ASSAY: A JOURNAL OF NONFICTION STUDIES
11.2

Kelly Myers and Bruce Ballenger
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Essayism in the Age of AI
In 2022, when Generative AI burst onto the scene, writing instructors sounded the alarm. Some predicted that the technology would put an end to the academic essay. Others worried about an epidemic of cheating, and struggled to redesign writing assignments. In desperation, some colleges even tried to ban Generative AI altogether. The rest struggled to craft policies to guard against plagiarism.

It was at this historical moment that we began work on the 10th edition of The Curious Researcher, a textbook that focuses on research-based writing. At first, we shared our colleagues’ panic: how would we write a book about research-based writing during a moment when the very nature of research and writing–even fact–felt in flux? As we were working on the book, we met every Friday at a little cafe near the Boise State campus, and we always sat at the same table by the door. The setting remained the same, but each week it was like we were living in a new world. Whatever grounding we had in our understanding of authorship was suddenly unsettled and we had to find our footing.

Since then, rapid and shocking AI developments have only ramped up.  We grappled with how to address AI in the content of a textbook, only to learn in December 2024 that a UCLA literature professor was using an AI-generated textbook, a development that critics decry “takes the human out of the humanities” (Palmer, “The AI-Generated Textbook”). Now, in 2025, we’ve learned that the entire California State University system will become “the first AI-powered university system in the United States,” providing access to AI-powered tools to “460,000 students and 63,000 faculty and staff across all 23 campuses” (Palmer, “Tech Giants Partner”). Generative AI will continue to revolutionize instruction, and it will further complicate our understanding of authorship. The basic challenge is this: How do we keep student authors in the driver’s seat when it is expedient to turn the wheel over to Generative AI?

As we puzzled over this question, and our own limited expertise in the technology, it dawned on us that what The Curious Researcher has been arguing since its first edition—that students can best learn the spirit of academic inquiry by exploratory research—was exactly the kind of writing pedagogy that would subvert the power of Generative AI to take over students’ writing. This approach puts an explicit narrator in charge of exploring personally meaningful questions. It encourages writers to choose subjects not because they know what they think but because they want to find out what they think. What we’re describing is the essay—not the formal, thesis-driven essay that dominates instruction—but the exploratory, personal essay inspired by Montaigne. It is the kind of prose that AI cannot write and the kind of personal inquiry process that it cannot engage in.

Teachers and writers of creative nonfiction will certainly recognize our description of the essay here, but the exploratory essay is rarely a feature of other academic courses that emphasize  researched writing. The dominant model of the “research paper” has always been argumentative. There are good reasons for this, of course, including the fact that academic discourse thrives on argument, and it is a skill students should learn. But as an introduction to academic inquiry, the essay’s focus on personal discovery is an appealing alternative, particularly in the age of AI, which can generate conventional papers much more easily.

In what follows, we describe how inquiry-based pedagogy can work, especially in composition classes, but also in any courses in which students are asked to do research. Personal essayists will recognize the approach. Montaigne, the inventor of the Western essay, celebrated the power of self-knowledge, and once minted coins with the question that described his quest: What do I know?   This question is a perfect starting point for student writers because it inevitably leads them, as it did Montaigne, to explore the gaps in their own knowledge, and to see the pursuit of filling those gaps as much more than a rote intellectual exercise, but one that might have personal significance, and it assigns value to their personal experience as a source of knowledge.

This process is driven by questions, and in this essay we focus specifically on how to  craft promising questions that will sustain the research process and lead to new learning. Despite traditional taboos against first-person writing in academia, we argue that the emergence of Generative AI challenges writing teachers to return to Montaignian practices and re-engage the personal essay genre, a move that will tap students’ motivation to explore interesting subjects in search of fresh understanding.
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Though we were skeptical at first, we also began to see how Generative AI can assist in this process. We will describe some of those strategies, particularly how AI can help students explore subjects and find good questions without surrendering authority or compromising personal curiosity and growth.


The Capacity to Care

Beyond the use of first-person pronouns and personal opinions, “personal writing” and inquiry-based composition pedagogies focus on a student’s motivation, positioning personal motivation as central to writing and research processes. It’s helpful to return to Gordon Harvey’s description of this kind of motivation as “presence” in a piece of writing. We can detect “presence” in writing through “the wonder and even passion” that writers feel as they “pursue a topic through twists and turns” (650).  Generative AI tools simply do not have intrinsic, personal motivation. The technology can’t feel wonder and passion as it pursues a topic, or use wonder and passion to explore deeper layers of inquiry that are complicated by personal history and cultural constructs.
We can break it down even more by defining motivation as a student’s capacity to care. In his recent Chronicle of Higher Education article about project-based learning, Chad Raymond claims that “In the age of ChatGPT, faculty members have no choice but to adjust course design from a focus on ‘what’ to ‘why.’” When students are connected to the why, they are more intrinsically motivated. And, as Raymond explains, projects that “elicit emotional investment and original thinking from students [make] AI use less attractive” (“AI and the Case for Project-based Learning”). The capacity to care will not only distinguish a human author’s work from anything that Generative AI can produce, but when students tap into their intrinsic motivation, they can experience the limitations of the technology in new ways.

