention. Every sentence, every word, has to be as sharp, engaging, clear, and accessible as we can make it.

Grant Writing Research Tools/Process

We’re not going to go through this whole list today. But if you choose to work on the grant research project, then you’ll be familiarizing yourself with this stuff.

For Next Session

I have designed an assignment that should help you put some of the writing tips from the CCGA into practice. To Canvas!

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ENG 429 7.T: Graham Project Reflection

Today’s Plan

  • Thursday in the lab: Helping out a Friend
  • Changes to this Project
  • A thing I wrote

Thursday in the Lab

I think I mentioned that my friend and former student, Dr. Kristen Gay, is doing some research on AI. I want to spend 15 minutes at the start of Thursday’s class responding to her survey.

Changes to this Project

Originally I had planned for you to write a response to Graham after the current reflection paper. I’m going to cancel that so we can begin the next project.

Instead of writing a response to Graham, I’ll ask you to incorporate that response into your reflection paper. I’ll also ask that you submit two versions of this paper–the longer one that you want me to read, and a shorter one (one page, single-spaced) that you will read to the class next week. I’ve already written my longer one, and I’m going to read it to you today.

Just Because the Machine Can Produce writing Doesn’t Mean It Is Writing

I started drafting this on Friday, when I was supposed to be grading. I was going to take 15 minutes. Instead I took 3 hours 5 hours (sorry 301 students, I didn’t get to your papers). I’ve swung back to it a few days later. I should still be grading reading and responding to those papers.

First a bit of background. In my 429 Rhetoric and Technology seminar, students used AI to generate papers. I revised and further developed an assignment shared by Scott Graham in 2022. We then scored those AI generated papers using the rubric the English department uses for institutional-state program assessment. To help spur reflective thought on the project, I distributed a survey that asked students to respond to a conclusion Scott Graham drew after conducting a similar experiment in 2022. Graham concluded:

“AI-generated essays are nothing to worry about. The technology just isn’t there, and I doubt it will be anytime soon.”

After our work this semester, I’m not sure I agree with Graham. As I discuss further below, our experiment has demonstrated that the quality of AI writing largely hinges upon the quality of the human writer prompting it. Producing excellent writing (according to our state standards) requires quite a bit of prompting, an intimate working with the machine. But the technology is closer, let’s say, then it was in 2022. And I think it will “be there” sooner than most of us writing folk would like.

I find myself in an odd institutional position. On the one hand, I’m by and large responsible for thinking about professionalization and career readiness for students. As I touch on below, I expect proficiency with AI to appear more frequently in job advertisements. On the other hand, my theoretical interests include people like Walter Ong and Gregory Ulmer, and I’ve thought and continue to think about how electracy (digital technology) challenges some of the ontological, epistemological, and ethical elements of literacy. I’ve already done some of that writing, and I hope to do more soon.

In part what I write below is a reaction and a response to Megan McIntyre and Maggie Fernandes’ podcast “Everyone is Writing with AI (Except Me).” It is an excellent podcast and worth a listen. In short, I challenge their interpretation of what it means to be critical about AI. I don’t think one can be critical of AI if you are not using it. This isn’t to say that their skepticism is ungrounded: the environmental, economic, and potentially racist dimensions of AI are really concerning. But if we are to steer the implementation of AI into our campuses, we will have to have hard data, or at least soft experience, to ground our positions. Two side notes and then I’ll end this preface.

  • Side note #1:, I don’t think you will find the kind of racism that Nobles describes in ChatGPT and other advanced LLMs because of guard-rails OpenAI has developed. That doesn’t mean it isn’t enforcing a putrid form of white, standardized, academic English. I have a few students experimenting with ChatGPt this semester on this issue, and they report that it is pretty difficult to get the machine to say something problematic. I know this was a problem with earlier releases, but OpenAI is investing a lot of labor into guard-railing GPT.
  • Side note #2, as I discuss below, it doesn’t take much direct prompting to teach the machine what you want your English to sound like. I think it is much easier to address the issue of language diversity than it is most of the others, although that might mean “selecting” a voice rather than developing one. This semester, our 429 seminar has been pushing against the machine, testing what it can do and exploring what it cannot. In short, if you feed it some of your *own* writing, it can quickly learn to mimic that. Your voice can be its own (apologies).

My students are writing reflection papers on this first project, and below is what will count as mine. So here’s three reasons for being concerned about the future of artificial intelligence and writing (both academic and professional). And a coda on what learning should mean.

#1 Cheating will become a REAL problem. Not for every student. But for every student who doesn’t care about learning, cheating will be as easy as breathing.

One student in our class, Luna, composed a pretty amazing paper using AI. Beyond its argument–which is smart–and its use of sources–which is uncharacteristically sharp–Luna’s paper had a real “voice.” Luna’s ability to generate a voice is a concern, certainly on the “cheating” front. How did she do it? By feeding the paper a short amount (say five pages) of her own writing and asking the machine to mimic it. That’s it. And it worked. Really well.

This troubles me because voice and personality are probably the best dimensions we (veteran writing instructors) have for identifying cheating. Just like when someone drops a quote in the middle of a paper, we can hear it. Voice isn’t the only thing that my class and I identified as weak points capable of identifying AI writing. For instance, the machine has a general inability to deal with a direct quotation, both in terms of contextualizing material and (even moreso) doing any analysis of a quote. Getting the machine to do those things requires a large amount of prompt engineering, directing the machine to react to words or to think about how a quote resonates with a previous part of the paper. That’s another weakness: AI writing shows almost no intertextuality without extensive prompting). But here’s the thing–developing writers struggle with those elements too, especially citation. In fact, we (Composition Studies) have an incredibly in-depth and longitudinal research project, the Citation Project, that shows that developing writers (those in first-year required classes like ENG 122 or ENG 123) really struggle to *meaningfully* incorporate quotes into research papers. Folks hypothesize that this comes not just from laziness or lack of investment, but also in a decline in critical reading skills. I’ll circle back to that below. For now, I will just say that if the machine can already develop a voice based on a sample of writing, then detecting cheating will be damn-near impossible. If it can learn to be a “good” writer like Luna, then it can also learn to mimic being a developing writer. Let’s assume a future in which detecting AI writing is impossible.

