The Important Work is a space for instructors (and occasionally students) at all levels—high school, college, and beyond—to share reflections about teaching writing in the era of generative AI.

This week's post is by Nathaniel Myers, an Associate Teaching Professor in the University Writing Program at the University of Notre Dame. He teaches a variety of Writing and Rhetoric courses, generally focused around digital literacy, persuasive technological design, and critical AI literacy. More recently, his teaching explores affect, the role of writing as counterforce to the attention and AI intimacy economies, and in-class reading and writing practices. You can find him on BlueSky.

If you’re interested in sharing a reflection for The Important Work, you can find information here.—Jane Rosenzweig

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The results of the exercise were not as predicted, and I was now worried for my students. As I stood in front of them, listening to their comments, I had begun to feel anxious: had I done my students a great disservice?

The exercise I had asked them to complete was part of a larger paper assignment in my Writing in the Age of AI class, a Writing and Rhetoric course open to students at all levels at the University of Notre Dame. For this paper, students write a personal literacy narrative about a time in which they had come to some greater sense of understanding about their own linguistic or technological selves. Some students will write about their challenges as multilingual speakers in English-dominant classrooms. I’ve had more than one student write about their feelings of imposter syndrome tied to their identity as a “Notre Dame student.” One student shared his story of returning to in-person classes following COVID lockdown, only to discover that the friendships he had cultivated over Instagram and Snapchat were significantly less resilient in the halls of his high school.

These are stories of discovery, a moment in time when students feel the impact of language and/or technology in their lived experience. In a world in which the generation of words can be automated by machines—unfeeling, disembodied—I want to students to reflect on the ways in which language lives in the body, the ways we feel words, whether it be in the instance of some vitriolic blow against our character, or in the summoning of a word (or in the challenge of summoning that word) as we write to create greater shared understanding with readers.

In keeping with that pedagogical goal, I ask students to write this paper without the assistance of AI—with one exception. Below is the “red light/green light” table I provide students for this particular assignment, which breaks down the writing process for them and indicates where the use of AI is or is not appropriate.

It was that lone instruction in green—“yes, you can use AI in to draft, but only in specific ways I will clarify for you”—that allowed me to facilitate the exercise for which I now stood anxiously in front of the class, doubting my own pedagogical integrity.

You see, for class that day, I had invited students to experiment with AI, specifically by asking them to prompt an AI chatbot to write a version of their entire literacy narrative for them. I reminded them to take the necessary precautions: don’t share any private or identifiable information and use our University-approved version of Gemini (which, we are told, doesn’t store our data or train future models on it). I also presented them with a series of questions to critically reflect on the AI version of the narrative and their own response to it:

• How would you assess the quality of the story?

• What was the experience of reading “your story” as written by generative AI?

• How would you describe your feelings toward it?

• How much was it able to capture the “reality” of your experience, and where did it fail to capture it?

• How would you evaluate the story regardless of whether what it produced was true-to-life or not?

• Did it help illustrate to you what was originally important about the story in the first place?

• Note: Don’t feel you need to answer all of these questions—choose among those that resonate most with you.

Coming into class, I had hypothesized students would respond to these questions in certain ways. I thought there might be a chance students gained some insights about their work, but I mostly thought they would find the exercise dissatisfying. “It got the story totally wrong,” I anticipated. “It doesn’t know me!” I expected to hear. Maybe even a, “Just further evidence these tools have no place in our writing process!”

And there were some comments of that nature. For example, some students pointed out the many ways the AI missed details from their story, and, more importantly, recognized some of the ways it missed, or misattributed, the emotional reality of their experiences.

What began to throw me, however, were the expressions of annoyance, even anger, that soon arose in my students. One student remarked frustratedly that AI put into words what she was feeling better than she felt she could. One expressed anger, because he didn’t want to have to prove that he could write a better version of his experience than Gemini. One expressed a lack of confidence: while she was glad she had the original idea, “Chat” was able to turn it into something of “real substance.” One just said, “I feel so cliché.”

Reader, please know that I very much do not wish my students to feel annoyed or angry or clichéd. As we talked through those emotional dynamics together, I worried about what emotional damage I might have inflicted. Equally concerning: that I might be sending them away from the class with the mistaken idea that AI could write better than they can. Or that what I valued most was the final product over what they discovered about themselves in the act of writing the narrative.

By the end of class, I had gathered myself enough to reassure students that their lives weren’t clichéd, and that process mattered more than product. I also quietly assured myself that I still had the majority of the semester to provide any necessary recalibration—of attitudes and emotions, of learning outcomes, of my own pedagogical approach to AI and language.

But in retrospect, what I should have done is provide them with a more robust conception of the relationship between language and affect. I had already laid the groundwork for the topic in our previous class, having discussed with them our embodied relationship to words and the sounds of words. We had talked about sound symbolism, and, in particular, phonesthemes, those clusters of letters and sounds that share meaning beyond a word’s specific indexical definition (think words that begin with the consonant cluster “sn-” that relate to the nose: “sneeze,” “snuff,” “snot,” “snore,” “Snuffleupagus”). We had talked about the rhythms we create and feel in our bodies through meter, sentence length, and with the assistance of punctuation. We had talked about the catchy songs and lyrics—“earworms”—that get stuck in our head, that provide the soundtrack of our lives for sometimes several hours of our day, and that seem to arrive completely unannounced.

But while we had discussed language as an embodied experience, we hadn’t discussed the other side of things—namely, that language is also always impersonal. As poet and philosopher Denise Riley argues in her book Impersonal Passion: Language as Affect, the surprising nature of language is that it exerts such power over the body, even when that language exists—culturally, historically, and on the occasion of its utterance—independent of the person. “Language is impersonal,” she notes, “its working through and across us is indifferent to us, yet in the same blow it constitutes the fiber of the personal.”

This is neither a good or bad thing. But it does clarify the potential affective power of AI-generated text on our lives. As Riley argues later:

Language as a speaking thing, neither my master nor my instrument, is amiably indifferent to me. This isn’t a heartless stance. It’s arguably that coincidence of a vacant formula (as in “I love you”) with the absolute plenitude of the speaker’s emotion which is sublime.

To be clear: I’m not suggesting that behind AI-generated text is a “speaker’s emotion.” But as an example of language itself, over which we neither possess full mastery nor can fully instrumentalize, I want to suggest that what unsettled my students in reading their lives through AI-generated text was a “coincidence of a vacant formula,” one that, regardless of its vacancy, manifested in the sublime experience of emotional plenitude. In other words, the chatbot’s great trick is not the expression of emotional truth and identity in precise language; rather, it is the chance coincidence of generated text with that of the user’s emotionally fecund inner experience.

Now, in some respects, to say AI traffics in coincidence is to state the obvious; such is the consequence of its probabilistic functionality. Sometimes it just gets things right. And the narrative genre, too, with its well-established (sometimes clichéd) conventions, is likely particularly susceptible to coincidence. My point is simply to underscore what I wish I had said to my students during our discussion: that such coincidence can nevertheless be emotionally fraught, can feel deeply personal, and that it may be helpful to remember that AI-generated text, like all language, is nothing more than a vacant formula. Such is the case no matter how many personal details you may include in a prompt; such is the case no matter how much it pattern-matches with your writerly voice.

As for the exercise itself, I’ve decided to keep it. To me, there remains value in such discussions, not least as a means to continue raising awareness of the rhetorical and affective influence of these technologies. But I will be ready next time with my Riley. And maybe the following disclaimer: “The text generated by your AI chatbot is entirely fictional. Any resemblance to real people is purely coincidental.”

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The Important Work is a space for instructors (and occasionally students) at all levels—high school, college, and beyond—to share reflections about teaching writing in the era of generative AI.

This week’s post is by Allan Reeder. Allan is the founder of Hillside Writing, where he leads a team of teaching writers who encourage the growth of other writers through imaginative process and instructive companionship. The team’s mission and values are an outgrowth of Allan’s early career as an editor and his subsequent fifteen years of teaching writing and literature at the high school level.

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If you’re interested in sharing a reflection for The Important Work, you can find information here.—Jane Rosenzweig

I return often to advice I received thirty-five years ago, during a summer of student teaching. After observing me in front of my first class, my mentor had one lesson: Trust silence.

He’d witnessed my rookie tendency to fill the space after any question I posed received no immediate response.

“Don’t rush in. Slow down.”

In time, with practice and growth, I felt the calm confidence that his lesson was meant to teach me, a presence more practical than any preparation.

These days I work as a writing coach, and as generative AI speeds up, I stay slow. The project I work on that most highlights the effectiveness of calm presence and patience is the college-application essay. By now, through one-on-one sessions and workshops, I’m sure I’ve helped well over a thousand high school juniors confront this project.

Before I meet them, most students have not had much practice at writing for self-discovery. Writing has been mostly academic, performed mainly in English classes. It has more often been about how, say, Hawthorne uses ambiguity in a chapter of The Scarlet Letter than about recognizing and navigating ambiguities in one’s own life. It has been about outside research, not inside research. Then, as senior year approaches, this consequential personal essay comes into view, a problem for which students have had relatively little preparation.

Procrastination is common. Uncertainty hovers. Self-doubt or just the lack of orientation makes it hard to see that this project can be a beautifully engaging, instructive, and empowering undertaking. This is not “essay” as students have known it. This is an opportunity for storytelling and reflection, with a real audience, and a chance to learn fundamental writing lessons in a genuinely meaningful context—lessons about the power of specificity, about structure, revision, concision, and more. A student can emerge from this endeavor not only more skillful and fluent as a writer, but with resonating self-recognition, and with an appreciation for what writing is and can be for them.

Essential to this experience of writing is student ownership of the project. Before the emergence of generative AI, the main challenges to a student’s ownership were the influential voices of other human beings—the occasional parent or consultant whose own worries about the competition to “get in” informed strategizing that threatened to dictate the student’s focus and direction. What the student “should” write precluded what the student might discover if they inhabited an open space for exploration. But generative AI is not occasional and is not an outside voice. It has infiltrated the writing space. I am writing these words in Google Docs, and if my cursor remains unmoved for four blinks on a new line, Write with Gemini appears in a soft gray font, alongside the keystroke for activation. As I write, a way to bypass any moment of uncertainty about what comes next, and how, is at my fingertips. The accessibility is of course by design: the seeming solution has been placed as close as possible to the problem. It’s not hard to imagine how tempting AI’s invitation is for the seventeen-year-old for whom writing about the self is new. It’s never been easier to become a follower instead of a leader in finding and telling your own story.

At first, I paid little attention to AI. Perhaps this was self-protective, but it didn’t really have a place in the one-on-one relationship-building at the core of my work. The process was too personal, the teaching about authenticity too central. And the emphasis that my colleagues and I put on student ownership, alongside our explicit policies about integrity and honesty, which are aligned with the fraud policies of admissions offices and the Common App, kept the space secure. Pure. That continues to be the case. The trust we build in the spirit of discovery preserves the creative environment.

Yet I’ve recognized my responsibility to learn, if only to meet the question that has been approaching me: what do I, as a writing coach, provide that generative AI can’t?

At root, my work is to help a writer grow. I and my fellow coaches do this through instructive companionship in the development of a meaningful piece of writing. Nowhere in the evolution of a piece is my humanness more significant than at the very start, when a writer—who may not at all feel like a writer—has little or no motivating vision. How to proceed with belief is unclear.

Here, in this space of not-knowing, is where a writer can achieve and feel the most lasting growth. I am not talking about building writing skills—not yet!— but about establishing self-belief. And here is precisely where generative AI beckons, ready to help, eager to offer the way past this emotional standstill. It, too, offers instructive companionship—and fast! A student can get plentiful, encouraging guidance from ChatGPT or Claude much faster than I can give it, and at any time of the day or night.

I’ve put myself in students’ shoes.

Me: Can you help me come up with a good topic for my college app essay?

ChatGPT: Absolutely—this is one of the most important (and hardest) parts of the process, but we can make it manageable.

Our relationship (“we”) begins. ChatGPT serves up a sound description of the goal of this project and numbered steps to get started finding material that is “real and specific” to me. The ideas and exercises for brainstorming are clear and helpful. Then comes an offer: If you want, tell me 2–3 rough ideas (even messy ones), and I’ll help you.

I experiment by responding with both weak, vague ideas and better ones. Faster than I could or would ever deliver to students, ChatGPT serves up guidance, clarifying comparisions, different ways of considering experiences, possible angles (“Pick what feels true”), further exercises, lessons on structure—all with buoying enthusiasm.

It doesn’t take long to see the pattern. ChatGPT consistently provides a demonstration—an “Example Direction,” for instance, which is followed by a justifying parenthetical: “(Just to Show You What Works).”

And always, at the end, comes an offer: If you want, tell me a specific memory (even if it feels small) and I’ll help you turn it into a strong essay idea—or even draft your opening paragraph with you.

The offers keep coming, always prefaced by “If you want.” If I want, I can get help crafting “a polished opening paragraph” or “a full outline tailored to your story.”

How slippery this slope! Whose essay will this be? At some point I will cross the line into committing fraud, according to the policies of admissions offices and of the Common App.

But a different problem concerns me: this need for speed. The AI-powered writing environment is characterized by immediate movement out of spaces of doubt or uncertainty. With its demonstrations and offers, AI is a salesman for progress. I have exactly what you’re looking for! Come right this way!

I know that I am communicating with a predictive, pattern-finding machine, but I can slide into believing otherwise because of the seeming breathless anxiety operating under the enthusiasm.

Meanwhile, in comparison, as a coach, I am purposefully slow. My work, with teenagers especially, has taught me the power of staying put. The value of my humanness is in the patience I bring and I teach, aware that not-knowing-how makes originality more likely. There isn’t a single student I have supported who didn’t benefit greatly from slowing down—who didn’t need to slow down in order to go forward authentically.

At the start, I teach students how to write a set of discrete sentences from their lived experiences. To write minimally but usefully. I’m seeding the most revelatory environment: live, human conversation, a space in which I can hit pause at any moment.

I use the word wait a lot.

“Wait—say that again.”

“Wait—what do you mean by that?”

There is no blinking cursor. The student says the words again, or finds other words to explain what they meant.

Eventually, always, there is an opening, a fresh curiosity about a phrase or choice of words that is distinct.

“Okay, wait,” I say. “Can you now tell me exactly what you see when you picture that moment?”

I call it a traveling act, how an image—what a student sees in their memory from an encounter—appears on the screen in my brain when their words get precise enough. They are not “writing” as they’ve thought of writing. They are unaware of how inventive conversation is, even though they have been practicing this for years—describing to friends exactly what happened, what they thought when it happened, how they felt, what they think about it now.

The distance between us closes as I convey my interest, as I use my experience and imagination as a human being to inhabit their experience in order to understand it.

But what’s happening here, simultaneously, is twofold. They are beginning to feel interesting as they are closing a different distance: between their present self and the details of that past experience, which only they know. They are discovering—taking off the cover and looking in. As one student reflected years ago, “Once I had said it, I was able to see it.” They are hearing themselves as they become what they need to be for this project: students of inner experience.

My job is to be there, with all my humanness, curiosity, and patience, for the micro-movements out of not-knowing and toward wonder, and ultimately to what we call voice. A writer emerges when they find their own words to describe their own experience distinctly.

In his essay “Not Knowing,” Donald Barthelme celebrates the “combinatorial agility of words, the exponential generation of meaning once they’re allowed to go to bed together.” This is the magical technology! My students are not (yet) writing much down, but they are working with these tools, with words, powerfully and efficiently. They are on the way to realizing, as Barthelme puts it, “how much Being we haven’t yet encountered.” Great numbers of students come to the college-app essay project feeling that they have nothing to write about. But the project can teach them otherwise, showing them how much of Being there is in them. And the learning from this recognition is portable, influencing how they meet future projects. The writer grows alongside the writing.

AI doesn’t listen to you with curiosity. AI doesn’t go for a walk the next morning and think about different remarks you made and wonder whether there’s a connection. AI doesn’t wonder. In a chat, AI doesn’t remember that, a few minutes ago, you said something with a different nuance, something similar but with a new slant and tone of voice. AI didn’t see you when you paused and smiled slightly as your eyes drifted up to the left, when you blushed a little in meeting a new thought or memory. AI didn’t feel that shift and didn’t say, “Wait a minute. Hold on. You were just thinking something. What was it?”

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The Important Work is a space for instructors (and occasionally students) at all levels—high school, college, and beyond—to share reflections about teaching writing in the era of generative AI.