The question then becomes, how do we foster this kind of motivation, especially in required courses, but also any class that requires researched writing? One way is to invite students to write first-person narratives that draw directly on their interests and life experiences, but that is not the only way. For Eli Goldblatt a personal connection “doesn’t mean students must always write autobiographically, but they must learn how to find the motive spark, the intention to speak, within whatever subject they take up” (461). With that “motive spark” and “intention to speak” comes investment in the work and a sense of agency.

When a student has found the “spark,” they are “invested in a subject and freely directing the essay—not surrendering control to a discipline’s conventions, or to a party or to easy sentiments and structures, or to stock phrases.” On the other hand, “Without an urgency that is felt as personal, a writer will always be looking to the teacher, the boss, the arbiter for both permission to begin and approval to desist” (Goldblatt 461). It’s easy to see how students would default to external validation or Generative AI short-cuts without internal motivation driving their process.

There is new urgency to this idea of a “motive spark” because personal motivation can keep the student in the driver’s seat, and force Generative AI to ride shotgun. In particular, this idea of a “motive spark” draws our attention to the beginning of any writing project. Now, in the age of Generative AI tools, we must focus more intently on the starting point—the earliest moments of inquiry and the student’s initial decisions. That starting point is now a crossroad where students must reflect on their motivation: How can I care about this? If we provide them with avenues to care–including encouraging them to pursue investigations of personal significance—then they are less likely to surrender their authority to AI.

The truth is, all writers, novice and advanced, have starts and stops throughout any project. We get hit with writer’s block, we run out of time, we lose the thread of the piece. That means these crossroad-moments will recur, along with the temptation to hand the reins over to Generative AI. And in these moments, motivation matters, especially when we consider surrendering control to Generative AI. The polish and efficiency of Generative AI output is dazzling. So much so that, ironically, some college instructors now feel drawn to “imperfect” writing rather than the “sterile, bland, and airless” AI-generated prose their students hand in (“Teaching”). We must help our students learn to appreciate the motive spark over the dazzle.  But with limited knowledge of a subject, or limited time, or limited interest in the work, the dazzle of a quickly finished product becomes more and more appealing.
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The spark, on the other hand, is intrinsic. It’s the motivation that helps students see obstacles as opportunities for new learning rather than a set-up for failure. Spark is the entry point into the unique learning, growth, and expression that occurs through the process of writing and revising. The dazzle is general; the spark is personal. Dazzle is a flash; spark is a slow-burn.


Teaching Essayism

Motivating our students—helping them to ignite the flame of curiosity that inspires our own research—is at the heart of inquiry-based teaching. Our approach is to encourage students to adopt a new, and often unfamiliar identity as writers. When we ask them to become essayists we mean something quite particular, starting with introducing a new purpose for the writing project: an attempt to find out rather than to prove. One writes to discover what one thinks. This is often a radical shift for students, especially with research projects, which typically focus on reporting information or proving a thesis. This focus on personal discovery immediately shifts writers’ choices about what to write about. Rather than choosing topics or approaches because they already know what they want to say, writers choose subjects because they raise questions they want to know the answers to.

Because they have experience using a thesis to manage their encounters with sources, students may initially have a hard time using questions instead. Finding interesting, sustainable research questions requires that they suspend judgment and spend more time exploring a subject. Generative AI threatens to subvert this process because it short-circuits the process of discovery—and this is where many instructors balk at the use of AI, and rightly so. It’s like picking up a paint palette and 30-seconds later marveling at a painted canvas. Why would someone take the time to paint, stroke by stroke–especially if they are busy, anxious, and/or don’t enjoy painting? When even experienced writers struggle with exploratory writing, how do we convince students to do it, especially when AI makes it so easy to avoid?

What motivates our students is what motivates contemporary essayists: the promise of discovery, and the potential to unravel—perhaps only temporarily—some mystery that has meaning for them. The “I” of the essay puts them—rather than AI— in charge of this investigation. Essay writing also fundamentally shifts how writers see the information AI provides. Rather than always seeking to enlist sources to prove a point, essayists analyze information less as evidence than as exhibits that need interpreting: What new questions does this information raise? How does it connect with or complicate what I’m thinking at the moment? Do my own experiences help illuminate what this source is saying?