But here’s the real thing: I don’t care if I can’t detect it because I really don’t want to be a cop (and the MLA/CCCC doesn’t want us to do that either). I don’t want to surveil and police writing.

I haven’t had to write a teaching philosophy in a long-time. But if I am spit-balling what is important to me, it is to reframe the classroom as a space of opportunity for people to do things. Sometimes, those are things that they want to do; other times they are things that I think they need to try (in order to become better humans, better thinkers, better Writers, or better citizens; rarely do I care if they become better students or “writers”). I’ve long ago stopped using the word “students” whenever I can and am being thoughtful and careful; that word sustains a power imbalance between me and the people with which I work. Let’s be real, there is a power imbalance–there’s a grade book that only I have access to. But I can, through assessment strategies and assignment design and direct communication, try to offer them the opportunity to “take the power back.” Or, at least, to take responsibility for what they want to learn and do. There are some courses, like ENG 328 (Graphic Design) or ENG 301 (Writing as a Job) where I feel a greater obligation to “discipline” (Foucault) students, to alert them to external expectations that should become a part of their internal process and self-regulation. But ultimately, I teach the rules so that you are self-aware of when you want to break them. If you want to design a flyer for a poetry reading in Comic Sans, by all means knock yourself out. But know that I will mock your choices. Also, know that you are free to make them. I’ve adopted ungrading as a way of making the my classes a safe space for experimentation, potential failure, reflection, and growth. Grades are bullshit for many reasons, but primarily for me because they tend to inhibit all of those dimensions of learning.

That’s a lot of writing, and I’m probably feeling self-conscious here. I think I wrote all of that because I want to stress that I am not someone who wants to police “students.” I want to develop environments in which people can learn. But I am also postpedagogical. Pedagogy is the word we (folks who study/care about teaching) use to describe not “what” you teach (that’s curriculum), but “how.” Cicero might be the first postpedagogue. He wrote that “the greatest impediment to those who want to learn are those who want to teach.” (Well, he actually wrote that “The authority of those who teach is often an obstacle to those who want to learn” and I’ve been misquoting it for years now. Whatever.) “Teaching” too often is a telling, an ordering. A telling of what to do. An ordering of a chaos. It makes this whole thing, this system, this building, this class, seem so simple and efficient and possible and worth investing in. I show you what to do and then you do it. And if learning isn’t happening, then either I am a bad teacher or you are bad students. We (everyone, always, already) are the failures, because the system is just so good.

But if you’ve been around this place as long as I have, then you know that is bullshit. That is not how any of this works. Sometimes, maybe it seems that way. I but assure you it isn’t (and I will not, I repeat I will not lecture about phenomenology and how you have taught yourself anything you have learned, anything that out of experience you have transformed into theory and repeatable practice. I will not talk about phenomenology and consciousness and metacognition. Nope). A mentor used to say that he knew a person had arrived when we (emerging scholars) surpassed our self-perceived reliance on him, when we no longer needed him, when we overcame him. It wasn’t a battle metaphor, or if it was, it wasn’t about us defeating him as much as us conquering a preliminary stage of imposter syndrome. We recognized that we could do it on our own. We carved our own path. I’d like to say that we didn’t just produce some writing, but we did some Writing or maybe even experienced W-R-I-T-I-N-G.

A concern I have, and will work out more fully below, regarding artificial intelligence is that it undermines learning; it just writes. So much writing, done for us. “Good” writing in the sense of White-Standard-Academic-English. But if it writes, then no one is learning (except it?)–no one engages that messy and painful process through which we (humans who accept the struggle, the frustration, the fight, the work) learn that we can do things. Hard things. I’m not just talking about learning to craft worlds with words or move mountains with metaphors. Here, I’m talking about learning about our own capacity.

But first let me conclude this section emphasizing that if we (back around to teachers who care about learning) are going to make a place for Writing and W-R-I-T-I-N-G in our classes, then we are going to have to convince students, those people paying to sit in our classes, that they are worth fighting for. They are worth the frustration and the effort and the pain and the frustration did I mention the frustration of, as Jim Corder describes it, reducing the wild infinite possibilities of existence down into dumb, frustrating, inadequate words. We have to trust that students want to experience and learn, rather than design systems that “ensure” that they will (as if that’s even possible). We can no longer “order” their best interests. Learning outcomes will be sales pitches more than strictures.

#2 “Who cares if it works?”. That’s a line from Robocop, a hyper-violent sci-fi action movie from the early 1990’s. In the movie a corporate executive designs an automated police robot in an explicit effort to make policing more cost efficient. There is a demonstration that goes woefully wrong. After the “disappointing” demonstration, the CEO orders the production of another prototype project, the titular RoboCop project. In a later scene, in the men’s room, a metaphorical pissing contest takes place between ED-209 and Robocop’s respective designers. A snippet from ED-209’s designer:
“I had a guaranteed military sale with ED. Renovation programs. Spare parts for years. Who cares if it worked or not?”
There’s a real corporate, economic history that underwrites that line (I think particularly of the Ford Pinto and the cost-benefit analysis that prompted Ford to let people die in crashes because it was cheaper to pay insurance payouts than recall and fix the cars). I have little faith in this era of late-stage capitalism that anyone will do what is good for writing. “Who cares if it works?” Is that even a [rhetorical] question?

I’ve been haunted by this scene so many times while in academia. There’s an old content management system, Blackboard, that absolutely resonates with that lack of care. At UNC, I’ve used so many digital project management systems that clearly lack any investment in user experience (looking at you Slate). For our purposes here, those lines foreground my concern for large-language models like ChatGPT.