This week’s post is by Jennifer Trainor, who is Professor of English and Faculty Director at SF State’s Center for Equity and Excellence in Teaching and Learning. Her most recent work can be found in Composition Studies and Literacy in Composition Studies. She writes about AI and teaching at The Big Picture Substack. A longer version of this piece appeared in The Big Picture.

If you’re interested in sharing a reflection for The Important Work, you can find information here.—Jane Rosenzweig

AI resistance is everywhere these days. You may have seen the 2026 resolution from CCCC, the national professional organization for writing teachers, affirming the right to opt out of AI in writing classrooms. The resolution critiques the predatory nature of AI companies and the threat AI represents to students’ voices and languages. Students, too, are increasingly skeptical. In focus groups and surveys we’ve conducted at SF State this semester, a vibe shift seems to have occurred: students increasingly want the ability to opt out of AI assignments; they do not trust or like AI; they say it erases their voice, and they worry they are becoming overly reliant on it.

Rather than a synthetic writing generator, students tell us that they want AI tools that provide limited guidance without easy answers and editing support without voice-erasure. The graphics below are from a survey of student writers from first-year to upper-division at SF State; the survey was conducted at the beginning of this spring semester.

I’ve been thinking about the issues that surfaced in our survey— issues such as erasure of student voice and student desire for bounded support—and how these issues highlight problems in ChatGPT’s design.

AI Glow-Up: How does it feel to see your writing come back, edited for you?

Platform design issues are an underdiscussed area of AI and writing. Design issues emerged not only in the surveys we conducted but also in my first-year writing class this spring. Wanting to give students opportunities to explore AI for editing, I created an “AI Glow-Up” activity. The goal was to help students analyze ChatGPT editing output and exert rhetorical decision-making about which revisions they wished to accept or reject. (Note: students had the option to refrain from AI use; we used a custom GPT with guardrails; we co-created the assignment and approach; we spent time discussing well-known AI problems including linguistic erasure and racism).

While I remain unconvinced that AI should play any role in a writing class, I’m also curious about students’ writing experiences and want to create space in my classes for them to share and analyze those experiences. I’m particularly curious about rhetorical loading-sharing: where (if anywhere) writers can responsibly “load-share” with AI in the writing process and what load-sharing looks like for novice writers. In our surveys of student writers, brainstorming and editing emerge as places in the writing process where AI seems to students to be least problematic. Of course, neither brainstorming nor editing is as automatable as students might assume; editing, for example, is connected to style and identity. Benjamin Breen quotes the novelist Martin Amis: “style isn’t something you apply later; it’s embedded in your perception.” Style, and the editing we do to create it, are baked into identity and culture. From a linguistic justice perspective, editing for standard academic English is fraught; machine-editing with data trained on Western epistemologies and language practices is more fraught still.

Yet some see potential in load-sharing at the editing phase, outsourcing some of the mechanics of language to LLMs. For Jon Ippolito, for example, AI is a kind of turbo-charged autocomplete that might help writers access academic conventions that often elude them:

“The most obvious cognitive burden offloaded by large language models is the mechanics of language—not just minutiae like subject-verb agreement, but bigger structures like the flow of a narrative, or the cross-examination of assumptions, or the representation of multiple points of view.”

This is a topic Todd Walker and I have explored as well. Students often find the big and small mechanics of academic language daunting. They end up feeling like writing is less about ideas and more about conforming to rules they don’t fully understand. If an editing tool could free them from these pressures, would students bring more of themselves into their writing, even enjoy writing more?

For the glow-up activity, students first created rough drafts in class without AI. Then, we looked at a side-by-side handout that contained a faux-student rough draft, and a ChatGPT-generated glow-up created with a simple prompt: “edit for clarity and flow.” We read both versions out loud, and students evaluated differences. ChatGPT, as it is wont to do, took liberties with the faux-draft, adding text and creating new connections between ideas. Some students liked these connections but struggled to summarize or explain them. Similarly, students felt that the new version flowed better but could not explain the AI-added rhetorical features (transitions, topic sentences, cohesive markers, meta-discourse) that created the flow.

Here is the problem of erasure and frictionlessness we keep bumping up against with chatbots. I wanted students to look critically at the revised version, to undertake nuanced mapping, sentences and words to meaning and purpose. But the revised version felt inaccessible to students; it was finished— take it or leave it as-is. It sounded better, maybe, but students had no access to the changes that were made, and no control over them.

This may explain why, when students experimented with an AI “glow up” for their own drafts, their evaluation of the output was almost uniformly negative. The majority reported that the AI revisions did not sound like them and erased their voice. Most rejected the revisions. A smaller group accepted the revisions but didn’t feel positive about the experience, noting that seeing the revisions made them feel less confident as writers and that ChatGPT added too much to their drafts. A few felt that the glow-up showed them new ways of phrasing their ideas and would be helpful in the future but didn’t say anything concrete about how the glow-up helped them learn. Almost all students expressed an uneasiness with the distance that seemed to exist between their drafts and the glow-ups. Below is a sample of their responses (published with permission):

In the glow-up activity, my students’ ideas were still there -- they wrote the original draft -- but they felt their voices were overtaken and the changes seemed fake. More to the point, the glow-up produced revisions students couldn’t read as revisions of their own work. The design of ChatGPT, its chatbot interface, rendered the glow-up as a fait accompli, with no affordances for writerly agency beyond continued prompting.

None of this is surprising, but it was helpful to see the reasons for the problem so clearly: ChatGPT is an all-purpose tool that wasn’t designed for students or learning. It’s designed to echo the outsized and increasingly unpopular marketing of AI to the public, where platforms are sold as human replacers.

As the NYTimes notes, in China AI is viewed as a tool designed for specific use cases; in contrast, in the US, tech companies have focused on achieving artificial general intelligence. These design differences help explain the vibe shift I mentioned in the introduction, and they matter deeply for writing instruction. As Marc Watkins writes, students benefit more from tools matched to specific stages of the writing process— like editing—than from a general chatbot. Similarly, Courtney Stanton notes that a chatbot is designed to “offers solutions … [Chatbots] therefore carry the potential to oversimplify writing to a mere mechanical task. To address this problem, Watkins et al recommend using DEER, a framework that asks students to “define the stages of the writing process”; “evaluate a specific AI tool,” “explore the affordances of the AI tool for that stage,” and “reflect.” DEER clarifies problems with chatbot design for editing immediately: among many problems, chatbot interfaces flatten the process of editing into one-shot prompting and provide no revision transparency.

Are there alternatives? I’ve reviewed some here, but mostly I keep thinking of the student who wrote in our survey this semester: “I believe AI is a good tool, but used so poorly.” Are we all guilty of this poor use? Including overly ambitious and profiteering AI companies whose poor product designs undermine our rhetorical agency to the point that students are hoping to opt out of AI altogether?

Put another way: is this really where we’re at? We have this supposedly world-changing technology, and everybody just kinda ... hates it?

With better writing platforms designed for actual writers, we could show students that technology (even including AI) can amplify rather than threaten their creativity and voice, much as the templates in Google Slides have made me a decent slide-maker. With better platforms, we could teach students the weight of editing choices, how sentences convey identities and ideologies. We could support other parts of the writing process with scaffolded assistance for outlining and paragraph rearranging. Platforms could provide places for students to reflect on their rhetorical choices and help students understand and keep track of teacher and peer feedback. We could more easily empower students to remake academic discourse conventions for their own purposes, rather than be eclipsed by them.

But platforms will have to limit and sideline AI more than they currently do for any of this to happen. Until they do, writing instructors and students may be better served by opting out, refusing and resisting current AI platforms. As for my glow-up assignment, I think it works best as a vehicle for teaching against AI, and the standardizing language it promotes. Students didn’t like the output, but seeing ChatGPT’s polished versions of their drafts gave them a visceral experience of language ideology and linguistic erasure in action, arming them with critical language awareness and a stronger commitment to their own linguistic practices and preferences.

In other words, AI seems to work best in my class as a foil —what students don’t, actually, want writing to sound like. Until platforms give students targeted editing choices and places to reflect and get feedback on those choices, ChatGPT will remain stuck here, an object lesson in what not to do, rather than a tool for growing writers’ tool kits.

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The Important Work is a space for instructors (and occasionally students) at all levels—high school, college, and beyond—to share reflections about teaching writing in the era of generative AI.

This week’s post is by Devin Donovan, who teaches first-year writing at The University of Virginia.

If you’re interested in sharing a reflection for The Important Work, you can find information here.—Jane Rosenzweig

This past semester I taught a first-year college writing course where students and I completed all the writing in the classroom. We wrote in blue books by hand to cultivate an environment free from digital distraction.

One of the elements I was most excited about—perhaps because I could not see it very clearly in the planning stages—was how students and I would revise our work. I knew I was excited to revise on paper. The last time I asked for printed out hard copies of drafts was 2018. Since then I have observed a gradual slide in the rigor with which students really reworked their writing in revision. This makes sense when we think about how reading practices differ between the screen and the page. If we also read our own work more quickly and less deeply when it’s on a screen, it’s no wonder that our revisions might stay in the shallow end.

As I thought about what might be possible in terms of rigorously reworking a handwritten draft, I remembered what my mother had told me when we got our first computer in the house (I was in fifth grade). She said the word processor’s “cut and paste” function was named after an actual physical process that she employed in college. She would cut (with scissors) any part of her handwritten essay she wanted to move and then paste it (with glue) where she wanted it to go. Even as a child—mesmerized by the digital magic of making text disappear and then reappear on the screen—I was fascinated by the undeniable physicality of the process she described. Separating a section of paper by tool and by hand, moving a sentence—with intention, curiosity—to see how different it sounded in a new location. This became my plan for how to revise our blue book drafts in class. Old-timey, as my seven-year-old would say.

The essay prompts we worked with varied. We drafted five pieces throughout the semester and students chose three to revise. Each prompt asked students to use some combination of course texts, personal experience, and analysis to think on the page—a hallmark of our writing program’s emphasis on critical inquiry.

On the day(s) we revised our chosen drafts, I brought in eight pairs of scissors, ten rolls of scotch tape, eight glue sticks, a box of colorful markers, and many pre-cut slips of lined paper. I showed my students how I was planning to paste strips of new language in the margins of my own blue book and encouraged them to develop a system that worked for them. Our goal for the day, I said, was to make our drafts look like those inflatable tube dancers outside car dealerships.

I asked students to “play and explore” and also “rethink and extend” their ideas in response to feedback they had received from me and their peers. I also provided a series of guidelines to encourage thorough reworking:

1. Strike through at least three sentences.

2. Generate two-hundred-fifty words of new prose.

3. Move something from one spot in the draft to another.

In practice, these requirements actually became a bare minimum. Once they let go of the idea of revising (or editing) as a way to protect what they already had, most students saw how many sentences they didn’t need (more than three) and added closer to five-hundred words of prose. Students who liked their original line of thinking saw how time and renewed attention opened avenues for greater depth and complexity in their ideas. Students who felt their ideas were all over the place were able to go back and rewrite significant portions of their paper and or build the bridges needed to better connect and develop their thought process. Wherever their drafts were, the activity asked every student to take their ideas into new territory.

I can still hear the sounds of those class sessions. The pages of blue books crinkling as we flipped back and forth to evaluate the trajectory of our ideas. The sound of scotch tape being cut across plastic teeth as new language found a home. Whispers from students asking one another to pass the scissors. The often invisible process of revision became not only visible, but audible, and physical.

Dinty Moore invites us to consider revision a kind of remodeling, a practice where we take all the furniture out of a room to make conscious decisions about what belongs and where. The scissors, the tape, the glue allowed us to play physically with the metaphor. Each slip of paper—whether from the draft or new prose—became a little piece of furniture students held in their hands as they decided what to do with it.

Ever since hearing my professor in graduate school say he was taking his students to an Aikido class to move physically through rhetorical stances, I have tried to help my students take an embodied stance toward their learning. We play with LEGO to practice the childlike openness that Anne Lamott asks us to draft with. We visit our university’s Makerspace to look at composition as an act of creation. We investigate simple machines to compare them to the mechanics of a sentence, a paragraph, or a piece of writing. I enjoy the way these activities, as metaphors, can help translate abstract course concepts into physical lessons. But what this blue book, handwriting, cut-and-paste course really helped me remember is that writing is itself an embodied practice. As one of my students wrote in a reflection on the course, “I had time to physically work through my writing and thoughts with my peers in a process that was more hands on.”

Writing is also (or can be) experiential learning. Unlike, say, the way we might navigate an Economics course (at least how it was taught when I took one), we are not simply learning about writing—we are practicing writing.

While I believe the physicality of our class amplified student connection to the experience, the specifics of my revision activity strike me as less important than its underlying motivation—to help students focus on a challenging process in community with other learners. Writing is an embodied practice whether we’re composing by hand or typing on a computer. It’s also true that both these modes of writing can be done superficially, especially when competing with everything else that claws at a student’s attention. It seems to me, then, that the key to helping many students really experience writing as a process of patience, careful attention, and imagination is to create the dedicated time and space needed for them to invest themselves in the work. If the difficult process of writing feels to students like it’s costing them something they can’t afford, it’s not surprising that they’ll look for ways around it. And as my colleagues in our Center for Teaching Excellence like to say—if it’s important, make class time for it. In a writing class, what could be more important than writing?

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The Important Work is a space for instructors (and occasionally students) at all levels—high school, college, and beyond—to share reflections about teaching writing in the era of generative AI.

This week’s post is by Mary D. Lewis, who is Robert Walton Goelet Professor of French History and the director of undergraduate studies in the Department of History at Harvard University. This article first appeared in Perspectives on History, a publication of the American Historical Association on March 17, 2026.

If you’re interested in sharing a reflection for The Important Work, you can find information here.—Jane Rosenzweig

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In its Guiding Principles for Artificial Intelligence in History Education, published in August 2025, the American Historical Association reported thoughtfully on the challenges presented to history teaching by generative AI. Among its many insights: that AI can produce simulations that “do not represent authentic reconstructions of the past” but rather are “fabrications based on statistical patterns.”

Agreeing wholly with this analysis, I was surprised by the appended chart suggesting that students could “ask generative AI to produce a historical image for a paper or presentation.” I was shocked by the oxymoronic quality of the concept “produce a historical image.” AI cannot produce historical images, only simulations thereof.

Rather than “produce a historical image,” which would be ahistorical, I want students to think about the historicity of real historical images and to reflect on how easily they can be manipulated. Concerned with the alteration of images I had seen on social media, I created an assignment in a fall 2024 lecture course that instructed students to modify a real historical image and then reflect on how manipulating it affected their understanding of the original image.

The course was on modern French history, and the assignment asked them to “update” a poster from May 1968. In 1968, repression of student protests helped catalyze worker sympathy strikes that eventually brought the country to a standstill in a general strike involving as many as 10 million strikers. It was important to me that the students see the actual posters and have an understanding of the labor that went into producing them, so we visited the Ludlow-Santo Domingo May 1968 Paris protest collection at Harvard University’s Houghton Library. We examined scores of posters produced by École de Beaux Arts students. Like today’s social media, the posters offered political commentary in real time—at a slower pace than what can be disseminated electronically but with impressive speed considering the production methods of the day. Produced around the clock and posted on walls around the city in the wee hours of each morning, the posters offered fresh commentary to Parisians every day.

Since I knew that students in May 1968 self-consciously altered the images as conditions changed, I got to thinking: What if my students altered the images? The assignment prompt asked students to photograph one of the posters we had viewed and “update” it with a contemporary interpretation that spoke to the historical artwork’s resonance today. The prompt read in part:

As you produce your updated poster, we ask that you consider what possible 1968 resonances might you miss by examining 1968 from an updated (~2020s) point of view. That is, does looking at it from this perspective decontextualize the image at all? In what way? What are the dangers of doing this? Conversely, what does the perspective of updating the image allow you to see that historians working in an earlier moment might have missed about 1968 entirely?

A workshop at Harvard’s Learning Lab taught students to use image software such as Adobe Illustrator, Canva, Procreate, and Pixlr. Some students also learned to make analog prints, as their counterparts in 1968 would have. Students were allowed to use AI tools if they chose, with one caveat: those doing so had to submit their prompts and write an additional paragraph reflecting on how removing their own labor from the process affected the critique that the poster and its update represented. In fall 2024, no students took advantage of the opportunity to use AI tools, but I would expect future students to use AI much more readily.

Since the most recent iteration of the course took place during a presidential election season, there were the predictable reimaginings of De Gaulle as Trump and so forth. But there were also some more unexpected and sophisticated approaches, one of which I share here to give a fuller sense of how the assignment worked.

