A few weeks ago, I had the pleasure of being in the front row when Newbery Medal-winning author Kwame Alexander visited South Bend, Indiana. Alexander was on tour to promote his newest lyrical novel for young readers, Black Star, the second in “The Door of No Return” series. There were many engaged young people in the audience, and they dominated the question and answer period with verve. A few showered Alexander with praise for his novels, which taught them history they hadn’t learned in school. Some said he’d showed them how poetry can carry a story. One high-schooler asked how she could find a publisher for her novel; he invited her to chat with him after the event, and she beamed. And then came more tentative questions from the back of the room: “How do I become a better writer? And how can I figure out what to write?” Heads in the crowded auditorium swiveled in the direction of the speaker. I suspect many of us empathized with this brave young person who managed in several seconds to capture the core anxieties of most writers. Kwame Alexander smiled broadly, and, I like to think, said a version of what we all tell our students (and ourselves!) when we’re worried about our writing and paralyzed about what to write about. His advice: “Read a lot. I mean, really read a lot, and pay attention to what you like and don’t like.” He also said, “In order to be interesting as a writer, you’ve got to ask a lot of questions. In order to be interesting, you’ve got to be …”  And since he paused and I was in the front row, I supplied a word: “Interested.” It was not an original contribution, I know, but he gave me a dazzler of a smile, and repeated it to the crowd: “Yes! In order to be interesting, you have to be interested.” He seemed so pleased to be brainstorming with the mixed-age crowd about the hard work and satisfactions of writing. I wanted to bottle the fizz of the evening. At this stage of the semester, many of us are working with students who struggle to find something to write about. After all, one can write about anything, but to be worth the time of the writer — and reader — that pesky “So what?” question remains the gold standard. How many of us have sat with students who are simply stuck and “can’t think of anything to write about”? It’s tempting to flip Kwame Alexander’s point and say, “I can’t make you interesting if you’re not interested.” But that’s not very helpful. So, my co-author, Stuart Greene, and I offer a variety of actually helpful questions for students in Chapter Five of From Inquiry to Academic Writing, Fifth Edition. Channeling Kwame Alexander’s advice to be interested readers so that they can be interesting writers, we help students see themselves as participants in the scholarly conversations they read about, inviting them, with scaffolded models, to:             1) Draw on your personal experience             2) Identify what is open to dispute             3) Resist binary thinking             4) Build on and extend the ideas of others             5) Read to discover a writer’s frame             6) Consider the constraints of the situation. Like Kwame Alexander, I believe — and hope you do, too — that our students can develop into more interesting writers by learning to be more interested in the world. And that’s a habit of mind that pays dividends far beyond our classrooms. ... View more

  In a recent Chronicle of Higher Education piece, Marc Watkins shared an AI-Assisted Learning Template that he uses to encourage critical reflection and open disclosure of AI use on writing assignments. I find much to admire in his approach: he pushes students to consider assignment goals and the extent to which an AI tool helped them achieve those goals. In short, he asks them to reflect critically on their learning. And to the cynics who suggest that students will just use generative AI to compose these evaluations, Watkins admits the possibility. But he suggests most students are “eager for mechanisms to show how they used AI tools,” and he clearly wants to resist “adopting intrusive and often unreliable surveillance” in his classrooms.  I applaud the learning focus embedded in his assignment design, as well as his rejection of detection-obsessed pedagogy. But I still have concerns. First is the notion that we “teach ethical usage” primarily by ensuring students do not cheat. I think discussions of ethical AI use must include transparency in what we ask the AI to do and how we use the outputs, of course, but there are other ethical issues as well. In my current corequisite FYC course, for example, we are looking at how generative AI platforms were developed and trained: was labor (particularly of minority groups), privacy, or copyright abused in the process? Will my use of the AI expose my words or data in ways that I cannot control? If I want to use the tool to brainstorm for a few minutes, are there environment, worker, or privacy costs to that session? Is there a risk that my use will perpetuate biases or misinformation? Ultimately, is the AI the best tool for the task at hand? If not, is using it for the sake of convenience an ethically defensible choice? My students and I have also asked this question: do we have a right to know when we are interacting with synthetic (i.e., machine-generated) text or images? The AI-Assisted Learning Template could be modified to include some of these reflections, so that learning issues are coupled with broader ethical concerns. I have included a similar exercise with the AI-themed assignments in my FYC course this term. But another issue lingers for me: how much reflection will actually occur in assigned self-evaluations? McGarr & O’Gallchóir (2020), Thomas and Liu (2012), Ross (2011; 2014), and Wharton (2012) are just some of the researchers who have suggested that assigned (and assessed) reflections are inherently performative: students may be just as much focused on managing their perceived image through skillful self-positioning as engaging in deep reflection. Students want to position themselves positively, as those who made a good effort and determined to learn something—deploying what Thomas and Liu (2012) call “sunshining” to characterize their efforts, challenges, potential failures, and ultimate progress.  