Overall, I think people who struggle with writing would think most of our papers are marvelous. Such “good writing.” Those are the same people who don’t want to pay for writing. Because of those people, business faculty at this school make over 100k and I make 66k (after my “big” summer raise!). I’ve long thought that people who *can’t* write or play music or draw or make art often suppose that those of us who can just have a “gift.” There’s a jealousy that leads to a dismissal of the work. And/Or maybe there’s just another manifestation of corporate greed that doesn’t want to pay for labor. Who embrace technology in the worst spirit of Heideggerian efficiency. Whatever. Maybe that’s me making unfair sweeping generalizations out of spite. Maybe. But I believe one REAL reason for concern is that the machine is getting good enough that I don’t think non-experts will see (or “care” about) the limitations that we (writing folk writ large) all clearly do. Or they won’t want to see. Whatever. I have little faith that the folks who hire and fire will truly care about Writing if the machine “works” well enough to sell it to someone who knows/cares even less about writing.

My ENG 301 class found that 11 of 92 jobs this semester called for experience with AI–what will that be next year? Given what I have read and heard from folks working in the field, that number will continue to grow rapidly. I just read a celebratory NPR article on how great it is that more people are adopting AI in the workplace than previous technologies and research would have anticipated. I’m assured it will be great for the economy. Sarcasm and cynicism aside and general loathing of unfettered capitalism aside, here’s where my institutional position weighs on me–between wanting to encourage learning and recognizing my responsibility in preparing writers, editors, and designers to enter a job market seduced by? infatuated with? invested in? exploring the effectiveness of? the machine.

I hope this “concern,” about AI replacing human intellectual labor, is unfounded. I *hope* that people will see the machine’s limitations. (But then again, “who cares if it works?”). I *hope*, maybe even expect when I am feeling particularly positive, that, due to the machine’s reliance on human metacognition, that writers will be in more demand as long as we call ourselves “prompt engineers.” Maybe.

#3 I care about learning. As I wrote above, our Graham Project demonstrated a few general weaknesses with AI. More importantly, we’ve shown that metacognition of writing correlates strongly to paper quality.

So a more longitudinal question is whether we (Rhet/Comp folk) care about where the words on the page came from. Do we care about ideas and process and structure (genre)? Or about words (product on the page). In short, does it matter is writing becomes sitting at the machine and asking it to produce words? If the machine frees us from that frustration Corder describes?

I am being somewhat facetious here, because I would argue that the complexity and struggle of putting words on a page affords the ability to think with words (nuance, sophistication, critical dissection). Writing, of course, requires critical reading, and critical reading is learned through writing. Through reading, we gain awareness of both genre/structure (what moves could this writer make) and attention to decision (what moves did she make?).

When I read a block quote, for instance, I am identifying all the elements of the quote that could command response. After the quote, I am focusing attention on what she chose to respond to, because that tells me what she thinks is important. Critical response–my own writing–often begins by wondering what else she might say. This thing is already long enough, but here’s where I could insert some Walter Ong and feel right at home: the idea of thinking in literacy as “deep” engagement, and thinking in electracy as horizontal association.

So much of our disciplinary blood is dedicated to notions of “process.” I remain suspicious if our praxis, particularly our assessment practices, actually reinforce that. That’s another conversation for another day, but it explains why I am being facetious here–because the machine is *really* going to put that commitment to the test. The question I want to sharpen here is about whether instruction in writing has always been about prompt engineering as rhetorical / genre training. Whether it really matters how words got on the page if the intention behind those words lies in the writer. Who sets the purpose? Who distributes the product? What change does this writing hope to engender? Can we use the machine to democratize writing, to allow more people to create and distribute words that mean something to them and their world? Does it matter if syntax and diction were auto-generated for them? Does rhetoric care less about the choice of medium? As a young graduate student I had an opportunity to talk to Cynthia Selfe at a CCCC’s conference, and I asked her how she justified her use of digital composing tools and softwares. And she responded, with some Aristotle, that her job was to teach people to compose via “all available means.” What if the machine is another available means? One that, in the words of Ethan Mollick, promises to “democratize” creativity and expression?

Again, I’m being somewhat facetious here, and making a counter argument that I don’t think I believe (honestly, I’m not sure). But I think the challenge before us–my reason for concern–is to convince people that why that matters. Especially those people sitting in our classes and wondering why they have to care about writing.

And I don’t think our current model of K-Higher Ed education, as a Hunger Games style contest of survival and competition, helps. I don’t think streams of standardized tests that rank and file help. I don’t think GPA’s help. I don’t the system in its current form lends itself to making the argument that we have to make: that learning is what is important. The system tells students that grades matter. And, as I have already suggested, I don’t think grades and learning belong in the same sentence.

But to understand what I mean by learning requires a bit of a coda, of a concluding piece that refigures what has come before.

Coda
Throughout this piece I have played with a distinction between writing, Writing, and W-R-I-T-I-N-G. Two things are influencing these distinctions. The distinction between Writing and writing draws upon the distinction between Thought and knowledge made by Bill Readings in his book The University in Ruins. Readings was writing in the mid to late 1990’s, attempting to make sense of the radical changes he saw transforming higher education. He increasingly saw schools losing their historic mission (to transmit State/Culture and/or to craft souls). He didn’t lament the loss of those things so much as he worried about the new mission: the creation and transmission of knowledge. Selling answers. Demanding answers.

Readings’ book is fairly complicated and I will fail to do justice to it here. But, damn it, I am going to try. In his final chapters he tries to create a rationale for the University that steers it away from becoming a souless, bloodless knowledge factory. Instead, he believes, it could reharness the energy of its origins and become a site for questions. For asking questions that stubbornly resist being answered. These questions he calls “Thought.” The University as a site of encounters with Thought.

Below I want to share a few quotes from Readings’ work. More than any other book, I think University in Ruins changed the way I think about teaching, about my role. In part because Readings invested in Levinas, and his discussion of teaching in terms of obligation (rather than spontaneity, self-realization, what he calls the Kantian Enlightenment inheritance) resonates with me.