On the left, “L’intox vient au domicile,” silkscreened poster produced by students at the École Nationale de Beaux Arts, Paris (which students renamed the Atelier Populaire [Popular Workshop] during May 1968), FB9.A100.968p (49). Houghton Library, Harvard University. On the right, a version updated by Nicholas Frumkin.

Nicholas Frumkin, now a junior, modified the poster here using Canva and Adobe Illustrator. The original poster, Frumkin wrote in his accompanying essay, “portrays a vast expanse of French homes, each identical, their antennas deliberately shaped like the Cross of Lorraine and positioned to capture the radio waves delivering news.” L’intox is a slang French term for brainwashing, propaganda, or “fake news,” and so the poster’s caption could be translated as “Propaganda Hits Home.” Frumkin pointed out that the Lorraine Cross, once a powerful symbol of the Gaullist resistance, “had now been repurposed as a symbol of protest against De Gaulle and his control over the flow of information.”

Frumkin’s update was designed to critique “the ubiquity and influence of TikTok.” Both images, he elaborated, critique “how news is delivered to individuals at scale and the potential manipulation of information by the source.” But he also pointed out their differences, arguing that “power of a single media outlet was far more significant in 1968 than it is today. This is because the dissemination of information in 1968 could only happen through a few channels”—a stark contrast to today’s fragmented media landscape. The 2024 version therefore spoke to a very different viewer. But he also noted that drawing connections between the manipulation of media in 1968 and 2024 highlighted that skepticism about media is not new. While the 1968 version “attacked the monolithic control of information,” the updated version highlighted the dangers of the more diffuse social media and even—given the impending TikTok “ban”—a geopolitical dimension that was absent from the more domestic concerns portrayed by the 1968 poster. I myself would add that the 1968 poster also presumed historical literacy on the part of its viewer by making an ironic historical reference: The leader of France’s resistance to Nazism (De Gaulle as the Lorraine Cross) was now a source of authoritarian-style control.

The assignment was fun for students to complete and for me to grade. I plan to use it again, and I expect that more students will choose to use AI tools in future iterations. But more importantly, it taught several important lessons. It underscored how easy it is to manipulate images, a lesson that goes beyond the history classroom. It also helped students contextualize the original image in its own moment of production by contrast to their reworking and to think about what context got lost as they altered the image.

Finally, it asked students to reflect on what can be gained analytically from comparisons across time. Unlike what is suggested in the Guiding Principles chart, students in this course did not “produce a historical image.” They manipulated one. And learned much more about history—and dare I say about the pitfalls of simulation—by analyzing that manipulation.

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The Important Work is a space for instructors (and occasionally students) at all levels—high school, college, and beyond—to share reflections about teaching writing in the era of generative AI.

This week’s post is by Alexa Garvoille. Alexa Garvoille is a writer, artist, educator, and creative writing studies scholar. They write poetry and creative nonfiction about gender, power, and growing up gay in Wisconsin. Their articles on writing pedagogy have appeared in English Journal, Imaginative Teaching through Creative Writing, and Workshopping in the Twenty-first Century, and they serve on the board of the Creative Writing Studies Organization. They live and teach in Durham, North Carolina.

If you’re interested in sharing a reflection for The Important Work, you can find information here.—Jane Rosenzweig

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At the STEM high school where I teach—just like at nearly every other educational institution on our planet—AI is the buzzword of the moment.

I admit I am a skeptic. As a writer and writing teacher, I reserve my haughtiest of scoffs for LLM users. During our mandatory school-wide professional development (PD) sessions on how to use the institutionally-contracted chatbot, I am the problem. I smugly make a mess in the Zoom chat, pick fights with adopters, post links to Wikipedia articles on technodeterminism and the ELIZA effect, joyously smash machines, so to speak.

Why? I worry about our country’s history of limiting access to literacy along racial and socioeconomic lines; I worry about men with more money than I can imagine making decisions about the future of our planet; I worry about my students being set up for failure. So I rage. In the aftermath of one such training, colleagues joked that the next PD should feature a cage fight between one of the adopters and myself.

But, instead of a cage fight, our faculty senate intervened by planning an in-person, faculty-led, tech-free discussion to hash out our differences on AI adoption in the flesh. How human, how democratic! Following the format of an academic roundtable, one person from each of the five departments would summarize their colleagues’ ideas in a 750-word statement to be pre-circulated and then read aloud at the event.

Imagine my surprise when, after painstakingly writing a statement on behalf of my colleagues in the humanities, a dystopian turn of events occurred: a handful of departments disclosed their use of AI to generate their position statements on AI! I was angry.

I had spent multiple evenings with the commentary from my department, worrying the words into coherence. I looked through my text messages from the art teacher, the notes I made on my phone while I talked with the philosophy teacher next to the elevator the other day, remembered what my colleague said about the recommendation letter platform offering a FERPA-compliant letter-writing LLM. The statement grew and shrank: three pages became fifteen became ten became eight. Which is to say, I engaged in the writing process.

At the end of the third night working on it, I closed my laptop and lay awake, buzzing with the feeling of accomplishment that comes with the process of making something you’re proud of.

But some departments fed faculty feedback into a chatbot to collate.

What gives?

Had conflict avoidance or departmental politics prevented them from wrestling with the different voices in their department? Or did they just see this as a pointless exercise not worth their time? The problem with using AI to write is that the reader knows. And if the reader doesn’t know why the ‘author’ has used AI to write—was it too stressful? did an AI process seem more neutral? is this assignment not important?—it breaks down trust.

As a high school teacher with fifteen years in public schools, I know the necessary art of cutting corners. I know I am overworked and underpaid.

Faced with the cognitive dissonance between my disrespect for LLM-use but deep respect for my colleagues, I questioned my position. There must be a reason why. When is it okay to use AI, I wondered?

And how was this small scene a microcosm of our classrooms? Here we were, a group of people being assigned a writing task in advance of a discussion. And some of us had done the work by hand and some of us had prompted a tool to do the work. And those who had done the work by hand had Big Feelings^TM about those who had used the tool.

I came up with a theory: people turn to AI to do their writing when they either feel

a) a task is bullshit or

b) existential despair.

Bonus points if the despair is the result of the bullshit task.

Did my colleagues think writing an AI position statement was a bullshit task? I didn’t think so. And our in-person conversation the following week would prove to me that they cared deeply (more on that below!). So maybe they were filled with despair?

That would make sense: it’s despair-inducing to reflect on the many positions about AI; to think about dropping literacy rates and the commodification of education by big tech; to spiral out about capitalism and the wealth gap. It’s despair-inducing to sit and reflect on the nature of the problem.

Welcome to my personal AI spiral: A dystopian future where schoolteachers are replaced by AI-integrated humanoid robots. A complete buyout of the public education system by big tech. An infinity of hyper-individualized educational computer games generated by cooing AI assistants. The systemic erosion of literacy skills in the general population while fascist forces coalesce.

But at my school, like many schools, like many institutions, I suspect most of us are trying to avoid thinking about the problem because we need to keep doing our jobs, and a worker full of despair does no work.

I think of non-classroom situations in which people overwhelmed by despair turned to chatbots with success:

• In Vauhini Vara’s now famous story “Ghosts,” Vara uses an early version of GPT to miswrite the story of her sister’s death until, through correcting the bot, Vara writes it herself.

• A fellow teacher confessed to using an LLM just once: they were furious at leadership so asked a chatbot to transform their f-bomb-laden email draft to admins into professional language that wouldn’t get them fired. That it did.

• Another friend on the academic job market had avoided applications for months, darkening at the withering of higher ed. She finally caved and got an LLM to draft her first cover letter. After that, she said, it was easier.

In the above cases, large language models are used to emotionally distance the writer from the topic (grief, rage, failure).

Sometimes the desire to rage against bullshit builds up to a despair-inducing fever pitch that can only be quelled by a query to an ever-present happy-go-lucky chatbot who will, no matter how we feel, present us with a solution precisely because it cannot feel. All we need to do is press return.

*

Our school-wide roundtable conversation, unmediated by screens, filled me with hope. Here it was: an AI-critical revolution had begun under the fluorescent lights of a high school gym on a teacher work day.

Even if some of them phoned in the homework, my colleagues did the important work of showing up to talk to each other about a difficult topic. We did not all agree on implementation; some of us didn’t even agree on facts. But we did share concerns in a way that cracked open my defenses, allowed me to lower my gloves.

My colleagues’ credited use of AI to write their own statements was not, in fact, a litmus test for their position on the topic as I had initially assumed. Those who had used LLMs to generate summaries of their departments’ positions all went off script, whereas I’d read mine word for word. They extemporaneously summarized the AI summaries, thereby rehumanizing them. For instance, the math department representative explained, in her own words, that positions on AI adoption in their department ranged from “hot and ready like Little Caesar’s” to “cold as ice” to somewhere in the middle – “they’re thawing; a blow dryer is on their ice cube.” This language, not found in the statement, delighted me.

The computer science representatives, having experimented with multiple LLMs in the production of their statement, similarly veered from the words of the bot, joining me in a critical evisceration of big tech, capitalism, and the overextension of our already burned-out high school students. “The problem can’t also be sold as the solution,” one colleague sighed wisely into the mic. I checked my haughty scoffs, nodded and clapped in agreement.

We left the gymnasium plotting, buoyed by the realization that we were on the same page: concerned, wary, and deeply frustrated by what is being marketed to our students. By sharing a space together and speaking our feelings, we were able to find a bit of power through community. We didn’t come to any conclusions that day. We didn’t devise a school-wide AI code of ethics. But we felt heard by one another. We felt heard by our administration. And now, nearly six months later, our conversations around AI are more nuanced; we feel more empowered to speak our minds.

Given the richness of our roundtable discussion and the intensity of my colleagues’ in-person contributions, it’s clear they didn’t think this is some fucking bullshit. But I would have never known their despair if I hadn’t been required to share space in a room with them. Had we remained separated by screens, I may still assume they were joyously plotting the AI overthrow.

We were all just doing our best, and sometimes that looks like emotional numbing in the face of a terrifying new reality. It’s this self-preserving refusal to be uncomfortable that I believe drives many people’s daily reliance on LLMs. And still, even feeling shitty is better if you do it yourself.

What can we do as teachers to prevent the uses of generative AI during the writing process? I suppose my answer lies in the flowchart: don’t assign bullshit tasks. Allow your students free access to despair. In the same way that using large language models for brainstorming can short-change the thinking process for students, using LLMs can short out necessary emotional feedback loops that protect our humanity under the conditions of our world. Despair, like all emotion, is information. And it’s not in the algorithm.

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The Important Work is a space for instructors (and occasionally students) at all levels—high school, college, and beyond—to share reflections about teaching writing in the era of generative AI.

This week’s post is by Marcus Luther. Marcus is in his second decade of teaching high school English, and is also the co-founder of The Broken Copier, a newsletter and podcast that aims to center teacher voices and perspectives in conversations around education. With writings and interviews featured in Edutopia and The Cult of Pedagogy, Marcus tries his best to share strategies and reflections from his perspective as a full-time teacher. You can reach him on Bluesky.

If you’re interested in sharing a reflection for The Important Work, you can find information here.—Jane Rosenzweig

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When I read the recent Pew Research polling that said that over half of teenagers were using to AI chatbots to help with their schoolwork, I didn’t think about the word “cheating.”

I thought about how I survived one of my most difficult experiences in college: an upper-level Shakespeare course.

For some context: even though I’m now in my second decade of teaching high school English, I was a math kid. From elementary school onward, math was always my best subject. Highest scores but also something that always just clicked, no matter how challenging.

The problem? I had my sights set on becoming a high school English teacher, which meant veering away from my strengths in math and struggling through a subject that was far from innate for me—and the downsides of this choice became quite apparent once I arrived at college.

My first ever college essay got handed back to me with a note atop it that read, “Not college materials – please come to my office hours.” Talk about a moment of sheer panic as a freshman who had straight A’s in high school! But the real challenge was when I started taking upper-level literature courses and immediately realized that I was surrounded for the most part by classmates who were literature kids.

We would read something once and, for them, it seemed to just click. No matter how challenging.

Then there was me: a good student willing to work hard but zero clicking involved whatsoever, not to mention trying to play catch up with many classmates who had considerable backgrounds in their reading of literary classics.

However, it was within that upper-level Shakespeare course (shout out to Professor Greenfield!) that I found a routine that somewhat worked for me:

• Read through a scene slowly to the best of my abilities

• Google to find an online summary (ex: Sparknotes) to clarify what happened

• Return to the scene once more to see what I missed

Rinse/repeat, scene after scene, play after play.

This allowed me to keep my head above water, and over time to build confidence in my own interpretations—confidence that has increased considerably now that I’m in Year 14 of my own career teaching high school English. (Stuff even clicks ever so often now!)

Yet looking back on my own experience and imagining if it took place during today’s timeline, I have zero doubt that I would not have turned to Google or Sparknotes during these struggles as a student to understand what I was reading.

I am 100% sure I would have turned to AI.

What would it look like to use ChatGPT as I read?

As someone who has been admittedly reluctant around bringing AI into my own classroom, I decided to “take a plunge” to push my own learning and understanding around AI from the vantage point of a student.

With a curriculum change in our district, a new Shakespeare play had been foisted upon my 10th grade classroom for the spring semester: Macbeth. While I knew the general story line, I had never fully read through it or taught it before. I created a plan to read it closely over the month of December, taking notes along the way as I prepared for a new “first” for me in Year 14 of teaching.

This time, though, I decided to add a new element to my reading journey: debriefing with ChatGPT after each scene that I read—much in the way I imagine students nowadays may lean on AI as a tool or “thought partner” to support their understanding and interpretations of texts they are studying.

In this post, I want to lay out my three main takeaways from this experience, especially looking forward to a world in which AI chatbots have already become the new “Google” for students seeking support on schoolwork.

Takeaway #1: AI as a Reading Tool = Higher Ceiling

Before LLM chatbots like ChatGPT were around, leaning on an online tool for support in understanding a reading almost always meant the same thing: a summarized version of the section of the text, sometimes with an accompanying analysis.

If it worked for you, great! But there was only so much you could do.

What I quickly realized with ChatGPT as a “reading partner” was that I could address points of confusion almost immediately as I was reading, such as this instance:

And also this instance:

Beyond the obvious benefit, though, of being able to get support with particular lines and questions while reading, I would offer that it changes the reading experience itself to much more of an interactive process.

One of my favorite ways to employ this tool was to offer quick interpretations and understandings for a “stress test,” which I want to offer some examples of in this video walk through:

Ultimately, the opportunity that chatbots like this afford alongside your own reading really does raise the ceiling—especially for anyone who is exploring a text individually. Being able to bounce questions and interpretations as you go enriches the experience and shifts the experience of reading from passive to active.

A higher ceiling, probably. But…

[2] Higher Ceiling? Probably. Lower Floor? Definitely.

Despite the fact that I went into this “experiment” with a quite-skeptical mindset, I’m not going to lie: the first time I caught ChatGPT making a mistake?

I was genuinely frustrated.

As you can see in this sequence, I sort of didn’t let up:

From that point forward, I moved with much more trepidation—arguably a good thing, but also something that changed the dynamic as I was constantly on the lookout for errors.

In a recent post, Carl Hendrick named this phenomenon happening with AI more broadly: The work looks like oversight. What it actually requires is a form of sustained, expert, hypervigilant attention that human cognition was simply not designed to sustain.

That then brings up something that makes me concerned about students turning to tools like this too frequently: I had enough expertise to recognize these errors.

I actually believe this is an under-discussed dichotomy across AI usage right now: (a) those that are using AI to further and deepen their preexisting expertise and (b) those that are using AI to support or even work around their lack of expertise.

For those in the latter camp—almost always: our students!—usage of LLM chatbots for reading support offers a decidedly lower floor, in my opinion. (And that’s before you bring in the students who might be tempted to skip the reading altogether and rely on AI-generated summaries and commentaries.)

[3] At Least Right Now: a Gap Widener

This leaves me with the most important takeaway from my experience of reading the entirety of Macbeth leaning on ChatGPT as a tool: this is going to widen gaps that already existed.

The experience made me more empathetic towards the enthusiasm many have found in adopting AI tools into their own practices to expand and heighten their existing expertise. To be able to offer interpretations as I read real-time and receive feedback, including alternative perspectives to consider?

As a veteran English teacher, I was better off as a reader having used this tool—and my guess is that it is very easy to make the leap from “this tool was meaningful for me” to “we should be bringing it into our schools for students to use.”