My own research in the language of student reflections suggests that students make linguistic choices that distance them from agency over decisions and outcomes. They also foreground what is perceived as desirable—effort, open-mindedness, resourcefulness, and willingness to learn or grow. Many find it hard to acknowledge uncertainty about what they learned in a given assignment; after all, doing so might be perceived as criticism of the assignment or failure to learn—and engaging in either one is risky for students. But learning insights don’t always arrive on schedule, packaged neatly prior for submission by the due date. Still, my students have been trained since middle school to assert with confidence (and deferential gratitude to the teacher who provided the opportunity) that they have indeed learned, just as they were supposed to. Check that box. I am not suggesting that the template is without value or that reflective writing needs to be scrapped altogether. FYC certainly has a rich history of reflection-infused pedagogy that cannot be ignored. But as we adopt (and adapt) technology, tools, and templates, let’s consider how to ensure that students are empowered to question such technology, tools, and templates—promoting honest and authentic reflection insofar as that is possible.  How do we do that? I don’t have all the answers, but I agree with what Marc Watkins says: we should “shift focus away from technology as a solution and invest in human capital.” Ultimately, he says, he should honor the “irreplaceable value of human-centered education.” Exactly. Thanks to Marc for opening space for this conversation.   Photo by Solen Feyissa on Unsplash ... View more

This series of posts is adapted from a keynote address given by Stuart A. Selber for the Teaching Technical Communication and Artificial Intelligence Symposium on March 20, 2024 hosted by Utah Valley University.   This is the second post in a series exploring five tenets of a Manifesto for Technical Communication Programs: AI is evolutionary, not revolutionary AI both solves and creates problems  < You are here AI shifts, rather than saves, time and money AI provides mathematical solutions to human communication problems AI requires students to know more, not less, about technical communication My touchstone for this tenet of the Manifesto is what John Carroll and Mary Beth Rosson (1992) called the “task-artifact cycle,” which posits that each new technology solution entails a new set of problems, and further design and invention. Some of the problems created by AI are certainly well documented. AI can generate inaccurate information, make up information (hallucinate), automate work that requires human judgment, neglect current events, reinforce bias and discrimination, and spread political disinformation. AI companies are working hard to address these problems, but their solutions can spawn other or additional problems. Consider what happened with the Gemini platform from Google. In response to concerns about perpetuating bias and discrimination, Google retrained its Gemini robot to be more sensitive to diversity issues, but a result was racially diverse images of Nazi soldiers and the Founding Fathers. The task-artifact cycle can be illustrated at nearly every twist and turn. A good example of how this cycle has played out previously is templates, such as the templates in Microsoft Word for technical communication documents and in WordPress for websites. And it applies to AI too. After all, much of the content generated by AI is templated, reflecting conventional understandings represented as predictive patterns in a large corpus of texts. The field has done a good bit of research on the use of templates, showing what they buy writers and outlining problems for both writing and education (see Arola, 2010; Gallagher, 2019, chapter 2). What templates buy writers is a mechanism for embodying genres, foregrounding document structures, enforcing consistency, and supporting collaboration. Templates also aid invention by signaling design dimensions of conventional genre elements. But a new problem, especially in schools, is how to account for the template in a technical communication project. Phrased as a question, How much of the template do I have to revise to make my project original enough to warrant individual academic credit? My students often ask this question, and it is easy to see why. (For student-facing guidance on templates and the use of AI, see the new edition of Technical Communication, available this fall.) And it is a pretty safe bet that students will be asking this very question about website designs generated by AI, code generated by AI, and content generated by AI, especially because users typically own the copyright to AI output, at least according to AI companies. In fact, a question in the courts right now is whether robots can even own copyrights because US law states that copyrights only protect human-made work; this is not the case globally, by the way. We will have to see what ends up being the tipping point here. If a student revises 51% of an image generated by AI, is that enough of an intervention for them to be able to claim that the image is a human-made work? Time will tell. Plagiarism has certainly received a lot of attention in schools, and AI promises to exacerbate plagiarism problems unless they are accommodated thoughtfully. By plagiarism, I of course mean cheating, in which students knowingly hand in the work of a robot as their own. But I also mean the more interesting cases of so-called inadvertent plagiarism, in which students do not really know how to incorporate the work of a robot into their writing and communication. Teachers themselves are struggling with how to think about AI-generated content as source material for technical communication students. I already mentioned that one complicating factor has to do with copyright. In ChatGPT, for instance, users own the copyright to both the input, their prompts, and the output, robot responses. This makes sense in that AI companies