More than anything, Readings believed education is, like the Yeats poster on the door to faculty offices, the lighting of a fire (and not the filling of a bucket). When teaching stops being the filling of student brains,

Teaching becomes answerable to the question of justice, rather than to the criteria of truth. We must seek to do justice to teaching rather than to know what it is. A belief that we know what teaching is or should be is actually a major impediment to just teaching. Teaching should cease to be about merely the transmission of information and the emancipation of the autonomous subject, and instead should become a site of obligation that exceeds an individual’s consciousness of justice. (154, emphasis original)
No individual can be just, since to do justice is to recognize that the question of justice exceeds individual consciousness, cannot be answered by an individual moral stance. This is because justice involves respect for an absolute Other, a respect that must precede any knowledge about the Other. The other speaks, and we owe the other respect. (162).
Rather, to listen to Thought, to think beside each other and beside ourselves, is to explore an open network of obligations that keeps the question of meaning open as a locus of debate. Doing justice to Thought, listening to our interlocutors, means trying to hear that which cannot be said but trying to make itself heard. And this is a process incompatible with the production of (even relatively) stable and exchangeable knowledge. (165)
To believe that we know in advance what it means to be human, that humanity can be an object of cognition, is the first step to terror, since it renders it possible to know what is non-human, to know what it is to which we have no responsibility, what we can freely exploit. (189)

I don’t have time right now, but here’s where I can write the thing that I’ve always wanted to write but never quite got around to doing: to thinking about postpedagogy as an ethical relationship with each student in which I try to let them dictate the grounds of our experience.

My favorite line from Readings: “Thought is an addiction to which we cannot break free” (128; see “we are addicted to others, 190). But the we here–that one is difficult to pin down. There’s many people (and a lot of teachers) who are afraid of Thought. Who want or need to order the chaos.

You know you are in a weird place when you need Victor Vitanza to clearly explicate something for you. But here I am. The distinction between W-R-I-T-I-N-G and writing comes from Vitanza’s 2003 essay
“Abandoned to Writing: Notes Toward Several Provocations” in Enculturation.
Vitanza was charged with examining the relationship between Rhetoric and Composition, the slash: rhet/comp. His (third) sophistic response is to challenge both disciplines to examine what they want from writing, and based on that desire, to recognize what they might force writing to be. In doing so he hopes to reopen the question of what writing itself might want, what plural forms of writing become erased in the name of a controllable, articulable, teachable form of writing. One that can be efficiently assessed. One that shows its value as standing reserve. Vitanza’s prose can be hard to cite–and, frankly, to understand, but it is worth experiencing here:

Perhapless, there are two possibilities here: “We” can start teaching writing precisely as the university needs it taught. Or “we” can attempt “to teach” writing the way “we” want. But there are, let us not forget, third (interval) wayves. And therefore, “we” should ask: What is it that writing wants? I suspect that “writing” does not want what either the uni-versity thinks it needs nor what “we” think we want.

Taken seriously!? In an institution! Writing scares, frightens, threatens institutions! Take, for example, Jean Genet’s writing in prison and Jean-Paul Sartre’s “Introduction” to Genet’s writing. Take Hélène Cixous’s thinking of Genet in “prison.” Think of misprisions. On the contrary, at our institutions, we are taken far more than seriously. So-so-seriously. That’s Why we would be suppressed so that we could dis/engage by wayves of “learning” students to write from their impotence while at the uni-versity and while graduating into yet other institutions! But then, Who says anything like this at any uni-versity! It is, after all, just silly!

I will skip (rocks across the sur-face) what “we” might want writing to want. Writing just wants. Wants, W.ants. It’s not that writing wants what “we” want when “we” know what “we” want! Rather, WRITING WANTS! Just WANTS.
[…]
Yes, “writing” is about sea CHANGE. And that, my d.ear.est, is why we are afraid of the thing called W~R~I~T~I~N~G~, and why we insist on “teaching” writing and IN institutions! I understand that YOU are afraid of the DRUNKEN BOAT.

The DRUNKEN BOAT was an experimental digital poetry publication. That the link in Vitanza’s article is now dead, a 404 error, seems indicative of what happens to W-R-I-T-I-N-G that does not meet the institutional, professional, efficient, “working” standard.

If I synthesize these two influences together, to engage in W-R-I-T-I-N-G is to try and be(come) in proximity to Thought. An experience of a moment in which something emerges that you cannot believe you thought. From where did this idea come to me? From Thought, approached in a moment of W-R-I-T-I-N-G. Writing as a verb, unfolding and happening and “pac[ing] upon the mountains overhead / And hid[ing] [her] face amid a crowd of stars.” (W-R-I-T-I-N-G will always be a woman for me; one I desire but thankfully cannot control). Vitanza reminds us that we cannot disentangle W-R-I-T-I-N-G and desire. Why would you try? Love and passion are not about control, or at least they shouldn’t be.

writing, with a small w, is about reducing the impossible to the assessible. It is about efficiently scoring a paper and the person who wrote it. It feels to me more a form of police work than an ethical engagement with a person or people. Some people want to discipline, to make writing right. I don’t want to do that. I want to make possible an experience of W-R-I-T-I-N-G.

Writing is reaching to an audience, reaching out, imagining responses that you will likely never hear. Sometimes I write to the living, sometimes I write to the dead. But in both cases the responses are imaginary, others that haunt. W-R-I-T-I-N-G might lurk under the spectral sur-face of those imagingings.

The machine is as far as one can be from Readings’ pedagogy of Others and alterity. All it is, all it does, in Levinas’ sense is “return the same.” It feeds our narrative back to us. The faceless commonplaces that circulate. From its desire to please to its synthetic processing of expected tokens (words) to its white-washing of language into a cold and lifeless spew, there’s no love there. There’s no encounter with an other. There’s no wilderness to explore. It institutes institutional writing, returning answers to questions with confidence and pleasure. It never tires, though your free trial might expire. There’s no surprise, though you might feel the uncanny. Readings reminds us that Thought is non-transferable precisely because it is a disorienting experience of difference that in turn changes us to think about ourselves differently. It cannot be packaged or commodified. It cannot be taught, only learned. The hard way.