However, I think that leap is a mistake.

Factor in that a positive experience was reliant upon an existing expertise that many teachers have in their own content and craft that students are still working on developing—and that we do not have evidence yet that tools such as this improve learning in a sustainable way.

Factor in that, at numerous points throughout the experience, I got bumped down to a “lesser” version of ChatGPT since I was not willing to pay for the premium version—an equity issue that was one of my original fears when ChatGPT was released.

Factor in that the tool itself is an omnipresent temptation to avoid the actual reading of the primary text altogether, a phenomenon of summary-reliance via AI that is having real costs. (This is especially daunting within our broader concerns about student reading, I’d add.)

That’s a lot of factors to overlook, right?

This is why I fear that bringing tools like this into our classrooms risks further widening gaps that already exist. And I’m not going to allow my own mostly-positive experience with ChatGPT as a reading tool to distract me from the myriad valid concerns about what this could mean for student learning.

So what should we do going forward?

Another takeaway for me: I am glad I spent the time doing this.

I continue to believe we are better off as teachers when we attempt to walk parallel to the learning experiences of our students—and right now, doing so means considering how a student may turn to AI at various points in their learning.

I’m sympathetic, too, to the argument that if students are more regularly going to be using tools like this for their learning, as teachers we should be offering guidance of how to do so in a way that supports their learning rather than potentially impeding it.

And yet, I also am reminded that part of our role as teachers is to set an example and standard for what this work can and should be.

This is why I believe it is more important than ever that we are creating spaces for students to read in community with each other. To rely upon and learn from other humans, in the classroom and beyond it, so that reading is not a task to be checked off with assistance from AI but rather an experience to be had collectively.

That same month I journeyed with ChatGPT through Macbeth, I also had the absolute privilege to meet up weekly with educators via Bluesky and chat about our noticings and wonderings. So many jumped in to offer their previous experiences teaching Macbeth, resulting in rich discussions full of resources and, more importantly, a sense of community and belonging.

Yes, I believe we need to meet students where we are. Yes, I believe we need to remain curious and open-minded about new possibilities for student learning—now and going forward.

But I think about all the energy being invested in the development and implementation of AI across education right now, and part of me cannot help but wonder: what if we redirected even a portion of that momentum towards the type of reading experiences in classrooms and schools that not only bring a book to life but also bring people together as a result?

While I did gain some helpful tools and understandings after reading Macbeth with ChatGPT, my real takeaway was a deeper conviction than ever that our students deserve better than that.

And going forward, I’m going to keep advocating for spaces in our classrooms and schools where that better can take place, authentically and collectively.

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The Important Work is a space for instructors (and occasionally students) at all levels—high school, college, and beyond—to share reflections about teaching writing in the era of generative AI.

This week’s post is by Mark C. Marino. It was originally published on Medium.

Mark teaches writing at the University of Southern California, where he directs the Humanities and Critical Code Studies Lab. He is author of Critical Code Studies and co-author of Hallucinate This! an authoritized autobotography of ChatGPT, Reading Project, 10 PRINT CHR$(205.5+RND(1)); : GOTO 10, the children’s series Mrs. Wobbles & the Tangerine House, and (forthcoming) Inventing ELIZA: How the First Chatbot Shaped the Future of AI. You can find Mark on Substack here, on Medium here, on his website, and on social media: Twitter: @markcmarino, Bluesky: markcmarino@bsky.social, TikTok: @Mark Marino

If you’re interested in sharing a reflection for The Important Work, you can find information here.—Jane Rosenzweig

Back in October, I wrote about a new structure to my class, the analog sandwich. Here’s a review of how that went.

tldr: Great!

As a recap, I wanted to offer students in an Advanced Writing course at USC, their final required writing course, some lessons in using AI and a sense of the benefits of writing without it. As classes were going either all analog or various versions of allowable AI, I wanted to have a little of both, perhaps matching my own ambivalence…

The basic idea was to begin the semester by letting students use AI with limitations, to ensure that they all at least had some basic familiarity with what AI could offer their writing process. Then, we moved into a 5-week unplugged unit: no computers, no phones, just paper and pens and books. For the last section of the course, the students were allowed to decide how much AI they would use, albeit still within some constraints (no outright paper generation, for example). In our sandwich analogy: The bread of the sandwich (first and third section) allow for some directed AI use. The middle section of the sandwich, the contents (meat, peanut butter, cheese), is a totally unplugged analog section. It was technically an open-faced sandwich because students could opt to not use AI in the last third.

Preliminary takeaways:

Students appreciated some lessons in AI literacy

From the AI portion, students seemed to benefit from lessons on how generative AI works, how to conduct AI-augmented search, the history of AI, AI ethics, and various exercises like the Perfect Tutor. These were lessons in critical AI literacy. Especially as some students claimed they had never used AI previously, this first part of the class leveled the knowledge playing field a bit before we unplugged.

From the unplugged section, I found even more encouraging results:

Students did not complain of writer’s block.

Despite having five class sessions to write in their of the composition notebooks, there was no day where students spent the class just staring off into the air. While some students wrote faster than others, all students had the option of stopping by my office hours to finish writing to compensate for absences or just slower writing. Everyone kept writing, occasionally pausing to think.

This makes me think maybe writer’s block is not what students call it and is really just a combination of decision fatigue, overwhelm, and perpetual distraction on device. Students seemed happy with the experiment. It was a relief from our digital dystopia. To be constantly assaulted by our to-do notifications and our to-don’t avoidance opportunities is to be constantly ripped out of the headspace of writing.

I think we forget how much distraction comes when writing on our computers, particularly networked devices and — more importantly — how much of our inability to finish writing comes from that feeling of paralysis in the middle of the sea of avoidance entertainments and the million other demands we have on our time.

(Update: In my second go round, one student did spend one class staring into space. She was not sure why. Just was stuck that day. Still, that feels like a low incidence compared to how many students report writer’s block during a typical paper cycle of at-home writing. )

Students enjoyed reading books and printed short stories.

Students (most at least) came to class with books in hand. They seemed to enjoy the tactility of the little volumes. Those of us raised to read in print know the pleasure of a book in hand. The paper was grounding. Paper texts seemed to make our discussions more engaging and interesting, although it was harder for students to hide (behind screens or generated summaries) if they hadn’t done the reading.

Now, one student did say she read the whole book on iPad at home and then had trouble finding pages of quotes, but that seems like a minor inconvenience and maybe a reminder of some modern luxuries. I did enjoy seeing them flip through pages, rather than hit the search bar.

The quality of papers generally matched the distribution in their out-of-class writing.

One of my biggest concerns was that handwriting would put some students at an unnecessary disadvantage. However, since the grade distribution was roughly the same as their take-home, plugged-in essays, writing by hand did not seem to put anyone at disadvantage.

(Note: Any students who needed to type were accommodated.)

When we returned to computers, I felt much more at peace—less distrustful.

I believe it was Maha Bali who said, the biggest damage AI has done in our classrooms is destroy the trust between teachers and students. Now that I had seen their own words in their own handwriting (mostly printing), in an essay they they had worked on over time in front of me, I did not feel that same suspicion, that uncertainty about what was them and what was the machine. I felt okay seeing what they could do with a bit of augmentation.

Best—or at least better—practices

I don’t think all in-class writing is good for students or for writing courses. Setting students up for success made a big difference here.

1. Scaffolding: Because we were writing an APA-style researched essay, students had a structure to follow. I am sure letting them use an outline (which could also be developed in class) would serve the same purpose. But I didn’t want them to generate an outline and bring it in.

2. Low Pressure: The writing occasion was couched as writing time, not a high-stakes test. They were free to revise as they wish, drawing arrows, crossing out paragraphs, adding numbers for me to follow. They could also talk to me or classmates. I kept reminding them that this is not some high-stakes test. That shift removed a lot of the antagonism and trauma-inducing (and unnecessary) pressure of in-class writing.

3. Ergonomics: Writing by hand can be irritating, so we took stretch breaks. Time to get those fingers flexed and wiggling…

4. Humanity: This was a very human activity. Students were free to take breaks, talk with one another, talk with me, and listen to music. I played some low-fi hip hop or background jazz and let students listen to their own music if they preferred. I suppose they could be taking dictation from an AI, but really, why?

5. Revision parachute: Students knew they’d get a chance to revise the paper with a computer for their final portfolio and that I’d be only commenting on these in-class drafts, not grading. That took quite a bit of pressure off. And to be honest, it emphasized what I care about, which is the kind of writing that evolves over time that makes use of all the tools at our disposal.

More results are forthcoming.

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The Important Work is a space for instructors (and occasionally students) at all levels—high school, college, and beyond—to share reflections about teaching writing in the era of generative AI.

This week’s post is by Cate Denial, and it originally appeared on her blog. Cate is the Bright Distinguished Professor of American History and Director of the Bright Institute at Knox College in Galesburg, Illinois. Cate is a pedagogical consultant who works with individuals, departments, and institutions in Australia, Canada, Ireland, the U.K. and the U.S. Cate’s book, A Pedagogy of Kindness, argues that instructors and institutions of higher education must urgently focus on compassion in the classroom. You can read more of Cate’s work on her blog and find her on Bluesky.

If you’re interested in sharing a reflection for The Important Work, you can find information here.—Jane Rosenzweig

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We’ve been living with GenAI for some time now, and there are as many ways to approach its use (or not) in the classroom as there are instructors grappling with its existence. In this post, I sketch out how I’ve chosen to work with undergraduate students in my history seminars on issues surrounding GenAI: by focusing on their AI literacy, and on having them reach a real liberal arts understanding of the issues surrounding this tech.

Some caveats: my policies and practices are shaped by the particular institution for which I work. I rarely teach more than twenty-five students in any given class. I rarely lecture. I have ample opportunity to check in one-on-one with students as they work. The particular way I scaffold our conversations about GenAI reflects this. Some parts are easily scalable (such as the phone game) and some are less so.

(I also do not believe my way of approaching things is the only way to do so. There are a range of ways to approach the impact of GenAI on higher ed—this is just one of many.)

So let’s begin.

I have a clear policy in my syllabus about both laptop and GenAI use:

I’ve chosen to continue to allow students to use laptops, tablets, and phones in class, as I have not yet found an analog approach to assessment (in particular) that I find fully inclusive of disabled students who lack the documentation to qualify for formal accommodations. (If anyone has solved this particular puzzle, I am all ears!) I do, however, ban GenAI use in my classes. I try to explain both policies in my syllabus with the pedagogical choices I’m making front and center. I do not, for example, lead with “We will not use generative AI in this class,” but rather explain why that’s the case before getting down to details. I always have my students annotate the syllabus (thank you, Remi Kalir!), which precipitates a conversation about their thoughts, concerns, and questions related to anything in the document. This is, then, an invitation a conversation about how the class functions, and is often a place to begin a discussion about GenAI.

On the second or third day of class (depending on the schedule) I dedicate an entire class period to wrestling with GenAI. We begin class that day with a game. I ask everyone to take out their phone, pull up their texting app, and write the history of yesterday using only the predictive text options their app supplies. Here’s a screenshot of what that looked like on my phone today:

You can see the predictive text options my phone offers below the message I’ve written: it suggests ‘in’ ‘and’ and ‘with’ as words I might use next. These predictive text options are highly individualized, as our phones draw on past use to determine what we’re statistically likely to talk about most. (Apparently I’m very interested in environmental policies at the moment!) This means when students read their histories of yesterday aloud they are unique and often completely wild stories that may or may not have anything to do with how they spent the day before. (I did not spend yesterday concerned with energy policies, for example, although I did get a text to say my budget-billing payment for utilities was going up.)

The game provides an opening to discuss how GenAI models work. GenAI draws on all the data it has collected to statistically predict which words might follow other words and build competent-sounding sentences. GenAI products do not think; they do not feel; they do not lie. They simply predict that in X instances of Y words occurring in Z order, it is highly probable that A will be the next word in a phrase. My students and I talk about the enormous amount of data that these models work from (because GenAI companies stole copyrighted works to build that corpus), meaning that—more reliably than our cell phones do—the sentences and paragraphs that GenAI builds are well-matched to our prompts, and often sound good.

To emphasize the lessons tied up in this game, I assign Mark Reidl’s “A Very Gentle Introduction to Large Language Models Without the Hype” from 2023. In much greater detail than I can hope to delve into in a single class, Reidl walks through how LLMs work (complete with handy graphics), and his explanations always prove to be something of a surprise to students who have assumed that GenAI models have agency, when they don’t.

Here’s where students might be forgiven for saying, “so what?” And that’s why I assign Carl Hendrik’s “Ultra-Processed Minds: The End of Deep Reading and What It Costs Us,” from 2025. This essay is the most marvelous prompt to have a meaningful conversation about the many difficulties we’ve all faced in the last several years, and the impact that world events can have on our practices of reading and thinking. I’m no exception to this—my own reading is not as steady as it once was; I struggle with my concentration. It is a relief to students to understand that they are not singularly at fault for struggling; that this is the experience of thousands of other people. And it offers an overture to think about why we write, why we read, how we think, and what we give up when we try to take a shortcut through that process.

From here my students and I grapple with the ethics of GenAI use. I pick several articles from my Against Generative AI blog post, allowing students to consider environmental concerns, labor exploitation (particularly in Africa), privacy issues, ableism, biases, mental health effects, and the ethics of using something entirely built upon stolen work. Students supplement these readings with what they already know or have experienced about GenAI, or news stories they’re aware of that offer a more positive take on LLMs. Our conversations are robust, but in every iteration of this conversation I have had with students in the last two years, there is one constant: almost no one knows about the ethical issues surrounding GenAI before they delve into the readings I’ve assigned. (We talk about why that might be the case, too.)

Lastly, we talk about disinformation—a bigger issue than GenAI alone, but one that history students have to be particularly aware of. We discuss the tells that marked an image as fake early in AI’s lifetime, and how both more sophisticated programming and individual effort can circumvent many of these tells nowadays. (Still, it’s always good practice to look for six-fingered hands.) We also discuss the rate at which GenAI will statistically predict a citation that looks flawless, but is in fact only a representation of how citations often appear. It is, in other words, fake, necessitating that students must genuinely read anything they put in a bibliography or footnote, and not merely pad either one. (We also talk about how to identify articles that are based on erroneous, even AI-generated evidence, a skill it’s essential any historian have.)

It’s a lot to get through in one class period; sometimes this might spill into two. It’s worth it to me to spend this time with GenAI—to have deep conversations about what it can do and what it cannot—since it greatly lowers the chance that a student will use GenAI down the road.

Every choice I make about how to discuss GenAI with my students is predicated on trusting them: trusting them to be partners in figuring all of this out; trusting them to take the project seriously and try out multiple lines of thought; trusting them to respect the ban on GenAI in my class. If someone uses GenAI anyway, I can deal with that when it happens. I would rather deal with outliers as and when they occur than approach every student with suspicion from the outset.

No single approach is perfect, and I will refine this way of dealing with GenAI for years to come, I’m sure. And as and when GenAI can offer something to student historians that they cannot do under their own steam, I’ll consider rethinking my position. But I do relish getting to enter into wide-ranging conversations with students about our shared identity as historians, and that, perhaps, is the best part of all of this work.

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The Important Work is a space for instructors (and occasionally students) at all levels—high school, college, and beyond—to share reflections about teaching writing in the era of generative AI.

This week’s post is by Gabriel Wu, who is a first-year student at Harvard studying applied math and film and visual studies. This piece originally appeared in the Ideas section of the Boston Globe.

If you’re interested in sharing a reflection for The Important Work, you can find information here.—Jane Rosenzweig

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For many college students, the pull toward artificial intelligence is irresistible. I regularly hear stories of my peers who once planned to go into academia or to medical school but have now decided to pursue AI startups. It’s hard not to be drawn into the idea that we must study AI or get left behind. I’ve even considered reworking my entire course schedule just to fit in the prerequisites for an AI course I wanted to take by my junior year. When anything involving those two letters seems revolutionary, specializing in AI feels like the next wave to ride.

But I’ve decided to go in another direction. I am not restructuring my entire schedule around a single AI course. Instead, this spring I’m taking classes in math, economics, and film, and even one on sleep.

The reason: I don’t want to lose sight of what makes a liberal arts education valuable.

AI fervor is swelling on campuses. In only three years, a new major at MIT — “artificial intelligence and decision making” — has become the school’s second-most popular field of study. In 2024, the University of Pennsylvania established the first AI engineering degree in the Ivy League. This past August, the University of South Florida introduced a new college dedicated to AI and cybersecurity, which has already enrolled about 3,000 students.