like OpenAI want users to feel free to use the content produced by their robots. In most cases—again, at least according to AI companies—students may very well own the copyright to what an AI program produces for them. But this is an unsettled legal distinction, not a programmatic, pedagogical, or even ethical distinction. Just because students own the copyright to a text does not mean they can use it in just any situation. And this has been true historically. For example, most if not all technical communication programs do not permit students to reuse work to earn credit more than once without permission from the current teacher. There are limits to what copyright ownership buys students in technical communication programs. Plagiarism is obviously an old problem, but there does seem to be something qualitatively different when AI is involved. To prove academic misconduct, a usual part of the process has been to find the plagiarized texts. To find the plagiarized texts, teachers often use the very technology students used to cheat in the first place: Google—or another search engine. With AI, however, there are no plagiarized texts to find. Although some studies have concluded that robots themselves can plagiarize, the issue here has to do with what counts as proof for an academic integrity committee in a college or university setting. This question of evidence is a new problem that many teachers and program directors are now struggling to overcome. I hesitate to say much more about plagiarism, especially the cheating version, because it has received oversized attention in schools, forcing many of us to respond in kind. But the issues around integrating AI as a writing resource are worth our time and attention. To foreshadow my final overall point in the last post in this series (and some of the new coverage in the latest edition of Technical Communication): What, exactly, is the “where” of AI? Where should students be able to use it as a writing resources in their academic work? And how should they acknowledge that use? Anticipating the task-artifact cycle, it will be interesting to see what sorts of problems are spawned by our answers.   References Arola, Kristin L. 2010. “The Design of Web 2.0: The Rise of the Template, The Fall of Design.” Computers and Composition 27 (1): 4-14. Carroll, John M., and Mary Beth Rosson. 1992. “Getting around the Task-Artifact Cycle: How to Make Claims and Design by Scenario.” ACM Transactions on Information Systems 10 (2): 181-212. Gallagher, John R. 2019. Update Culture and the Afterlife of Digital Writing. Boulder: University Press of Colorado. ... View more

The polarization in American society today, and the way in which it is reflected in our popular culture, is a fundamental focus of the now-available 11th edition of Signs of Life in the U.S.A. And so I'd like to inaugurate my return to the Bits blog with a look at how political passion can pop up in some of the weirdest places, causing controversy in the most apparently innocuous circumstances. Allow me to explain. As I have noted a number of times in my years of blogging here, I participate in an online hobby forum (no, it doesn't involve firearms) where many of the participants are quite conservative. In fact, that's the main reason I still visit the site: it provides me with a glimpse into a world that tends to fly under the radar of most mainstream media coverage, and which is generally invisible to most cultural analysts. My participation on the site thus offers me insights into what is going on in places outside the ordinary academic orbit, and I am sometimes mystified at first by what I see there because I do not know the codes behind a lot of the comments there.     For example, in the run-up to Super Bowl LVIII there was a discussion on the site about who was going to win the game. That, of course, is only natural. A number of the comments, however, explicitly said, "I'm for any team except the Kansas City Chiefs." That was a bit odd. I mean, what's wrong with the Kansas City Chiefs? Perhaps, I thought, the hostility might be due to the Chiefs' recent domination of the Super Bowl (a sort of "damn Yankees" reaction), but I also noticed an obsessive focus on Kansas City Tight End Travis Kelce and how much attention he was getting. That puzzled me until it became clear that the objection was to Kelce's relationship with Taylor Swift and all the attention the two of them were getting. But that puzzled me too, because I thought that the kind of people who participate on the forum would adore this heartland-of-America team and the wholesome young couple at the center of it. So I was faced with a perfect opening for a semiotic analysis, one that begins with a question ("what is going on here?"), rather than a conclusion. And to answer that question I had to do some research.  Now, I am sure that many of you reading this blog will already know the answer to my question because all-things-Swift are well known to just about everyone. Except me. But I learned a lot in my research, including the fact that Taylor Swift is known to have voted for Joe Biden in 2020 and has generally indicated an inclination towards liberal political positions. Of course, I've also learned that long before Swift finally did announce her endorsement of Kamala Harris, her support was eagerly sought by Democrats and Republicans alike, though it was assumed that she would probably lean blue. So the explanation for all the conservative fuss about the Kansas City Chiefs on the web site is a simple one: the Chiefs had become Taylor Swift's team, which meant, in the code, that the Chiefs were anti-MAGA, which is why some Republican voters are now repudiating their Swiftie pasts. But the obviousness of the conclusion only becomes apparent when you can crack the code. As various news outlets like to say after things like presidential debates, there are five takeaways from this blog's analysis: Political polarization has infiltrated every nook and cranny of American popular culture. This polarization is often expressed in insiders' codes, which can be decoded by situating their signs in larger systems of associated phenomena that reveal what is really being said. A semiotic analysis best begins with a question – what is going on here? – rather than with an opinion or a pre-formed conclusion. One's own political views are not a part of a semiotic analysis. Taylor Swift is occupying a great deal of mental real estate, and not only among her fans – which says a great deal about the power of popular culture.   Image courtesy of IHeartRadioCA via Wikimedia Commons ... View more