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ENG 429 6.R: Reviewing our Reviewing, Planning to Reflect

Today’s Plan:

  • Let’s Talk Scores
  • Graham Reflection Essay Brainstorming
  • Let’s Listen to Something

Let’s Talk Scores

We have data! What does it mean!

Before looking at the scores, here is what I wrote to a friend after reviewing the essays:

After grading 10 AI Generated essays this morning, I have many many thoughts. Too many to sort through no this little sleep. One essay was absolutely amazing. A few others were pretty bad.

There’s so many variables that I don’t know–about how much effort someone put into their paper, about their metacognitive skills when it comes to writing, about how much they tried to put their metacognitive abilities into prompt engineering, about how they fed it source material, that it is hard to say anything definitive.

Graham Reflection Essay Brainstorming

Let’s turn back to the assignment sheet:

Graham Project Step #3: Reflection Essay
After you have generated and assessed your AI paper, I will ask you to compose a reflection, a postmortem of our project. I will specify what I want more in class–because I have not taught this project before, I do not want to too narrowly stipulate upon what you should reflect. I think the specific topics of this reflection will emerge throughout the work. We’ll spend some class time generating potential avenues for reflection.

I do imagine that you will discuss the overall quality of the papers you examined. This will likely involve reflecting on Graham’s conclusions. I also am curious as to the quality of the language model’s feedback. I will ask you what thoughts, questions, concerns you have as we conclude the project–this is a course on ideas. Tell me yours. Finally, I will ask you to assess your work on this project and tell me what grade you have earned.

I want to give you some time thinking about those potential avenues for reflection. What are some ideas or questions you would want your classmates to address? What are some argumentative positions we might explore?

We are sitting in front of computers, so let’s do 20 minutes of writing. Then we can talk.

Let’s Listen to Something

Something!

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ENG 301 5.M: Job Report, Miller Discussion

Today’s Plan:

  • Writing Up Findings
  • Job Report Overview
  • Discussion Section
  • Miller

Writing Up Findings

By now I’m hoping you have some graphs. Now we need to write a descriptive paragraph of each graph. I’ve got a web page that can help with this.

Job Report Overview

Here is a link to the rubric. The rubric is in many ways the assignment sheet.

Task
I am tasking you with writing up a report that can be distributed to various audiences to promote the WEP major [Writing, Editing, and Publishing] or Writing minor here at UNC. You should pick the program that is most relevant to you (and you can write a report about both). The report should present itself as objective research into the job market more than an attempt to sell our programs. But the primary goal is definitely to sell those programs. With subtly and research-based findings, of course.

Audiences
Our primary audiences are high school seniors and first-year students at UNC. We might also market the Writing minor to sophomores.

Our secondary audiences are primarily parents of those students, especially if they are paying for their students’ education and/or are hesitant of an English (humanities) degree. You might know the type.

Our tertiary audiences include: students who believe that a Writing, Editing, and Publishing degree is too similar to a business writing degree, folks who might oppose the idea of being trained to participate in a capitalist machine (think about how some of Miller’s colleagues were skeptical of professional writing as a humanity). How do we assuage that concern? BE SUBTLE.

For those of you writing about the minor, how might you frame the value of creative writing classes to those audiences–especially people who do not feel that they are creative or that there’s any value in it?

Structure:
As the rubric indicates, we will have an Introduction, Methods, Findings, Discussion, and Conclusion section. We will also have a title page and a table of contents. Make sure you have page numbers and a running head that does not appear on the first page.

Note #1: I am assuming at least a few people have never created a table of contents and don’t know what a running head is. That’s okay. Pretend like you’ve been hired and your job involves figuring that out.

Note #2: if you Google “professional report” you are going to get all kinds of templates that are highly visual and colorful. That isn’t necessary. I don’t want you to wrestle a template. Good old fashioned home cooking is fine.

Note #3: if you Google something like “business formatting” you are going to get an absolutely wild range of information. The rubric contains my preferred “in house style”

Word Limit: the word limit for the first draft is 1600 words. The discussion section has to be 500 words.

Deadline: Saturday at midnight. If you can turn it in early, then great. Ideally I would read 5 on Saturday, 8-10 on Sunday, and 8-10 Monday morning. If I read more than 10 of these in a single day, I get cranky.

Some resources:

  • A list of WEP courses might help you
  • A list of Writing minor courses might help you
  • Discussion Section Revisited

    When I went to check this at 12:45 today, I only had five responses. Let’s wait until Wednesday to address this. If you need it, then here is a link to the form.

    Miller

    What is positivism? Why is it a problem for technical writing? What does Miller identify as the most problematic dimension of a non-rhetorical approach to scientific communication?
    Adrien:
    Positivism is a view in which human knowledge is a matter of getting closer to the material things of reality and farther away from the confusing and untrustworthy imperfections of words and minds. Science is a model of this. Within this model of thinking, technical and scientific rhetoric becomes the skill of subduing language so that it most accurately and directly transmits reality; it aims at being an efficient way of coercing minds to submit to reality. Miller identifies the most harmful effect of this view on technical and scientific rhetoric to be the vision of technical writing as a coercive endeavor, which continues to push the idea that rhetoric is not humanistic, and is in fact a way to coerce the mind.
    Ren:
    I think that the qoute that best summarizes this idea is “positivism is the conviction that sensory data are the only permissible basis for knowledge; consequently, the only meaningful statements are those which can be empirically verified” (Miller 612). Miller argues that passivist theory was applied to technical writing it would be just a lot of data with no conclusion, because science isn’t independent of rhetoric due to nothing in our universe being completely independent

    Shannon:
    Those who believe in positivism are attempting to separate what cannot (and I would argue should not) be separated. No human being can be completely objective, especially in pursuit of an objective truth. Every single human is different in a million ways, and therefore, has various views on what “truth” is and is not. Because traditional humanities courses tend towards a more subjective view, the opinion (which is subjective in and of itself) of positivists seems to be looking down its nose at us as humanities major.
    Me: There was a time, not too long ago, when the humanities also sought objective, universal truth. Literature taught us what it means to be human. Literary works had one meaning that we could discover through close and careful reading (etc). Rhetorical theory could help us communicate truth to folks who most need to hear it.