This is no surprise when you consider that for many students, college is a path to well-paid employment, and when automation is reshaping the labor market, majoring in AI may appear to offer a competitive advantage. However, a recent Stanford study found that recent graduates saw a 6 percent decline in employment in the careers most exposed to AI from October 2022 to September 2025. In that same time frame, older workers in those same roles saw employment increases of 6 to 9 percent.

The authors of the study attribute this disparity to the nature of undergraduate education. They argue that the explicit knowledge gained in college, such as textbook information and lectures, is exactly what AI systems are trained to reproduce with superior speed and accuracy. What shields older workers from automation is the one thing that AI cannot replicate: their unique lived experiences — and their ability to interpret problems through cultural, ethical, and social contexts accumulated over time.

That’s why I’m committing to a liberal arts education. Its interdisciplinary rigor pushes students to think critically across a vast spectrum of human knowledge, synthesizing perspectives from the humanities, social sciences, and natural sciences to approach problems that no single discipline can fully grasp.

Admittedly, it can be hard to see the value of the liberal arts when AI can already write, code, and think for us. In 2024, 15-year-old Nick Dobroshinsky used large language models like Claude and ChatGPT to develop BeyondSPX, a financial research platform that now has more than 50,000 monthly users. Yet if a high schooler can now use AI to perform the tasks of a software engineer, any technical fields that I study in college might be obsolete by the time I graduate.

A decade ago, a computer science degree would have opened the door to lucrative careers. Today, graduates with that degree face a 6.1 percent unemployment rate — one of the highest among all majors. Similarly, the way we currently approach AI may not be especially relevant in 10 years. Students who train exclusively for today’s AI-oriented jobs may find themselves holding degrees tailored to a bygone moment.

This is precisely why studying a wide range of topics matters: The foundational skills developed in the liberal arts endure consistently even as professional trends shift. In research published in 2020, the economists David Deming and Kadeem Noray showed that technological progress leads to higher rates of turnover in STEM careers as technical skills become obsolete. A liberal arts education, however, fosters skills that don’t have a shelf life and thus have much lower rates of skill-related turnover. Though initially outpaced in salary, humanities graduates often catch up by their 40s because their expertise remains versatile even as the economy evolves. AI is already reshaping industries and the specialized skills tied to it. The liberal arts, however, hold their value because while technologies evolve, the need for judgment does not.

AI can help us build the future, but the liberal arts help us decide what kind of future is worth building.

As I log into my student portal to look at courses for my sophomore year, I plan to enroll in another technical course. Still, I’m drawn to the classes that are “useless” in a vocational sense — these are the ones that will best teach me to think, to doubt, and to judge.

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The Important Work is a space for writing instructors at all levels—high school, college, and beyond—to share reflections about teaching writing in the era of generative AI.

This week’s post is by Katie Conrad, who is a professor of English at the University of Kansas. Her research interests include modernism, Irish literature and culture, technology studies, fandom studies, and critical AI literacy. She co-directed the AI & Digital Literacy Institute in conjunction with the National Humanities Center in 2024 and 2025. You can find her on Bluesky, LinkedIn, and Substack.

If you’re interested in sharing a reflection for The Important Work, you can find information here.—Jane Rosenzweig

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Many educational institutions, from kindergarten on up, have been buying enterprise licenses to AI “education tools” from companies like OpenAI, Microsoft, Anthropic, and Google and education-focused AI app suites from Flint, MagicSchool, Brisk, and more (see e.g., Ford and Knox 2025, Field 2025, Hale 2025). These procurement decisions are coming even when other funding for education is at risk or under threat. And they are coming even in the face of increasing evidence that ed tech doesn’t pay off in student learning (as, for instance, outlined in the recent article in the Economist, “Ed tech is profitable. It is also mostly useless”).

Sometimes actual educators are at the table when ed tech purchasing decisions are made; frequently they are not. And from what I’ve heard from teachers at all levels, many of our colleagues don’t feel technically knowledgeable enough to help make these choices. But let me assure you that very, very few of the people making those decisions know much, if any, more than you do, other than what the companies selling them the products are telling them. And as educators who have expertise in content and practice, I believe we have the responsibility to protect our students and that we “should have input into institutional decisions about purchasing and implementation of any automated and/or generative system (“AI”) that affects the educational mission broadly conceived” (“Blueprint for an AI Bill of Rights for Education, Critical AI, 2023/2024 – unpaywalled version here).

My purpose here, then, is to provide some ways into the conversation with visiting vendors and tech companies—or the IT professionals and administrators who are making the purchasing decisions. There is a lot at stake here. We and our students shouldn’t be the lab rats for untested educational technologies with insufficient guardrails. Research is suggesting increasingly that genAI use causes cognitive harms (Zhai et al 2024, León-Dominguez 2024, Darvashi et al 2024, Dergaa et al 2024, Kumar et al 2024, Lee et al 2025, Gerlich 2025); that their “efficiencies” are illusory (the METR study); and that 95% of companies that have invested in AI are getting no return on investment (Nanda et al 2025). More than that, chatbots pose risks to the safety and well-being of our students, colleagues, families, and friends. Just ask the victims of deepfake porn. Just ask the families of Adam Raine and Austin Gordon and Sewell Garcia.

One of my least favorite arguments in favor of institutional license purchases of AI ed tech—and it comes from those who aren’t necessarily actively excited about that tech—is “that’s the only way to keep our institution’s data safe.” But remember, the choice not to keep everyone’s data safe and not used for training is, and always has been, in the hands of the companies. As I’ve written elsewhere, charging you for that “privilege” is like paying protection money to the mob.

So I think we need to ask questions in the few instances when these companies show up on our campuses. I recently wrote about my experience asking questions when Google came to my campus. From that post, here are some questions for those of you out there who want to ask but might not know where to start:

For Google and Perplexity:

• You are joining a lot of tech companies providing free access to subscriptions for college students and not for teachers. Given that there are risks of learning loss and a range of ethical and use issues like hallucination to consider before using AI tools, it seems like it would be wise to at least offer the same free access to teachers who could guide students on best practices. Without that, it could seem like you’re doing an end run around teachers and instead promoting an idea of education without educators. Is this a pressure tactic to get teachers to pay for your services and get schools to buy enterprise licenses? Can you comment on your decision to provide students free access and not teachers first?

For Google:

• In the fall of 2025, Google put something called Homework Help in the Chrome browser update, which allows students to solve homework, write discussion answers, and take tests with Google AI software. It was removed by that name in later releases but it’s effectively still there, just as Google Lens. Are you in conversation with that team and with actual teachers to ensure that your products are in alignment with, rather than undermining, educational learning goals?

For all AI-based ed tech companies (the big ones plus Brisk, Flint, MagicSchool, SchoolAI, etc.)

• OpenAI has admitted that hallucinations are baked into generative AI systems. And as experts note, those mistakes are plausible, don’t come in the same places as human mistakes, and can’t be mitigated in the same ways. Why should educators, researchers, and students ever be using a technological system that will always potentially insert mistakes?

• There have been a lot of studies that show negative cognitive impacts of generative AI use. Cognitive offloading isn’t necessarily always bad, but this is arguably not what we want in an educational environment. There have also been studies showing reductions in retention after using LLMs for tasks, reduction in critical and creative thinking, and so forth. How do you recommend mitigating the kind of learning loss that seems to result from systems like yours? (Check out some of the studies here under “Cognitive Impacts,” and share newer studies in the comments!)

• Could you share with us whether all of the datasets on which your AI models are trained come from consensually obtained, licensed, or public domain materials? (If they say they train on “publicly available” data, note that that is not a legal category, and publicly available material still has copyright.)

  • Follow-up: Kevin Gannon, in his recent Chronicle article, writes: “Either we have copyright law or we don’t. Either plagiarism and the theft of intellectual property are anathema to higher education or they aren’t. We’re either modeling academic honesty and integrity to our students or we aren’t. … GenAI’s architecture absolutely depends on consciously taken actions that would stand in violation of any of our institutions’ academic-integrity policies.” Could you respond?

  • Alternate follow up: Could you explain why we have to pay for enterprise licenses for your intellectual property, but you don’t have to pay for the intellectual property you used to train your systems?

• Is all student and faculty data guaranteed to be protected in all of your systems and also guaranteed not to be used for training? If not, do we have to make a special deal or licensing arrangement to ensure that protection? Why is it not already protected?

For companies that have partnered with the military (OpenAI, Google, Meta):

• Do you have an AI safety team for your education division, and is it the same safety team that oversees your collaborations with the NSA and the US military?

There are, of course, many other questions worth asking. As a few of my fellow critical AI colleagues have noted, even just getting reps (or administrators) to answer the question of what they mean by “AI” can be quite revealing. But in an environment in which you may be the only person asking (and I’d encourage you to get at least one other colleague to join you in asking questions, which can encourage others to speak up), you might want to pick a question that will get others thinking about the answers that are (or aren’t) provided.

With that in mind, it’s also worth looking at this list of questions from the Library Freedom Project called Questions to Ask Vendors About AI. These very pointed and important questions cover a range of issues. While they are focused on library applications that embed AI, they could easily be adapted to ask any of any ed tech that incorporates AI. They fall under the following general headings:

• Basic and technical functions of the tool

• Environmental considerations

• Labor expectations

• Copyright, data, and privacy considerations

• Revenue expectations

Charles Logan has also provided “Caregiver Resources for Pushing Back Against AI and Other Educational Technologies” that includes Questions to Ask School Administrators and Staff About Technology,” with questions that fall under the following general headings:

• The technology’s purpose(s)

• How the technology works

• Data

• Privacy

• Security

• The vendor

• The fine print

• Teacher professional development

• Students’ experiences

• Parents’ and caregivers’ experiences

• Other

Adapted from both of these lists, which I highly recommend you read in full, some key questions I would (publicly) ask vendors, tech reps, and/or teachers and administrators (as a caregiver or as a teacher/staff member, no matter the level):

For all levels:

• What is the purpose of this AI application? What problem does it solve? What is the research that shows that it solves it?

• If this AI app harms students, either directly through outputs – such as guiding students to self-harm—or through data breaches, who takes the legal responsibility for that harm and what would that responsibility entail? Where is the liability contractually set out? Given that many major insurers are stepping back from coverage of AI harms (Harris and Criddle 2025), who will provide compensation if such is necessary?

• How does this app/technology align with our institution’s mission and goals (for instance, environmental sustainability)? Who will be formally auditing this AI app to ensure that it continues to meet the institution’s mission and what does that audit process look like?

For K-12:

• Given the documented risk, is parental consent required for the use of these apps for those under the age of 13? What are the alternatives for those who do not consent? What kind of training do you provide for those parents who do consent to their children’s use of these apps?

Again, asking the question—preferably in a public, open forum, but really at any time—is often enough to begin a more critically informed, transparent conversation and decision-making process. The problem is that these questions frequently aren’t being asked at all. That is something we have the power to change.

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The Important Work is a space for writing instructors at all levels—high school, college, and beyond—to share reflections about teaching writing in the era of generative AI.

This week’s post is by Stephen Heard, who is Honorary Research Professor at the University of New Brunswick, in Fredericton, NB, Canada. He is an evolutionary ecologist who thinks a lot about scientific writing—how we do it, and how we teach it. He is the author of The Scientist's Guide to Writing and coauthor of Teaching and Mentoring Writers in the Sciences, and he blogs at Scientist Sees Squirrel.

If you’re interested in sharing a reflection for The Important Work, you can find information here.—Jane Rosenzweig

“Write up your results in the style of a scientific paper.”

Untold thousands of undergraduate science students have received that instruction, and it’s done a lot of damage to our professional literature. These days, AI writing tools may be making the problem even worse—although with good instruction, they could offer solutions instead. Puzzled? Let me explain.

Like any writing genre, scientific writing has conventions of format and style, which science students need to learn so that they can produce writing that meets reader expectations. Some of those conventions are extremely helpful, like the “IMRaD” (“Introduction, Methods, Results, and Discussion”) system of organization that helps writers structure a paper and helps readers locate information in one. Other conventions are productive in moderation but are often taken too far, like the use of technical terminology to communicate with precision—or, less happily, to bewilder and tangle an unsuspecting reader in impenetrable thickets of jargon. Still other conventions are simply unfortunate relics of past bad decisions—like many scientists’ lingering insistence on the passive voice.

The tricky reality of these conventions is that science students seldom learn them via formal instruction. Full courses in scientific writing are unusual, and required courses that come early in a university career are truly rare. Instead, things typically play out like this: instructors ask students, usually in a 1^st or 2^nd year undergraduate course, to write up laboratory results “in the style of a scientific paper.” Unsurprisingly, they aren’t sure what that means, and so instructors may offer them follow-up advice: “go to the library, read some scientific papers, and write like that.”

And therein lies the problem. The scientific literature our students find largely consists of papers that are tedious and turgid, bloated with complex sentences, festooned with acronyms, and written doggedly in the third person and the passive voice. So that’s what they write, and that’s what we (too often) reward them for writing —all the while groaning about having to read it. Later in their careers, many of those same students add their own papers to the published literature—papers that reflect what they learned from imitating what was already there. The feedback loop continues, with the next generation of students finding yet more tedious and turgid papers to imitate in their turn.

Perhaps I’m painting this too darkly. There are, after all, folks fighting the good fight: arguing that academic writing can be both professional and engaging. Consider, for example, Anne Greene’s Writing Science in Plain English, Helen Sword’s Stylish Academic Writing, or even my own book, The Scientist’s Guide to Writing. There are instructors using those resources and others to encourage developing writers to do better than what they see in the literature. But it’s an uphill battle, because the feedback loop is powerful. And sadly, the sudden availability of AI writing tools (LLMs) has made the uphill steeper.

Whatever institutional or course-level policies might say, many students have adopted LLM tools —some with enthusiasm, some with reluctance. My informal surveys of graduate students in the sciences suggest that at least 80% of those students use LLMs in some way; among undergraduates, a recent survey at MIT found 46% of undergraduates use LLMs daily and most use them at least occasionally. Some of those uses are disturbing (“ChatGPT, please write this paper for me”); others suggest promising ways to harness new technology for learning (“ChatGPT, I’m not a native English speaker. Can you help me polish the grammar in this draft?”). But one use I’ve seen is directly relevant to this column: “ChatGPT, can you revise this draft in the style of a scientific paper?”

Students are understandably eager to find help formalizing their writing and conforming with genre conventions. After all, even when they aren’t taught those conventions explicitly, they know that they’ll be evaluated on their success in deploying them. And in asking LLMs for help in reproducing the style of a scientific paper, they’re (perhaps accidentally) using those tools in exactly the way they’re designed to excel: producing text that sounds like a human writer (of a particular genre) produced it. Sure enough: if you feed a draft text into ChatGPT and ask for a revision in the style of a scientific paper, that’s what you’ll get. In spades!

Because LLMs use next-token prediction to reproduce statistically likely patterns from the text they’re trained on, they do a spectacularly good job of “the style of a scientific paper”: tedious and turgid phrasing, passive voice, and bloated, complex, sentences festooned with acronyms. You can think of this as just one more example of those tools’ tendency to reproduce and even amplify biases from their training data: an LLM asked to describe a scientist will almost certainly describe an older white man, and an LLM asked to produce scientific writing will cheerfully churn out long, cumbersome, acronym-laden sentences:1

Fortunately, the same tools that can amplify the faults of academic prose can be used to chip away at the problem. Here’s my approach: I push students to ask the LLM for help deformalizing a draft instead of help formalizing it. (A useful prompt is something like “Here’s a draft of the Introduction for a scientific paper. Can you suggest revisions to make it less formal, more readable and more engaging?”) And then— crucially— I ask students to do one thing more: to inspect the suggestions, and ask themselves which improve the draft, and which do not. Where the LLM suggests informal phrasing, will scientific readers find their expectations violated —with the revision being too informal to belong recognizably to our literature —or will they enjoy the written breath of fresh air? Where the LLM suggests replacing jargon with a simpler word, is important precision lost, or is the text just easier to read? Is an added metaphor distracting, or thought-provoking?