What do these researched essays look like? That can vary, but creative nonfiction offers great models, especially first-person accounts of writers who tell stories of how they come to understand something in which they have some personal stake. For example, after a trip to Bolivia, noted essayist Leslie Jamison feels a strange sensation in her ankle, and her doctors discover a worm embedded in her flesh. For weeks afterward, she’s haunted by the sense that there are more worms in her body, which in turn leads her to research Gorgollon’s disease, a controversial disorder in which people imagine things just under the skin—fibers or parasites—even though doctors find no evidence of them. Atlantic writer Faith Hill, in a romantic drought, explores the phenomenon of adult crushes. Are they beneficial or just pathetic?  For The Curious Researcher, Kelly wrote an essay about why, despite her academic accomplishments (among other things, she’s an Associate Dean), she sometimes still feels like a failure, a project that led her to an examination of imposter syndrome in higher education. And, after losing his beloved dog Ada, Bruce explored the topic of why people sometimes grieve more profoundly over the loss of a pet than a person.

Though they may not always be this directly personal, essays like these arise when writers embrace uncertainty and see questions as the way to steer through a sea of information. In a sense, when we ask students to write research essays we are asking them to look for “trouble”: What have they read, seen, or experienced that raise questions they can’t answer? Our students have written research essays like these (with citations and with or without the first person) on a range of topics, from sorting out whether Alcoholic Anonymous’ approach to labeling people forever alcoholics might undermine recovery to exploring the ways AI threatens to make a student’s Writing Studies major obsolete.

Generative AI can be enlisted to help students write essays like these or take an essayistic approach to any writing assignment (in other words, to stay in the driver’s seat), but first we must help students use AI differently than they use something like Google. Most of them are in the habit of using search engines to do a simple two-step: input keywords and then quickly harvest relevant output. We encourage them to do a more complicated dance. The initial input is the beginning of a conversation with the technology, one that proceeds using questions that drive writers toward what they want to know. Generative AI is a better writing assistant if we teach students how to follow questions with questions and carefully integrate reflection and analysis into the process of using the technology.
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To disrupt the input/output mentality, we must also name the larger tension that exists between product and process. Our students are often graded most heavily on the final product. As long as that’s the case, the pressure rests on what they produce, not how they produce it. An inquiry process is—by nature—messy and more difficult to assess, and as soon as grades are assigned to inquiry and exploration, we risk compromising the freedom and creativity of the work. One solution to these challenges is to focus on the work of question-making as a skillset that can be unpacked, taught, and assessed.


Enlisting AI in Sustained Inquiry

As we mentioned, the beginning of a research-based writing project presents a crossroad. Will the student’s curiosity drive the project, or will AI output lead the way? It’s not just a crossroads where the student makes a decision; it’s also a pedagogical crossroads where we have to teach students how to navigate toward (and then sustain) inquiry. As Ethan Mollick writes, “[W]e need to center teachers in the process of using AI, rather than just leaving AI to students” (“Post-apocalyptic Education”). One way to do that is to teach our students how to ask good questions from the very beginning of a research assignment. In the age of AI tools, that means how to input good questions into the technology and how to ask good questions of themselves as they analyze and reflect on the output.

Good inquiry questions, or more accurately sustainable inquiry questions, have some common qualities: they raise questions that don’t have simple answers, they are part of larger scholarly conversations, people have a stake in the answers, they can sustain the inquiry process over time, and most importantly, they matter to the researcher.

For undergraduates, the path to finding sustainable inquiry questions isn’t direct. Unlike scholars who have considerable domain knowledge on a subject, students begin knowing very little, especially if they take our advice to write about a topic because they don’t know what they think. Their exploration begins by orienting themselves to the subject with factual or definitional questions. For example, what is known about imposter syndrome? Who are some key theorists? What are some of the questions researchers have investigated? Generative AI—and Google, for that matter—can be very helpful with these kinds of questions.

Asking factual and definition questions are important opening moves, but they rarely lead to evaluation, analysis, or judgment; sustainable inquiry questions do because they focus on areas of dispute and uncertainty. We find that teaching how to craft questions like these is often the hardest thing to teach.

As they learn more about a topic, we encourage students to pay attention, as an essayist would, to what surprises them as they explore a subject. These are often “tell me more about ___” moments. For example, when Bruce was exploring pet bereavement, he became interested in the connection to attachment theory. In ChatGPT he entered the question: “can you tell me more about how attachment theory might apply to pet bereavement?” In response, the tool will produce lists and descriptions, and this is where the essayist mindset comes into play. In these moments, we need to encourage our students to read for resonance and follow their curiosity.