    Why does positivism/scientistic thinking lead us to devalue a traditional humanities course?

    Sara:
    I believe that it’s important to define science, and the ways that society has come to recognize its authority and significance. Historically, science has always been a practice: we are continually learning and revising and discovering more in the scientific realm and we are unlikely to run out of things we need to understand better. Science is a field of learning, which means that we don’t have all the answers and that most of our basis of knowledge is ripe for revision and deeper understanding. I consider it vital for an aspiring scientist to be able to say “I don’t know” frequently, and with the utmost confidence.

    Now, however, we view science as an authority that should know, and if they say they don’t then we are distrustful and label it as “fake science” or a plot. Miller details how positivism in science insists that there should be no discourse about the findings of scientists, that their goal is simply to be as objective as possible. But science is done by human beings, and what we study and why we study it is all subjective and motivated by human biases. The process is contaminated by the “disease” of humanity from the start. I say disease because people often see human opinion and feeling and emotion as the enemy; many would eradicate human subjectivity out of a desire for comfortability and simplification. But science isn’t about either of those things. Science challenges our humanity and reveals our ignorance with every peeled back layer, every layer peeled by our human curiosity and desire to understand the world.

    My response:
    There’s a difference between the sciences and Science. The former names a collection of practices, coalitions of people who do science (a series of tests, experiments, analysis, guess work, confirmation, etc). The latter names an almost transcendent ontological entity that commands human knowledge and regulates Truth. A marker of unquestionable authority. Now, I would argue, most scientists don’t believe in Science with a capital S. (And I am stealing the small s vs capital S from a philosopher/rhetorician of science named Bruno Latour). But captial “S” science dominates the way most people tend to think about scientific work: certain, objective, authoritative, final. I’m thinking about a comment Wyatt made–that while college tends to be more communalist in its approach, high schools are much more likely to be positivistic. Is that true?

    How can we describe the humanistic value of a technical writing course?

    Miller identifies 4 problems for technical writing pedagogy that stem from the positivist tradition. How do we avoid them?

    Molly:
    For example, “be objective, be unemotional, be impersonal.” In addition, from what I understand, is that positivist epistemology orders humans to understand subjects in specific ways. Miller also introduces the fact that scientists adopted “conventions… for staying out of the way of the subject matter.” This included using passive voice and the third person.

    Wyatt:
    Teaching people that the inclusion of human emotion is inherently less factual creates a belief that those who can disconnect themselves the most from their humanity, would thus be able to provide a claim that is of a higher level of insight, intelligence, and credibility.

    Adrien:
    Emphasis on style and organization seems to coerce a writer into falling into scientific ways of thinking, essentially destroying the idea of invention. If, in this way of thinking, science is meant to discover facts which are already in existence, technical writing becomes a way to teach recipes for description of mechanism, process, classification, and interpretation of data. All this does for technical writers is teach them how to use cookie-cutter modes of thinking and writing brought about by this institution of thinking, effectively distancing them from the entire purpose of writing: communicating clearly, effectively, and with the understanding that it will be read. By simplifying the words used, technical writers lose the comprehension and legibility so desperately needed in scientific fields, and make way for an inhuman, unemotional reading experience.

    How does Miller–writing in 1979–describe the epistemology that is replacing positivism?

    What does it mean to teach technical writing from a communalist perspective? Why might some students reject a communalist approach to teaching writing?
    Adrien:
    Some students may reject a communalist approach to teaching writing simply due to the fact that it requires rethinking their entire framework for not only science, but technical writing as well. It may be perceived as their entire system of beliefs being tossed out a window, for lack of a better term, and being forcefully replaced with someone else’s beliefs. That said, I believe the methods used by the teacher of this belief system makes all the difference.

    Carly:
    A humanistic perspective, as described by Miller, is the view that “knowledge is not discovered but constructed through communal agreement and shared understanding.” This approach shifts away from the positivist idea that facts are objective, self-evident, and exist independently of human interaction. Instead, it emphasizes that truths, especially in scientific or technical contexts, are shaped through the negotiation, discussion, and consensus of a community.

    I assert that Miller’s grounds for labeling technical writing as a Humanity lies in what she identifies as a consensualist relation to audience. Why do I think this? What does this mean?

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ENG 123 5.M: Research Methods / Research Questions

Today’s Plan:

  • Review Business
  • Potential Research Projects
  • 20 Minutes of Thinking and Talking
  • For Next Session

Some Review and Business

This week and next week we will be working on proposals. I will formally introduce the assignment on Wednesday. I wanted to spend today reviewing some previous material and then giving you some time to talk and think.

After what I deemed fair warning time, I deleted the original Further Thinking assignment. If you got a zero, you are welcome to resubmit.

Potential Primary Research Topics

I am asking you to develop a primary research project in which you use AI. Regulation folks, I am still wrestling with how you might use AI. I do think there’s several potential research topics for more traditional research papers. I started working (and wasted 45 minutes today) writing a research paper prompt about this MorePerfectUnion video. It brought me to this Food and Wine article which brought me to this letter from Senators Warren and Casey. And chasing down the inflammatory quote from the MorePerfectUnion video brought me to this NPR article, which offers a pretty balanced view of the issue (and puts that inflammatory quote into a different context).

But I’m not sure there’s an authentic research question here. There’s a question–should we be concerned about EDGE pricing and AI facial recognition in grocery stores. But with an authentic research question, I should be able to see what I can go test or do to help advance the issue. I’m not sure I see that here. I have the Warren & Casey letter and the NPR article. But what do I do next? And how do we avoid the commonplaces?