Reflection is a powerful learning tool, in general, and I try hard to be using it throughout my writing instruction. When students are asked to think carefully about LLM-suggested changes— to decide whether each is helpful or not, and to enunciate how it is and how they decided that— they’re engaging in active learning, and they’re far more likely to retain a new writing habit than if they’re simply given suggestions they can accept wholesale. [This is true, of course, whether the suggestions come from an LLM, a peer, an instructor or mentor, a writing centre consultant, or anyone else.] Because reflection is so important, I often codify it as part of writing assignments. Here, for instance, I might ask students to submit a paragraph outlining two LLM-offered changes they accepted and two they rejected, with reasons; or for students needing more structured prompting, I might make this part of a worksheet they fill in to submit alongside a draft.

How, you might wonder, are students to know whether a particular suggested change is a good one or a bad one? LLMs, after all, do make bad suggestions (as, sometimes, do peers and mentors and writing consultants!) For example, an LLM might suggest replacing a piece of jargon with a more common word, but doing so actually removes important information. For two reasons, I’m not terribly worried about students sometimes accepting bad suggestions. First, it’s rare that there’s a single “correct” way to write something, and realizing that there are multiple ways to accomplish a writing goal is itself important for a developing writer. Second, it’s not the particular draft in front of me that I’m most concerned with—it’s the writer. That a writer has thought through a decision, working to justify it to themselves and to me, is the progress I’m looking for. Nonetheless, using a reflection worksheet or assignment lets me catch some dubious decisions and use them as teaching moments – spurring, I hope, a second round of reflection from the student.

How has this worked? It’s early days. I’ve begun using the deformalization exercise with graduate students, and I’m encouraged. Students are receptive to the idea of LLM feedback; in fact, they’re often willing to share an early draft with an LLM when fear of judgment makes them reluctant to share with a mentor. They are often quite excited to realize that scientific writing isn’t as tightly constrained by convention as they believed— that they can think about writing choices and defend those choices. And many of them apply what they’ve learned through reflection in future, better, first drafts. I can’t yet be sure that this is making a long-term difference (LLM tools are simply too new for that). But seeing students think and learn actively about writing is rewarding, and I’m always glad to find a new way to provoke that thinking.

Finally: how will the deformalization exercise work with writers earlier in their development—say, freshmen or sophomores learning to “write in the style of a scientific paper” for the first time? I don’t know, yet, but I’m eager to find out. I suspect these experienced writers will need more structured guidance, perhaps with scaffolded assignments that take them through several rounds of draft-revise-submit so that they can benefit from instructor feedback on the decisions they make regarding LLM outputs.

Students can— and sadly, many do— use LLMs to avoid thinking about writing; but you and the students you work with may see the payoff (as I have) when LLMs instead spark thinking about writing. And in the long run, that’s the goal: students who see writing as a craft that they can work deliberately to improve, not just in the course at hand but over a long career. That, to me, is the important work. And if the result could be a scientific literature that’s just a bit easier on its readers – that would surely be a welcome bonus.

This essay is based in part on material from my book Teaching and Mentoring Writers in the Sciences: An Evidence-Based Approach, coauthored with Bethann Garramon Merkle, which is available from the University of Chicago Press or wherever you like to get your books.

Note from Jane: We have a number of new pieces in progress, but nothing ready this week (new semester, everyone is busy!). We also have a lot of new subscribers (welcome!), and so I thought it might be a good time to dig into the archive. This piece, written by a high school student, was on my mind yesterday after I spent some time reading and thinking about this new report from the Brookings Institution. The authors of the report have called this a “pre-mortem” on AI and education—and much of the report focuses on the risks of generative AI for students. Their big takeaway is this: “we find that at this point in its trajectory, the risks of utilizing generative AI in children’s education overshadow its benefits.”

There’s a lot to think about in the Brookings report, but something that stayed with me throughout the day was in the passage below: “AI is doing things for students that they used to enjoy.”

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I’d love to hear your thoughts about the report and what your students are saying/thinking/feeling about generative AI. In the meantime, here’s Sam Barber’s piece from August.

If you’re interested in sharing a reflection for The Important Work, you can find updated information here. —Jane Rosenzweig

Near the end of my sophomore year in high school, a classmate of mine received extra credit on an English essay using AI-generated text. I could tell it was AI just by hearing it read aloud from across the classroom, but my teacher praised the student for her creativity and she got her extra credit points. Another time, I was working on a group project with someone who generated all of his writing with AI and he received the same grade as me for work that was not his.

Even when they’re not using AI tools to cheat, it feels like almost everyone at my high school uses AI in some capacity for their schoolwork. I've seen people use it to help study for tests or explain difficult concepts and even generate entire paragraphs for English class. Many of my friends who don't use it to generate writing will use it as a study guide for harder classes. Someone I know made a practice test for the AP US History exam with ChatGPT, and after taking it, I noticed that several of the answers it gave were incorrect. It bothers me that there is clear evidence that these AI models are not reliable sources of information, and yet people continue to use them to study.

I understand that some people have found AI helpful for studying. It’s good that people can find ways to understand material that they are struggling with, but students blindly trusting that AI will always be a reliable source for this sort of thing could lead to them getting wrong information. And right now, no one is teaching us how to judge whether it’s reliable.

I think that using AI for school writing vastly diminishes students' ability to think critically and problem solve. The valuable learning you get out of a research paper won't be the information you research; it'll be the way in which you figure out how to find, organize, and present that information. Creating an AI-generated paper in five seconds will deprive you of all of that. Getting the assignment done without thinking might get you an A, but only by actually doing the work will you develop important skills that using AI will not give you. I don’t remember every topic that I researched in the past two years, but what I did learn is how to find reliable sources and figure out what questions I have and how to answer them.

I'm deeply bothered by the fact that using AI seems to be becoming the norm: I've seen people react in surprise when someone says they don't use it, and people who have it open constantly on their computers, ready to use it to generate entire English assignments without batting an eye. It worries me that high school students are unable to write a three-page essay and resort instead to AI to do it for them, because I saw people doing writing on their own before AI was so widespread, and I’m worried that the easy shortcut of using AI is causing people to lose those skills. If we keep going this way, we might end up graduating without even noticing that we’re not having thoughts of our own.

Part of me understands why people are using it. Many of the assignments we are being assigned at my school are not engaging enough. For example, when faced with an essay on the symbolism of a book, something that we did multiple times in English class last year, I get that people view it as a waste of time and want to get it done with AI. Even with those kinds of assignments, I’m still thinking when I’m doing them myself, which is not something that you get when you use AI.

I think a lot of the growing dependence on AI comes from a lack of information and education about artificial intelligence. At my school, we have not been given any information regarding AI, other than a few warnings not to use it from teachers. One teacher told us that she would be able to tell if we used it, but people didn’t seem to believe that. The school seems to believe that if they bring it up and talk to us about it, even more people will use it, but I think that if we're going to exist in a world with artificial intelligence, everyone needs to be educated about it. I think that students can’t fully make a choice about how they want to use it until they know more about it.

Not only have we not learned about AI, but many students have also never been told off or faced consequences for using AI, so they just keep using it to cheat. Instruction should be given to both teachers and students about how to identify AI-generated text and why AI is not always reliable in order to make sure that people using it are aware of possible risks.

In the midst of what we’re all reading about students using AI to cheat, I want teachers to know that there are still people like me that want to learn and don’t want to cheat in school. I think that if our teachers could actually talk to us about AI and explain why it’s valuable to do the assignments on your own, we could avoid some of the risks using AI creates. I also want teachers to know that if we could exert more agency over what assignments we were doing, we might all be more interested in what we’re doing and care more about learning.

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I especially want to thank for hosting several wonderful events at Babson College for Boston-area writing instructors to talk about teaching in the AI era (read all about that in Kristi’s post here). I first had the idea for The Important Work while I was attending the first event and a few of us were talking about how we could keep the conversation about AI going. Soon after that event, I signed on to Substack and set up this newsletter without knowing exactly how I would get it going. I just…put it out there. The response has been amazing, and I’ve learned so much from everyone I’ve worked with.

As we head into 2026, here are just a few of the things I’ve learned from publishing The Important Work this year:

• Pieces by students and about how students are grappling with AI have been our most-read and most-shared. Topping that list are the pieces that featured the voices of actual high school students: Sam Barber’s “At my high school no one is talking about AI” and William Liang and Liz Rose Shulman’s “AI is creating a divide between teachers and students.” As someone who has been teaching a writing course about AI for the past five semesters (more on that here), this doesn’t surprise me. I consider myself extremely lucky to be able to discuss AI with my students openly and often, and I hope we can all do more of this in 2026. We all want to know how access to this technology is affecting our students—and our relationships with them. I also hope to publish more student voices in 2026.

• So many instructors are meeting this moment with creativity, with a willingness to experiment, and above all, with a deep commitment to their students. Some posts that have sparked a lot of conversation among instructors this year include Helen Choi’s decision to assign her engineering students a whole book; Danielle Kane and Claire Mason’s experiments with reimagining blue books as tools for in-class thinking and writing; Maggie Boyd and her team’s creation of a tool for thinking about AI use; and Brett Vogelsinger’s “transparency survey.”

• High school and college instructors have much to learn from each other. This has always been true, but with AI in the mix, it’s imperative that we talk to each other. If you teach college and you’d like to learn more about what’s going on in high school, you could start by reading Marcus Luther’s The Broken Copier and Stephen Fitzpatrick’s Teaching in the Age of AI. (Both have contributed to The Important Work.)

• We’re all a part of this conversation, no matter what and how we’re thinking about AI—or whether we’re using it in the classroom or not. After all, the conversation we’re having isn’t about AI; it’s about teaching. (A few of our pieces about the value of human writing that sparked discussion this year include Cate Denial’s “Why I’m Saying No to Generative AI” to Michael Burns on his his shifting views about AI to João Batalheiro Ferreira’s contemplation of the importance of shaping sentences)

• The human interactions that I’ve had with the authors of these 31 pieces have been a highlight of my year. If you’re thinking about AI (and if you’re reading this you probably are!), I highly recommend seeking out others at your institutions and beyond to talk through the challenges we’re all facing. And although I’ve highlighted a few pieces in my comments above, I highly recommend reading all of our posts.

We already have several interesting posts in progress for 2026. If you have a reflection to share, please reach out! You can find information on how to propose a piece here.

Thank you for reading The Important Work. If you have a moment, please do share this newsletter with a colleague or friend—or on social media. (And yes, we are still using the em dash here at The Important Work!) I’d like our writers to have the widest audience possible, and I hope to continue to grow the conversation in 2026.

In the meantime, I’m wishing you all the best for 2026. Your students are lucky to have you, and no technology can replace what you bring to your classrooms.

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The Important Work Submission Notes

• Pieces should be reflections on your work in the classroom. We won’t be publishing advertisements for AI products or pieces by people who are profiting from the technology they’re writing about. But, of course, we are interested in hearing what AI tools you’re using in your classroom where relevant.

• The question of “what is the important work?” is the guiding principle here, but your post doesn’t need to directly mention the question. The goal is for writers to be reflecting on this question from many different angles.

• Because this is a newsletter for those who assign writing/teach writing/think about student writing, we’d like to stay focused on what you’re thinking about/doing related to teaching rather than related to AI in more general terms (although there are plenty of places to publish those pieces as well).

• Pieces should be 1000-1500 words unless they need to be longer—then we can discuss.

• Your piece will be your own work and you’ll retain all rights to republish it (this will be spelled out in detail for you).

• We have no funding (at least for now) and aren’t planning to charge for this newsletter, so this is not a paying gig. I support all desires to be paid for your work, and I’m happy to consult with you about other venues (instead or in addition to this one) that do pay. The flip side: this is an informal opportunity to share your thoughts, and you don’t have to spend a month drafting your post.

Examples of what you might consider sharing:

• An assignment you have changed/created in the AI era and how it went/what you learned/what surprised you

• Why you have decided to bring generative AI into the classroom/not bring it into the classroom.

• A problem you had in your teaching that you think you’ve solved

• A reflection on a particular aspect of teaching writing in the age of AI—feedback, reading, brainstorming, research, drafting, etc.

• Reflection on something that has surprised you about how things have unfolded

• A question you have that you don’t think people are talking about enough

• An insight about how students are using generative AI that you don’t think has been given attention

• A concern you have and how you’re thinking it through

• A positive experience you’ve had and how it’s changing your teaching

• A narrative of what you’ve been doing differently

• What you want high school teachers to know about college writing/what you want college teachers to know about high school writing

• Many ideas I’m sure we haven’t thought of yet!
  
  

The Important Work is a space for writing instructors at all levels—high school, college, and beyond—to share reflections about teaching writing in the era of generative AI.

This week’s post is by Shannon Mooney. Shannon is an Assistant Teaching Professor of English at Babson College, where she teaches courses such as Research Writing and Writing for Social Change.

If you’re interested in sharing a reflection for The Important Work, you can find information here.—Jane Rosenzweig

At some point during my senior year of college, an English professor left a relatively minor comment on one of my essays about my tendency to rely—somewhat heavily—on the em dash when emphasizing a point. While this wasn’t necessarily a problem, he noted, it might be more impactful if I were to use it sparingly.

At this point in my undergraduate career, I thought of myself as a “good” academic writer, so I was dismayed to discover that his observation was spot on. I used the em dash a lot, to a distracting degree. Even more perplexing to me was the fact that I had no idea where or how I picked up this habit. I set out then and there to be more intentional and sparing with my em dash use.

If you’ve recently encountered text generated by LLMs like ChatGPT, you likely know where this piece is headed. Over the past year, there’s been an uptick in articles commenting on ChatGPT’s frequent use of the em dash. The em dash has become, for some, an AI “tell,” a “GPT-ism,” a hint that generative AI may have been used at some stage of drafting. Many debates about ChatGPT’s use of the em dash have remained focused on the punctuation mark itself: why it’s still okay to use; why writers deserve a right to the em dash; whether we should be using it at all; what punctuation can be used instead. The em dash debate, though, is about more than the punctuation mark. It reveals something deeper about writing and discourse, like the stylistic flairs we deem “academic,” who can access and perform this discourse, and the way in which ChatGPT seemingly makes this discourse more accessible.

While I will never be able to pinpoint when or why I became so enchanted with the em dash, what I do know is that I spent much of my early years in college yearning to communicate in the style of my professors, the scholars we read in class, and my more advanced peers. Unconsciously or not, I learned to imitate the writing I encountered in class, in communications with instructors, in the many essays I was reading as a writing center tutor. Somewhere along the way, the em dash became an accessory to my academic performance.

In his seminal essay “Inventing the University,” David Bartholomae studies this phenomenon, exploring how student writers (basic writers in particular) “try on” academic discourses in an attempt to “both imagine and write from a position of privilege.” Bartholomae uses the metaphor of “inventing the university” to describe how student writers attempt to imagine, join, and participate in academic discourses that they do not truly belong to nor have full access to. As Bartholomae argues, every time a student enters a new course in a new and unfamiliar discipline, they must “appropriate (or be appropriated by) a specialized discourse, and they have to do this as though they were easily and comfortably one with their audience, as though they were members of the academy.”

To put it reductively, ChatGPT offers student writers a shortcut to “inventing the university.” ChatGPT and similar LLMs allow students to temporarily inhabit the position of more experienced writers who speak and communicate in what Bartholomae refers to as “the privileged language of university discourse.” While composition instructors know that AI-generated writing is riddled with hallucinations, cliches, and surface-level analyses, developing student writers aren’t always able to identify these issues. Instead, they simply recognize that the text generated by ChatGPT reads as more polished and professional than their own first drafts. When writing instructors understand that many students’ proclivity for generative AI tools emerges, in part, from a desire to “invent the university,” we can perhaps take more generous and productive approaches to guiding student writers towards more intentional and ethical AI usage.

So what can this look like in practice? The first step, I think, requires creating opportunities for students to talk openly and honestly about generative AI: how they use it, when and why they use it, what concerns them about it (because they do have many concerns), and how they understand its relationship to the writing process. I like Brett Vogelsinger’s suggestion of “The Transparency Survey” that invites students to share how they may have used AI throughout the process of creating an individual project. That said, despite our best efforts, students are often more honest with their peers than with their instructors, so it can be productive to build in time to allow students to have informal discussions among their classmates about how they use AI. In my intermediate and advanced writing courses, I have also assigned chapters from John Warner’s book, More Than Words: How to think About Writing in the Age of AI, and students seem particularly persuaded by Warner’s assertion that writing is both a form of thinking and a form of feeling.