In response to the prompt “can you tell me more about how attachment theory might apply to pet bereavement,” Chat GPT produced a list of six ideas and descriptions. From that list, there was a delineation of types of attachment, including “anxious attachment” and the idea that people with an “anxious attachment style may experience more intense and prolonged grief due to their heightened sensitivity to separation and loss.” This idea caught Bruce’s attention. There was a spark here.

To move from spark to sustained inquiry, we need to keep asking questions. In some cases, once a student has found the spark, the questions will just start flowing. If not, Chat GPT can be called upon to help generate inquiry questions. For example: “ChatGPT, you’re writing an undergraduate essay that looks at how an anxious attachment style can explain why people grieve so deeply for lost pets. What might be some good research questions on that topic?”

 In this example, Chat GPT generated eight questions each with a short description, providing multiple options to analyze and explore. But here’s the tricky part for students: the goal is not to copy and paste something that “sounds good” from Chat GPT, or to succumb to the dazzle. After all, it sounds good, so why not use it? Instead, we remind students that essayists see moments like these as an opportunity to have a conversation with themselves, beginning with a simple question: what do I find interesting here–and why? We encourage students to explore this through writing.

Typical interactions with sources often end here. But since ChatGPT is ready to continue the conversation, we encourage students to ask follow-up questions (e.g. Is there scholarship on attachment theory and pet grief, and what are some of the most cited articles?). In this moment, there are so many ways students might assert some agency over the information, and it helps to enlist students in discussing this. For example, what kinds of follow-up questions might be most helpful?

What we’re describing here may sound like a ship that sets out to sea and never finds its dock. It is true that essaying asks writers to engage in extended exploration, and we think that this part of the writing process—the challenge of crafting sustainable inquiry questions—is more than worth the time. But of course, we’re asking students to emerge from their investigations with ideas about what they’ve learned, and to write about them compellingly.
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Writing teachers have a long-standing practice of moving students towards closure with two simple words: “so what?” When we ask the “so what” question, we directly engage the capacity to care (i.e. Why should the reader care about this topic? Why do you care?). The answer to the “so what” question brings the purpose of the writing to the surface, and especially in personal writing it challenges writers to reckon with the stake people other than themselves might have in their research questions.


Sparking New Knowledge

In 1997, the Boyer Commission described the “ecology of the university” as a place where there is a “deep and abiding understanding that inquiry, investigation and discovery are the heart of the enterprise” (18). The commission argued that especially in their first year, students should have “opportunities to learn through inquiry rather than simple transmission of knowledge.” Twenty-seven years later, as we grapple with the power of AI to manufacture driverless writing, inquiry-based pedagogies seem timelier than ever. And the researched essay, with its insistence that writers take the wheel, is well-suited to help them steer towards the happy accidents that can lead to new learning.

It might seem naive to think that students will choose the spark over the dazzle. But we’ve seen again and again how inquiry-based writing ignites students’ capacity to care. That deepens when they see that the “research paper” —a form of writing many students find rote and uninspiring—can be a vehicle for exploring questions with personal significance. And when students explore questions that have personal significance, that spark has the power to ignite new knowledge, questions, ideas. Generative AI synthesizes, but authors invent. In this new age of Generative AI tools, we need to empower our students to raise their voices, find their questions, and share their unique insights.
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We still meet in the cafe near campus, wrestling with the latest iterations of AI, teasing out their implications for writing instruction. This is still unstable terrain. As teachers it still rattles us. As essayists it is just the kind of trouble we’re looking for.
Click here to download a printable PDF with Works Cited. ​
Bruce Ballenger is an emeritus professor of English at Boise State University where he taught composition theory, inquiry, the essay tradition, and creative nonfiction. He is the author of seven books, including three textbooks in the Curious series—the Curious Writer, Curious Researcher, and Curious Reader. His personal essays have appeared in the Sun, Fourth Genre, River Teeth, and a range of other publications. Ballenger is a former Chair of the department and co-founded Boise State’s MFA in creative writing.

Kelly Myers is an Associate Professor of Writing Studies and the Associate Dean for Undergraduate Education and Student Success in the College of Arts and Sciences at Boise State. In the Department of Writing Studies, she teaches courses on writing, rhetoric, and revision. In her administrative work, she oversees advising, first-year experience, and student persistence and re-enrollment. Myers co-authored the sixth edition of The Curious Writer with Bruce Ballenger and Michelle Payne. Myers and Ballenger recently published the 10th edition of the The Curious Researcher. She lives in Boise with her husband, daughter, and dogs. 

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