If anything, I think we can look at the 11 questions Warren and Casey propose and ask if those questions, and the forms of potential regulation they imply, are necessary/wise/productive/dangerous/adjective of choice? Meaning this sets up a potential exploration of forms of regulation we (as a nation) might want to explore. If you want to discuss a particular kind of economic or social regulation, then you need to prove the danger that such a regulation needs to prevent–that’s where research can come in. [And, one last link, the FTC is currently investigating what it terms “surveillance pricing.” I’m sure Noble would have something to say about this.

I’m also thinking about this interview (starts at 56 minutes) with Tristan Harris who gave this (in)famous talk called the AI Dilemma and is the chief officer of the Center for Humane Technology. Which leads to this. Some kind of paper that tries to articulate sensible regulation that also addresses the “roll out” argument (“if we slow down, then we will lose to China [or the terrorists etc etc”). How do we find a path through those commonplace starting points?

Okay, creative and education projects. One dimension of this project is generative: I want you to make something with AI.

How you use it is up to you–do you want to try and create or recreate something? Do you want to see if you can identify its limitations? Do you want to investigate whether humans can identify its use? Do you want to analyze its output? Do you want to use it to create educational materials? Art? To test a software/app?

Whatever you decide to do, you also need a way to analyze or evaluate it. This can be tricky. I think this second component, analysis, is trickier than the first, generation. We will worry about evaluation *after* you’ve pinned down what you want to do/make.

Projects in the works:
Ben, psychology students’ attitudes towards AI integration into therapy (see article)

I was reading Sarah’s paper and she wrote the line: “It is “tempting” because there is no decrease in quality with the use of artificial intelligence, only a dwindling of genuineness and consideration.” I responded:

Hmm. I’m not sure I agree with this. I do think there’s a difference in quality of writing. But I cannot easily disprove this. The evidence for my disagreement comes from reading bad AI generated writing on the internet and in my current research. It lacks voice.

It would be interesting to see how many people share my view. Have, say, 3 humans write a short paragraph about their favorite food or memory. Then have AI generate 3 similar paragraphs. Have research subjects rate them on a few different elements (engagement, feeling, etc). Compare.

The tricky parts here: how do you pick the three humans? We would probably want humans who are great writers or love to write. Who would prompt the AI?

I’ve also got an idea for a project kicking around. Last month as I was prepping for class, I came across a Google Chrome plug-in called Vera: The Truth AI. Let’s take a look. Let’s think about what we could do with this. Let’s think about why this might be dangerous.

25 Minutes of Thinking and Talking

Stages:

  • Everyone take 5 minutes to think / write
  • Group up and share 10 minutes
  • Quick Sweep

For Next Session

A few things:

  • Catch up. If you haven’t yet read your article from the library session do that. Write a paragraph or two about it (first paragraph on what the article seeks to accomplish, second paragraph on why it might or might not be useful for you and your research project). You will be writing about research soon.
  • Think more about today’s activity and what you might want to do. Maybe spend 20 minutes giving it a test run (with GPT, or internet searching to see if you find something)

If neither of those sound fun and you are struggling to think of an idea, then spend 20 minutes reading from the Try This! textbook. I’ll be asking everyone to read a chapter or two from this book depending on the kind of research they want to do. It might help you think of a project.

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ENG 301 4.F: Making Graphs, Writing Findings, Thinking

Today’s Plan:

  • Making Graphs
  • Writing Up Findings
  • Data Analysis with ChatGPT
  • Thinking (about Discussion)

Review

We’re writing a professional report. It will have the following sections:

  • Objective
  • Methodology
  • Findings
  • Discussion
  • Conclusion

Methodologies Email

Got a question about methodologies, thought I’d share my response:

Yeah. Our coding scheme is based on B&Ls, but has been transformed through different iterations of this research project. So we have to tackle that history.

We also have to explain what a coding scheme even is. Did you know what qualitative coding was before you took this class? Maybe, but probably not. So how can we explain it in an accessible way.

A real challenge here is you have to juggle audiences. I have made this intentionally confusing and ambiguous at this point. In an academic/science/governmental/grant kind of report, you have to be incredibly detailed here. This are “prove you REALLY know what you are doing situations.” For a more public-facing report, we want to be careful not to drown our reader in superfluous details.

I have made this confusing by *not* telling you which audience to prioritize. Why? Because I want you to try and do both. At the same time. You need to provide quick overview glosses *and* more extensive details. At some point, we’ll start talking about word count, and I will keep trying to make you do more with *less* words.

But one way to juggle the accessible / detailed dilemma is through the use of appendices!

Making Graphs

We have data! Now it is time to turn this data into graphs. Ideally, we would have done this Friday in the lab, but that didn’t happen. So I will demonstrate and point you towards some resources.

The easy part will be generating our graphs. The more difficult part will be revising our data to make a more rhetorically engaging draft. Remember that human attention is limited. Our job, as professional writers, editors, and designers, is to capture that attention, hold it for as long as we can, and make sure we pack every phenomenological second with meaning. We need to create hierarchy in everything we write and design to help maximize communication.

Initial Steps:

  • Step One: Make a Copy of this simple spreadsheet
  • Google has documentation for making a graph. Let’s run through the formatting options.
  • Make sure we title our graph Figure 1. Some Short Title
  • Does our graph need a legend?
  • Make sure labels are useful and legible

Revising our Graphs:
Now let’s look at our graphs like rhetorically informed information designers. How can we make them more impactful? Meaningful? What data do we NEED to share? [Note: this will be an even more recursive process once we run through the discussion section material; what content do we need? And this might help us think through some design possibilities.

Explicating Our Graphs
Next week I will talk about how we write about our graphs and complete the findings section. It is both kind of boring and also kind of hard. We will tackle that next week.

But first, the fun stuff! It is time for thinking!