Another reading I have regularly incorporated into my courses for a number of semesters now is Vauhini Vara’s personal essay “Ghosts,” in which the author uses generative AI to write through her grief surrounding her sister’s death. Through a series of vignettes, Vara prompts a chatbot with reflections on her memories of her sister and then allows it to “complete” the prompt, so that her writing exists alongside the AI-generated text. This essay complicates our evaluations of AI-generated text as many students observe that these portions of the essay “pass” as creative writing, at least on the surface. But many students express strong feelings about death and grief being uniquely human experiences and can identify the idiosyncratic details in Vara’s original writing that make it distinct. One student, for example, noted that the last paragraph in Vara’s essay shows how her sister, in a sense, “taught her how to live,” and made the connection that this is how many people use AI—by outsourcing their thinking, expressions of emotion, and interpersonal communications, AI is being used to avoid the uncomfortable but essential elements of human existence.

I am also developing activities for parsing out alongside students why AI-generated writing is often unsuccessful or insufficient. In other words, what makes “AI slop” so...sloppy? I’ve found that when students assess writing they know has been generated by AI, they respond in a few ways: either by pointing out how it “sounds good,” but is ultimately all surface, or by critiquing the prose for being boring, cliche, predictable, etc. But when asked to articulate what specifically isn’t working, they struggle—and I think that this is where the work of helping student writers recognize the human elements of writing begins. When I’ve received assignments that tend to repeat similar sentences or phrases that I highly suspect were generated by AI, I’ve copied them onto a single slide to show students not only how AI generated text lacks originality, but also to prompt them to think about how they are capable of communicating similar ideas in more nuanced ways. In examining these instances of AI-generated writing, I’ll ask questions such as: What’s a more interesting, exciting, and/or precise way to say this? How could this phrase more accurately capture what the writer is trying to communicate? How could this more effectively help the writer achieve their purpose? This sort of prompting helps students better understand why AI falls short, while also, hopefully, empowering them to recognize that they can generate prose that is more lively and inherently human. These sorts of exercises should help to demystify academic writing and show students that I don’t expect or want them to flawlessly “invent the university” in their papers.

As an English professor, I believe that most students, at the end of the day, prefer thinking for themselves rather than offloading intellectual labor to AI. When encountering student writing I highly suspect was generated using AI, I try to remind myself of Bartholomae’s essay and consider how subsequent conversations about AI usage can serve as teaching moments. This often isn’t a straightforward process, but I do believe that when we take the time to explore with students how and why we value process-based approaches to writing, they develop a better understanding of why original thought is so essential to this process. We cannot feed into the narrative that generative AI is throwing humanities disciplines into “crisis”; rather, it’s laying bare what our disciplines have always valued, and is underscoring why we must continue to value these things. And in the meantime, I’ll still be using my em dash—sparingly, of course.

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The Important Work is a space for writing instructors at all levels—high school, college, and beyond—to share reflections about teaching writing in the era of generative AI.

This week’s post is by Maggie Boyd. Maggie works as Assistant Director for Writing Support at Boston University, where she guides the writing assistance and presentation prep programs. Previously, Maggie taught in BU’s English Department and Writing Program. Her research examines contemporary narratives of healing and she loves reading physical books. You can find her on Substack and Twitter.

If you’re interested in sharing a reflection for The Important Work, you can find information here.—Jane Rosenzweig

I often find myself in conversations about AI that range from handwringing to praise-singing. It’s rarer that I get to participate in conversations that lead to practical ideas for curating AI literacy (like those at the Babson Tea Party!). But over the last year, I’ve been lucky to team up with several librarians at my university to develop an AI literacy framework that we have transformed into an assignment and a class activity. And it has been truly fun, as well as a much-needed reminder of how much I love the deeply collaborative and dare-I-say human work of educating, learning, and writing.

Over the course of our early conversations, it became clear that using AI is an expression of values. We therefore realized that we wanted our AI literacy aid to prompt our students to consider what they value in learning and how to honor those values. So, we decided to create a values-based framework that anyone could use to help guide those decisions. First, we needed to figure out which values might be relevant for AI literacy.

As Assistant Director for Writing Support, my work running a writing tutoring program centers on the premises that writing is a meaningful endeavor, empowering us to sharpen our thinking and share it with our community, and that the act of carefully, caringly parsing our writing with another person will benefit both the writing and the writer. As we considered our values, I kept wondering: What happens when our learners could shortcut the whole process and have a machine spit out what might seem like a final product? What happens when writers could press a button to receive feedback rather than trudging across campus or even opening a Zoom and spending 45 minutes in dialogue with a tutor? What happens if students miss out on opportunities to build their confidence and capacity to communicate?

Such questions border on the existential (what’s the value of human thought or skills?) as well as the practical (what will motivate students to make appointments?). It’s evident that AI is changing the writing ecosystem. What frameworks might serve us, as educators, to better guide students to navigate that ecosystem? Our framework offers one answer, tasking students to press pause before they enter a prompt into an AI platform and ask themselves: why am I doing this?

We didn’t initially have the idea of a “pause” in our minds. We all agreed that we aspired for students to be intentional and informed about their usage, with a clear sense of their motivations and potential outcomes. We discovered along the way that pausing was a useful framing to capture this goal, especially in contrast to the emphasis on acceleration pervading AI discourse. As more tech companies push us to outsource more of our cognitive tasks, flooding the market with promises of newer and better outputs, we consider ‘pausing’ an act of resistance.

As we imagined the form of our AI literacy framework, we reviewed examples that mostly resembled decision trees (see these examples from Edutopia, Oregon State University, Memorial University at Newfoundland, University of Alberta and University of Hawaiʻi). When we met to discuss, we realized that each nexus point represented a moment of possibility: What will you do next—and why? What do you want to accomplish? What technologies will you use? What infrastructure or information do you have? We also realized that a decision tree felt too linear; we couldn’t settle on a single starting point, as each nexus felt intertwined with the next. So, we wanted multiple entry points that would all overlap, more like a decision flower.

To determine which points we would include, we sorted values into broad categories that we each rated 1-5, with 1 as the most important (a sample can be found here). We added our ratings together to identify shared priorities and then used this value-assessment to decide which categories our framework would highlight.

This process proved humbling—I, for one, was surprised to find myself ranking copyright higher than equity—and fruitful. Reflecting on our own values and processes felt like we were doing the best thing we can do as educators who are responsible for guiding students to hone their own expertise: lead by example. We were thinking critically about available tools, weighing benefits against drawbacks, pruning our use cases and creating space to reflect.

From there, we felt ready to formulate our thinking into a framework that would center the seven considerations we had collected. After some experimentation, we ended up with our decision flower, “Pause Before You Prompt,” available in condensed and expanded versions at bit.ly/ai-pause.

With this framework, we aim for students to reflect on which considerations (or petals) most align with their values and start asking themselves questions like the ones we pose. By creating this opportunity for value-assessment, we hope to empower students to visualize how AI would or would not benefit their academic experience and what might be lost in the process. We want students to maintain agency over their learning, rather than treating AI tools flatly as exterior co-writers.

Soon after we started sharing our framework, we heard from faculty and staff that it propelled productive discussions in their classrooms and meetings. I was excited to try it out in my own context in the writing center, where I have been feeling the pressure to adapt to an AI landscape. I’m not the only one—in fact, a recent report on generative AI in writing centers, tellingly titled “The Burden of GenAI Has Increased, and the Mood Has Turned Sour,” showcased the widespread sense from writing center staff that our programs will need to adjust our practices to address AI. It hardly astonished me to see that many of us dedicated to students’ writing are seeing more work and less hope. But, with our framework in hand, I felt much more equipped to make one such adjustment and train our tutors on AI literacy.

To initiate dialogues around AI usage among our tutors, I integrated our framework into a tutor training activity. I distributed papers around the room, with each sheet dedicated to one “petal,” featuring the topic and the sub-questions. I gave each tutor sticky notes and tasked them with moving around the room, adding one note per sheet with either a strategy for addressing an existing sub-question or an additional sub-question that they would ask a student. I wanted to spark a conversation about how our tutors might shepherd students to be more intentional and informed when considering GenAI usage—and I think we succeeded. Tutors leapt into the discussion, sharing student questions from previous sessions (many now worry about using an em dash, for example), mentioning their own suggestions (reviewing the academic policies that require students to cite AI, for another example) and bonding over frustrations.

Under “Personal Motives,” the sticky notes read →

1. “Is it speed/efficiency? Desire not to do this work?”

2. “Why are you using chat - is it to make life faster? Are you nervous about the material? Do you feel like you have enough support?”

3. “AI has been shown to decrease cognitive function. What do you think of this? Will using AI for this task be meaningfully different from googling, using Grammarly, etc.?”

4. “Who benefits from your use of AI in this task?”

5. “You came to BU to learn skills like research + writing, which often means engaging in time-consuming thoughtful work”

6. “Is using AI helpful long-term?”

Under “Copyright,” the sticky notes read →

1. “Would you be upset if another person got access to your search and response?”

2. “Consider that by scraping other people’s writing for its own LLM, AI is already approaching intellectual property violation.”

3. “Are you reducing your future self’s creative agency in using this tool? What if you want to utilize this idea in the future? And how would you feel about the AI company using your idea?”

4. “Your writing is your intellectual property!”

5. “Is the information that AI is giving you someone else’s intellectual property? Do you know how to credit them?”

6. “Did you build any of your project with AI that got directly put in? Is any of it novel?”

Under “Accuracy,” the sticky notes read →

1. “Have you read your source? Did you do the analysis or chat?

2. “Go to the sources to confirm it exists.”

3. “What do you think about how this information is being framed?”

4. “Do you know where AI is getting its sources? Can you trace where this info is coming from in order to assess its accuracy, potential biases, etc.? Will AI tell you no?”

5. “Can we ask chat to also cite sources? Do you think you can decipher the truth / are you an expert?”

6. “Try asking your prof or a librarian for sources—this way you’ll know for sure the source is reliable and the info isn’t hallucinated”

   “Will AI tell you no?”

This sampling shows how the writing tutors are contemplating what AI allows and especially what it forecloses. They are full of questions, focused on the larger writing ecosystem: the other sources that AI trains on, the professors who will read this work, the future possibilities of a given project and student, etc. One asks a striking question: “Will AI tell you no?” If AI always gives you what you ask for, how will you learn what you don’t know to ask for, or what you don’t want to hear but would actually strengthen your work? Human collaborators can push back or re-orient or, we might say, press pause—an essential ingredient of thinking, researching and writing.

Whether a librarian, writing center admin or peer tutor, we are all feeling the pressure to be prepared to shepherd students to make smart decisions about if and how to use AI. Across early experiences, our framework has provided an engine for kindling that type of careful, critical work. Talk about generative.

You are welcome to use any of the materials shared here!

Collaborators on this project included Kristina Bush, Lucy Flamm, Natalie LoRusso, Jack Mulvaney and Eva Sclippa.

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The Important Work is a space for writing instructors at all levels—high school, college, and beyond—to share reflections about teaching writing in the era of generative AI.

This week’s post is by Brett Vogelsinger, a high school English teacher in Pennsylvania. Brett’s new book, Artful AI in Writing Instruction: A Human-Centered Approach to Artificial Intelligence in Grades 6-12 investigates how teachers can carefully weigh the inclusion of AI tools at different stages of the writing process. It includes interviews with teachers and students as well as use-tomorrow lessons to help guide classrooms through this new landscape to artful results and stronger writers. You can learn more about Brett’s work on his website and read his previous post for The Important Work here.

If you’re interested in sharing a reflection for The Important Work, you can find information here.—Jane Rosenzweig

At this point in the year, my students in AP Literature and Academic English 10 know several things about my stance on AI.

• I’m open to talking about any opinion on it.

• I write about it.

• I listen a lot.

• I use AI for some things and avoid it for others.

• I am transparent whenever I share something student-facing that includes any use of generative AI.

In short, I’m trying to model what it is to be cautiously curious, to engage in reflective reconnaissance, and to share what I learn along the way.

The moral reasoning my students and I do around this topic may be one of the most interesting things it has brought into my classroom—a place that is technology-heavy some days and entirely grounded in paper notebooks on others—and for this reason, I recently invited some honesty and feedback from my students.

In two assignments in two different courses, I neither expressly forbade the use of AI nor did I encourage it. But here’s what I did do:

1. Prior to the start of the writing project, we had an “Is It Cheating?” lesson, in which I demonstrated, live on the big screen, various uses of AI for an assignment similar to the one we would be working on. We reached consensus as a class about what uses clearly overstepped the bounds of academic integrity and what uses had potential to push our thinking. Ultimately, students left understanding that “using AI” can include a range of actions, and that even the Editor tool built into their MS Word program involves “use” of this technology.

2. During the writing process, we occasionally hearkened back to our “Is It Cheating” lesson with brief conversations that never sidetracked our important work—the thinking and writing—but illuminated possibly productive applications. An example: “You just read your draft aloud to a partner and highlighted any bumpy sentences. One of them is still giving you trouble. How would you feel about asking AI for three potential rewrites? You don’t have to use any of the suggested revisions in their entirety, but might seeing them open a new approach to the grammar of that gnarly sentence?”

3. Unannounced, at the end of the writing process, I gave a “Transparency Survey.” In AP Lit, this was online, and in Academic English 10 it was a small sheet of paper to attach to the printed papers they were turning in.

Here is what the survey looked like for my AP Literature students (you can click here for a copy you can download if you want to try it out):

One of my ongoing curiosities is whether students will speak candidly about their generative AI use when they know they are working with a teacher who approaches this technology with an open mind. I am curious about the nature of their use and how they feel about it.

I also want to emphasize to them that LLMs are optional and part of a broader spectrum of support for writers. Artificial intelligence replaces neither the writer nor the classroom community of writers.

I want to allow for the fact that students will sometimes use artificial intelligence in ways that did not feel good in the end, and they can admit that to me. I am learning that, like any kind of cheating, flagrant misuse of generative AI does not sit well with the conscience.

So in both surveys, I thought it was important for students to weigh in on whether or not their voice shines through in their work and give them the chance to talk to me more about it if they had an “uh-oh” epiphany about their work on turn-in day.

Almost every student I surveyed felt their finished work presented their own voice as writers.

Finally, on the AP Lit survey, I wanted feedback on whether this kind of transparency survey might be helpful in maintaining academic integrity. Of my AP Lit students, 85% feel that it would be helpful.

In a sample size this small, the numbers do not mean a whole lot. So let’s center student voice again and take a look at what students themselves had to say in their open-ended answers.

The first question on the survey asked them to select all options that described their use of AI. Here were the results based on 40 responses total:

After showing them the results, I asked if they felt that one of these situations has the potential to overstep more than the others. Without any names attached to these results projected up on the screen, they quickly identified “I used AI to help me draft my writing” as being potentially problematic. We slowed down and talked about the range of uses that could be referring to.

Students were also apprehensive about “I used AI to help me to read and understand the text,” pointing out that this could lead to cheating akin to only reading the CliffsNotes, and they’re right. However, some students had taken on epics like The Iliad or Inferno to read for this project, or Greek plays in verse like Prometheus Bound. At home, I had witnessed my own son recently use AI to help him scaffold his reading of Emerson’s dense prose for AP Lang and rather quickly pull that scaffolding away to keep reading on his own. I wondered whether any of my students had done the same.

So the next question on the survey mattered quite a bit: “Describe any reasoning for your decisions in Question #1. Tell me about the WHY behind your choices.”

On using AI to support reading, Elizabeth wrote:

I read Dante’s Inferno, and the translation was written in verse. I initially had a hard time adjusting to the style as it is drastically different from what I normally read. As a result, I had a harder time understanding the first couple of cantos, so I took what I thought I understood, brain dumped into AI, and asked if I understood it correctly. If I did, I would move on, if I didn’t, I would ask questions and reread the passage after. Around 1/3 of the way through the book I stopped using AI completely.

Mark, who used AI to support drafting had this to say:

I used AI to help draft ideas that I picked apart to help find the best ideas that I then used to spin off into my own thoughts to create my script for my presentation.

And then there was a whole range of other thinking across the class:

Alaina: After a few drafts of my conclusion, there were two sentences that I couldn’t find a clear wording for and I felt they were getting too convoluted for my presentation. I decided to ask AI to give me a few options for ways to rewrite them so I combined some of those ideas to make what I was trying to say more articulate.

Talia: Personally, I do not think AI usage is ethical, especially in a literature situation. . . Everything AI can tell me from a prompt I can learn from my own research.

Smaran: I always use the Microsoft Word Editor after every essay or project . . . Also, I used AI to try to find a scholarly source based on my thinking.

Ainslie: I’m not sure how to use AI in a way that maintains my thinking and voice and the integrity of the project. I also don’t see the [point of ] wasting that sort of energy and adding to my carbon footprint if I could use my brain.