Discussion Section

This is the part of this project that I really enjoy. Previously, we haven’t really done a lot of thinking. We’ve done a lot of stuff, but I wouldn’t call any of it creative or engaging. That’s the labor we have to do to get to the fun stuff. Now that we have some data, now that we’ve made some knowledge, we have to think about what it means. It is time for analysis.

I’ve put together a Google Form to help facilitate some analytic brainstorming. This is a heuristic, a series of questions about our data that can help us better understand it. I’ll ask you to complete this form over the weekend. I will anonymize and share responses on Monday.

I want you to spend about an hour working on this; there’s an extra-credit opportunity that can earn you a full point on your final grade (this has been added to the extra-labor section). I imagine the extra-credit opportunity would take you another hour.

Required Discussion Brain-Storming:

  • Compare our results to Brumberger and Lauer (and, if relevant, put in conversation with Lauer and Brumberger). Pick a code or related cluster of codes. How do our quantitative results compare with B&L’s results. NOTE: what makes this tricky is that B&L used a really weird scale or bubble to present their data. But you can still figure this out. Also, you can use codes here to think about how our data speaks to the notions of “responsive writing” and/or “hybrid editing” that L&B describe in the later 2019 article.
  • Compare our results to some of the results from my previous iterations–what’s different? *Why* might our results be different?
  • Looking through our data, I want to to identify a couple significant codes. First, what is a class from which someone might learn a specific tech/comp/characteristic? Second, what is a non-class way a UNC student might develop a coded proficiency?

Analyzing Data with ChatGPT

This year I tried using ChatGPT to collate the codes, and it worked. Here’s a link to my ChatGPT session.

While I was working to simply measure code frequency, ChatGPT offered me other options for data analysis. It wrote:

It seems you’re providing multiple sets of codes for analysis, likely for a research or classification project. Without access to my more advanced data analysis tools, I can still help with a qualitative summary or patterns from the given list.

To help, I need to know what kind of analysis you would like:

  • Frequency count: How many times each code appears.
  • Grouping: Are there any natural groupings or clusters within the codes?
  • Co-occurrences: Which codes tend to appear together?
  • Patterns: Any patterns over time or order of appearance.

Let me know how you’d like to proceed, and I can assist further!

Two things here:

  • First, I was on the free version of ChatGPT (4.o-mini). That’s what it means when it says it cannot access its advanced data analysis tools. If you don’t use ChatGPT often, then it will auto to the more powerful version of ChatGPT. I don’t know what those advanced options are.
  • Second, we can see that there are other options available

For extra-credit, I would like you to explore those other options. But I would also like you to think about what data to supply ChatGPT.

Here is a link to the raw data sheet, sorted by job type. A simple way to think about how to target more meaningful data is to look for patterns / clusters in a specific job type or, say, two types–writing and editing for instance. What do the numbers look like if you cut out social media jobs? Or what happens to the numbers if we only look at hybrid and social media jobs? What percentage of jobs have which codes?

To get the extra labor points for this, I want you to use ChatGPT to do some analysis. Select parts of our data, feed it in, and ask it to do something other than just frequency. I’ve added a link to a Google Doc in the extra-labor assignment; share the results of your labor there.

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ENG 429 4.T: Prompt Engineering

Today’s Plan:

  • Quick Reflection on Last Class
  • Discuss Graham
  • Prompt Engineering

Quick Reflection on Last Class

At some point in last class, Amber asked if I wished students were more willing to debate me in class. More combative. I cannot recall the exact conversation, only the question. My answer was “yes.”

But I wanted to circle back to that. I think that gut response is true: I do wish education involved more dialectic engagement, more back and forth, more challenge. But I think I want that because it is comfortable for me. It is something that I am pretty good at. It is familiar. It comes easy. So that is my natural, gut, heat of the moment answer.

But I want to revisit that because, while true, I don’t think it is the right answer. Not ethically. There’s a few things for me to unpack here. Most important, though, is that I stress that your generation’s general disinterest in educational debate and conflict isn’t a defect. It is a difference, but not a defect. I do not mean to mourn something cherished that we have lost. I am coping with difference, with having to change, with having to figure out how to welcome you in the process (ethics as hospitality).

Now, do I believe that certain democratic processes benefit from a populace trained in debate? Particularly folks who know how it feels to lose an argument? To develop a particular distance from ideas? Yes, yes, and yes. I’m tripping around a lot of stuff that sounds like old-school-semi-toxic-masculinity. But I am not calling for a complete distance or an instance on argument-as-objective-play. I think there’s a value in experiencing argument-as-a-happening. To feeling it. Encountering difference, especially different ideas about how the world (and the classroom) might work is an experience of cognitive dissonance. It is to feel estranged from the familiar. We need that to function in a diverse society. But arguing with someone isn’t the only way to get that experience(?).

Discuss Graham

Thoughts?

Prompt Engineering

First, I wanted to think about how Mollick structures his prompts:

You are an expert at marketing. When asked to generate slogan ideas you come up with ideas that are different from each other, clever, and interesting. You use clever word play. You try not to repeat themes or ideas. Come up with 20 ideas for marketing slogans for a new mail-order catalogue (106)

Here’s how I opened my ChatGPT session:

I am a writing professor and would like to test your abilities to help me write a 2500 word paper. The topic of the paper will be on chatGPT and racial bias and discrimination. Before we start writing the paper, I would like to know if you are familiar with several different sources I would like to include in the paper.

If you are not familiar with the source, then please let me know and I will be happy to select another one.

After we ran through 3 sources, I wrote:

My paper will have three major sections:

Section One: A Troubling History of Race and Technology
Section Two: Early Problems with Race and GPT
Section Three: What kinds of alignment and regulations might we need to make AI technologies safer for black people

Along the way I’ve used a lot of prompts to either improve particular sentences (this seems generic, be more specific) and to sharpen the quality of writing (this paragraph doesn’t have a strong topic sentence).

I am drawing upon the ILearnNH guidelines from last class, and on Graham’s advice to try and carve up the writing process into smaller units.

I have thoughts on this. [See this: https://prompts.chat/]

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