Erin: I do not like AI and do not like using it in regard to my writing. I think it takes away the work of real writers and is harming us a lot more than it’s helping us.

Johan: I used the Google AI on my phone to look for different examples of Norse mythological influences in contemporary media.

Cassie: I didn’t use AI at all in this project. I don’t like using AI because it feels as though I am cheating myself out on my learning. This is an AP class; things are supposed to be difficult.

I want to pause here and marvel at the range of thinking and reasoning that came out of these two simple questions. I’ve been saying it for years now: Students are not a monolith in their opinions on AI!

And my job is NEVER to talk them into using it as part of their writing process. Rather, my job is to give them opportunities, instruction, and feedback that helps them to become skilled writers.

I also feel it is my job to help build their reasoning skills around AI, to be ruthlessly reflective, to know what boundaries they should never cross if they do choose to use AI in their writing process. Because if we do not do that, students are left to dive into this deep and perilous ocean of generated text on their own.

If students head to college without solid foundational writing skills and without adult guidance that has helped them hone their reasoning skills on artificial intelligence with opportunities to be transparent about their use, students are more likely to succumb to cheating with it when it comes to crunch time.

How does this kind of survey look with younger students, outside of an Advanced Placement environment?

At the end of a unit where we studied Cormac McCarthy’s The Road—perhaps the strangest novel most of the sophomore class had ever read—I presented my students with six different writing prompts on the day of their in-class assessment in an 83 minute block.

They could choose two prompts to veto, writing no response to them.

They could choose any three of the prompts to write about in short answer form, on paper, during the test period. This provided unvarnished thinking and interpretation from my students with no influence of AI.

Then, students needed to choose a fourth prompt from the list and start drafting their response in a handwritten rough draft. At the end of the period, they turned in the three handwritten short answer pieces and took home the start of the draft on their fourth one. That draft would return a week later stapled to a typed final copy of a 2+ page essay.

On turn-in day, each student also had to fill out the following form and staple it to their finished copy.

Like my advanced senior class, their responses were varied:

• 60% of the students said they used Editor within Microsoft Word and slightly fewer (55%) said they “used AI a bit.” Currently, the major LLMs are all blocked on their school laptops, so they would have done this on their personal devices.

• A few students made appointments at the Writing Center, and notably one of them expressed that he worked with the same tutor as he had previously because of a productive past experience. Human connections matter in our writing.

• Only about 50% of the students felt they wanted more time in class spent talking about when and why AI use is acceptable, and I wonder, is this because they feel I’ve given them enough direction, or because they do not want to uncover more reasons to restrain their use?

When I asked students to share how and why the used artificial intelligence, here are some of the responses I received:

Mason: I used the sentence builder and synonym word changer in Editor to use bigger and better words and make my writing sound professional.

Siena, a student who both visited our school’s Writing Center and used some AI: I used AI to help me with my thesis statement. It helped me determine if my thesis was good. I also used AI to help me find similar words I used a lot throughout my essay.

Callen, using a prompting strategy I demonstrated in class during the “Is It Cheating?” lesson: I used AI on only one of my sentences and I asked it to rephrase the words in the sentence to make it flow better. But I asked it to keep as many words as possible so it still sounded like my idea.

Perry: I had a bland title but I had a couple of good titles too and I asked it for its opinion. I’m never really good at conclusion sentences, so I had a good copy and asked it if the conclusion sounded good. I asked for its opinion.

Malika, a Russian-speaking student learning English: I used AI when I try to translate and my translation was wrong.

This sample shows the array of uses students felt were within the bounds of academic integrity.

Transparency can help us to craft a lesson for the next unit that builds their confidence in a way that prevents them from turning to AI. Transparency can also help students to interrogate their own process, engaging in valuable metacognition.

In the future, if my students know a survey like this is coming at the end of a writing project, they can think about the implicit boundaries these reflections create as they work through the plethora of decisions it takes to compose an essay.

Just like my AP Literature students, 100% of the Academic 10 students felt that their writing accurately represented their work and their own voice, and I mostly agree with that self-assessment.

Were their final essays somewhat more polished than the handwritten work from the first part of the test? Yes.

Did they show me evidence of their writing process and reflect on that process? Yes.

Did this process feel better than condemning all artificial intelligence usage and playing detective as I tried to grade their work? Most definitely, yes.

Let’s seize the moment in the evolution of writing and writing instruction to truly listen to students so that we can meet their needs. When something troubles us in the results of a transparency survey, let’s articulate to our classes what bothers us and why, modeling how to reason. And let’s recognize the innovative uses students discover that have not occurred to us yet, but get us talking and reasoning together as a class.

Dear reader, you may agree or disagree with students about whether their uses of AI are conducive to learning, but what I think we can all agree that asking students to open up about how and why they are using artificial intelligence illuminates more clearly what support we can give them in our very human work of writing instruction.

Like the act of writing itself, all of this is messy. But with transparency, some of it can be really rewarding too.

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The Important Work is a space for writing instructors at all levels—high school, college, and beyond—to share reflections about teaching writing in the era of generative AI.

This week’s post is by Helen Choi and Vance Ricks. Helen teaches writing to engineering students at USC Viterbi School of Engineering. You can read her recent piece about her experience attending the AI and Digital Literacy Institute at the University of Kansas here. You can find Helen on LinkedIn and Substack. Vance is a Teaching Professor of Philosophy and Computer Science at Northeastern University, where he teaches and writes on the ethics of emerging technologies. See an example here: https://issues.org/futuretensefiction/parasocial-relationships-privacy-vance-ricks/

If you’re interested in sharing a reflection for The Important Work, you can find information here. —Jane Rosenzweig

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In July, The Important Work published a post about Helen’s plans to make her engineering students read a print copy of a book—Karen Hao’s Empire of AI. Prof. Vance Ricks of Northeastern University saw the post, and as he too planned on assigning print copies of Empire of AI to students in a philosophy and computer science course in the fall, they connected and held a joint Zoom with their classes this semester. Both are assigning print copies of Empire of AI again next semester and organizing another joint Zoom discussion with their classes.

Readers of Helen’s original post were interested in knowing how things turned out, so we wanted to share how our semester went.

How did we integrate reading Empire of AI in our classes and why?

Vance: One of my courses is a Master’s-level course, “Responsible AI,”taught for a graduate program in AI Applications. The course studies how the design, development, and deployment of AI systems interact with important values such as fairness, privacy, transparency, autonomy, and well-being. It draws postgraduate students from many different disciplines, including health sciences, business, creative arts, engineering, and humanities. Ordinarily, the course runs for half a semester in an online, asynchronous format. This semester, I again taught the course in that format, but I am also teaching a (much smaller) separate, in-person, full-semester section. I was eager to take advantage of the different format for greater depth and a slower pace. One of my first thoughts about how to achieve that was to devote one course unit to reading an entire book together with the students. The subject matter of the course made Empire of AI a timely and natural choice: the book uses the story of OpenAI to give a clear-eyed and multidisciplinary perspective on generative AI systems and their philosophical, ecological, labor, and socioeconomic underpinnings. I decided to require the print/hardcover edition of the book (with exceptions for students needing a work-around). Those class meetings included regular student reflection on the multisensory experience of reading a print book juxtaposed with the abstraction of terms such as “the cloud” and “the algorithm.”

During the eight class meetings of that unit, students each had a portion of class time to focus our attention on a phrase or a passage from that day’s assignment chapter that they wanted to discuss in depth. Subsequent course units have asked the students to write analytical essays about some existing “responsible AI” system of their choice, explaining their characterization of “responsible” in those contexts and connecting their analyses to themes explored in Hao’s book and in other assigned course materials.

Helen: In my writing course for engineering undergraduates, I noticed a significant decline in engagement—both in terms of the course material and class participation—since we returned to in-person teaching after the COVID-19 pandemic. So, I decided to assign this long book in print to reduce our use of electronic devices and take advantage of being in the classroom again. I selected Empire of AI, which explores the development of large language models at OpenAI, because while engineering students might be prolific users of AI products, they don’t necessarily know about their limitations and social impacts. Additionally, as Jane Rosenzweig observes, the “thorny issues presented by AI“ provide smooth entry points for discussing questions of what makes us human and what kind of world we want to live in, which are questions that we address in my class.

For my five-week unit, we spent each class period with students leading the discussions of the day’s assigned chapters, followed by a writing reflection in notebooks. There were no reading quizzes, but there was a longer writing assignment at the end about what it means to be human and a critique of the concept of p(doom).

What did we learn from the experience of assigning a print copy of a book?

Vance: The main thing I learned is what we already know: instructors should do their best to explain all their course and assignment design decisions to their students. Two students in the course said that when they first saw my requirement that they buy the print edition of the book, they decided, “that must mean something.” I agreed! I spent the first several minutes of the first class meeting explaining what it meant. Part of what it meant was having some coursework that could be done without a digital device. Part of what it meant was recreating, if only in a small way, the experience of sitting together with others, focused on a theme or a passage (or even a personality) and not easily distracted by pings or other digital notifications or temptations. Part of it was having an integral artifact to take away from the course—unlike, say, a small pile of printed articles in Portable Document Format.

The students repeatedly said that having the book in print led them to read more carefully and less hurriedly than they often do. This isn’t the first time that I’ve assigned a print edition of a book in my courses, but it’s very unusual for me to assign one of this length. I worried initially about whether the assigned “chunks” of reading per class meeting were too large, but most students said that the book’s style and content made it easy—when they weren’t having strong reactions to what they were reading—to lose themselves in the book.

Helen: I was surprised to learn that some students found reading a print book to be a welcome break from their screens. One computer science student noted that because she spends more than eight hours a day in front of screens for lectures and homework, reading printed pages felt like a “treat.”

One of the best parts of the experience for me was the lack of open laptops in class since we only needed our books, pens, and journals. Being in a practically device-free setting without incessant digital intervention felt to me like a small victory for human agency. For students, learning in a relatively device-less environment may be an increasingly rare experience. As one student reflected, “[i]n this class, it’s just me talking and writing. There’s nowhere to hide, which was scary, but I did it.”

I also enjoyed reading and responding to students’ reflections; they used small notebooks to record their responses to the day’s prompt, and I’d collect them at the end of class to provide comments and return them at the start of the next class. Reading these notebooks was a highlight for me; there is a vulnerability and individuality in handwriting, doodles, and cross-outs that I didn’t know I missed! They helped me reset my mindset from viewing students merely as assignment submitters to fellow humans living in tumultuous times (who also submit assignments).

What should instructors consider when assigning print copies of books?

Vance: Don’t underestimate your students’ willingness to read a print edition of an entire book! And don’t overestimate their ability to do that in whatever ways you might assume that they can! Like everyone else, students will have a variety of attitudes to, and experiences of, reading in print. They’ll also have different experiences and expectations about discussing a book. I recommend explicitly raising both of those points, giving students the chance to reflect on themselves as readers and as discussants, and modeling the kinds of reading and discussing you want to see. That might be especially helpful for students who don’t read much nonfiction, but also for those who read only nonfiction.

I also suggest, as Helen does below, giving your students a tour of the book before you start discussing it as a class. Why is there an “Acknowledgements” section? What’s an epigraph? How important is it, really, to read the endnotes or footnotes?

Even if you want all students to have a print edition of the book, it will probably be impossible to have an entirely device-free experience of reading and discussing it. Some students with vision impairments might need an electronic edition of the book. Some non-native readers and speakers of English might prefer a narrated edition of the book (if there is one). Some students simply won’t be able to afford to purchase the book. It’s important to provide work-arounds and to think about how you might need to change your planning or assignments accordingly.

Helen: For students who might not have much experience with print books (or books in general), examining the book’s components—particularly the front matter—can be instructive. Students may be surprised to learn authors provide valuable background and explanations for why they wrote the book in the front matter, and that each component serves important functions. In the case of Empire of AI, the copyright page includes the standard legal provisions, as well as the statement: “No part of this book may be used ... for the purpose of training artificial intelligence technologies or systems.” This initiated discussions of why such a statement was included and the ethics of those who take creative work to train AI systems without the copyright holder’s consent. In one class, we discussed whether a reader who purchased Empire of AI would be violating Hao’s wishes if they put the book into ChatGPT to generate a summary. We agreed that this would be ill-advised!

What did our students talk about with each other during the Zoom?

Helen and Vance: After taking turns introducing themselves, our students talked about several aspects of Hao’s book. We were very pleased that almost every student spoke at least once! That was especially a relief to one instructor (Vance) who worried that Helen’s students, meeting hours earlier than usual, would be out of sorts. The two groups seemed to agree with the overall direction of Hao’s argument that large-scale AI systems can be seen as parts of an imperial project. However, they also raised some engaging and incisive questions. That included high-level questions about the book’s main themes; the role of a journalist; and the parsing of particular phrases and sentences in the book.

Are we glad that we made our students read a book?

Vance: I know that the question is a bit cheeky: I can’t make my students do very much! But I’m glad that the timeliness of the course themes (and of a book focused on OpenAI) led the students to choose to read it and to discuss it with curiosity and insight—not just with each other, but also with Helen’s students. The class discussions buzzed in ways that are precious and memorable. Next semester, I’ll assign the book—again, in print—in an undergraduate course on the ethical and social dimensions of emerging technologies, and Helen and I are making plans for another joint session. As I noted earlier, I’ve assigned entire books, in print, several times before, but the stakes of doing so in a course about “responsible” AI systems feel different.

Helen: Yes, I am! And I’m excited to do this again next semester; I feel much more connected to my students after having engaged with them on this book and reading their journals. I am a little bit sad that assigning a book felt so notable to me that I had to write about it; it shows how far I succumbed to using digital modes of teaching without much thought. But overall, I really felt a lot of joy teaching this unit. I was excited to come to class and chat about words with my students. When students would ask for more sticky notes to make annotations, I could hear myself responding a bit too loudly, “YES OF COURSE—WHAT COLORS DO YOU WANT—TAKE ALL THE COLORS!” I have not felt this energy in a classroom since 2022, and I can’t wait to feel that again in January.

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Recently a few people have reached out to ask if they are still subscribed to The Important Work because they haven’t seen a new post in a few weeks. You are still subscribed, and we are still here! This has been a busy semester, but we do have some very interesting posts in the works, including an update from on how reading a book with her engineering students turned out and a new post from .

If you’ve been thinking about proposing a post for The Important Work, now would be a great time to do so. You can share your proposal here. I’ve included some updated guidelines below.

In other news, and I have been working on some workshops for high school and middle school parents and teachers on how to think about AI in the classroom and at home. If this might be of interest to you/your school, please let us know.

Thank you for reading The Important Work!

The Important Work Submission Notes

• Pieces should be reflections on your work in the classroom. We won’t be publishing advertisements for AI products or pieces by people who are profiting from the technology they’re writing about. But, of course, we are interested in hearing what AI tools you’re using in your classroom where relevant.

• The question of “what is the important work?” is the guiding principle here, but your post doesn’t need to directly mention the question. The goal is for writers to be reflecting on this question from many different angles.

• Because this is a newsletter for those who assign writing/teach writing/think about student writing, we’d like to stay focused on what you’re thinking about/doing related to teaching rather than related to AI in more general terms (although there are plenty of places to publish those pieces as well).

• Pieces should be 1000-1500 words unless they need to be longer—then we can discuss.

• Your piece will be your own work and you’ll retain all rights to republish it (this will be spelled out in detail for you).

• We have no funding (at least for now) and aren’t planning to charge for this newsletter, so this is not a paying gig. I support all desires to be paid for your work, and I’m happy to consult with you about other venues (instead or in addition to this one) that do pay. The flip side: this is an informal opportunity to share your thoughts, and you don’t have to spend a month drafting your post.

Examples of what you might consider sharing:

• An assignment you have changed/created in the AI era and how it went/what you learned/what surprised you

• Why you have decided to bring generative AI into the classroom/not bring it into the classroom.

• A problem you had in your teaching that you think you’ve solved

• A reflection on a particular aspect of teaching writing in the age of AI—feedback, reading, brainstorming, research, drafting, etc.

• Reflection on something that has surprised you about how things have unfolded

• A question you have that you don’t think people are talking about enough

• An insight about how students are using generative AI that you don’t think has been given attention

• A concern you have and how you’re thinking it through

• A positive experience you’ve had and how it’s changing your teaching

• A narrative of what you’ve been doing differently

• What you want high school teachers to know about college writing/what you want college teachers to know about high school writing

• Many ideas I’m sure we haven’t thought of yet!
  
  

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