This series of posts is adapted from a keynote address given by Stuart A. Selber for the Teaching Technical Communication and Artificial Intelligence Symposium on March 20, 2024 hosted by Utah Valley University.   As we kick off the 2024 fall term, I want to offer something of a conceptual manifesto for how to think about artificial intelligence (AI) in the context of technical communication programs. I hope to provoke pedagogical and programmatic initiatives that are both productive for our students and responsible to our field. The manifesto includes five tenets, each of which will be explored in its own post: AI is evolutionary, not revolutionary AI both solves and creates problems AI shifts, rather than saves, time and money AI provides mathematical solutions to human communication problems AI requires students to know more, not less, about technical communication Teachers can use these tenets as talking points for their students and to frame curricular developments and revisions in their courses and programs. Which leads me to our focus for this week. Tenet # 1: AI is evolutionary, not revolutionary When I was working on my dissertation in the early 1990s at Michigan Technological University, hypertext was all the rage, and many scholars in our field considered it to be a revolutionary technology that promised to suddenly change the nature of textuality in central ways. I am thinking especially of scholars in the groundbreaking collections edited by Edward Barrett (1988, 1989) and Paul Delany and George Landow (1991). But what hypertext really offered us was a platform for enacting postmodern theories of writing and reading that were at least two decades old. In this respect, hypertext was more evolutionary than revolutionary in nature. Historian of science Michael Mahoney (1996) has argued quite convincingly that “Nothing is entirely new, especially in matters of scientific technology. Innovation is incremental, and what already exists determines to a large extent what can be created” (773). We see this reality in AI itself: How can an AI chatbot generate anything entirely new when its training data comes from the historical past? Technical communication teachers and program directors have managed to domesticate everything from microcomputers and mobile devices to production and communication platforms to course-management systems and the internet of things. We will also learn how to domesticate AI for our purposes and contexts. Historically, a popular approach to the curricular integration of technology has been to “forget technology and remember literacy,” to reference what my dissertation director Cynthia Selfe (1988) wrote in the late 1980s. What continues to be powerful about this sentiment is that it reminds us that what we already know about teaching and learning will go a long way toward helping us address artificial intelligence. This is why the AI position statement from the Association for Writing Across the Curriculum re-affirms best practices grounded in decades of writing research. So too does the AI position statement from the MLA-CCCC Joint Task Force on Writing and AI. There is a trap, however, and that is relying too heavily on one-way literacy models as a foundation for AI initiatives. Many people simply transfer their existing assumptions, goals, and practices into AI contexts. Although it is comfortable and sensible to begin with current ways of knowing and working, such an approach is ultimately limiting because it is non-dialogic: Not only does the model assume that AI is neutral, but it fails to recognize that AI can encourage us to reconsider taken-for-granted assumptions, goals, and practices. So, in addition to addressing the possibilities and problems of AI, we should also see this liminal moment as an opportunity to revisit the status quo and consider how AI might encourage us to reinvent certain aspects of the field, including writing processes and the roles and responsibilities of technical communicators. On the broadest level, one of the more valuable aspects of AI might end up being that it can defamiliarize the familiar, as sociologist Zygmunt Bauman (2005) might put it, at least for the foreseeable future, so that we can look anew with fresh eyes at how we construct our professional world.   References Barrett, Edward, ed. 1988. Text, Context, Hypertext: Writing with and for the Computer. Cambridge: MIT Press. Barrett, Edward, ed. 1989. The Society of Text: Hypertext, Hypermedia, and the Social Construction of Information. Cambridge: MIT Press. Bauman, Zygmunt. 2005. Liquid Life. Cambridge: Polity Press. Delany, Paul, and George P. Landow, eds. 1991. Hypermedia and Literary Studies. Cambridge: MIT Press. Mahoney, Michael S. 1996. “Issues in the History of Computing.” In History of Programming Languages, Volume 2, edited by Thomas J. Bergin and Richard G. Gibson, 772-81. Reading: Addison-Wesley Professional. Selfe, Cynthia L. 1988. “The Humanization of Computers: Forget Technology, Remember Literacy.” The English Journal 77 (6): 69-71. ... View more

Commencement and award times in 2024 are, without doubt, a season of discontent, to say the very least. So many campuses turned in on themselves, as students, faculty, and staff raise protests, sometimes violent, against Israeli bombings or against anti-semitism, or both. Cancellations of commencement ceremonies–what’s to celebrate?    And yet. As always, there are some signs of spring and pockets of hope. If we look for them. If we find and share them. And while students on my home campus have mounted significant protests, demanding changes in University investment policies, they are also completing this spring term and participating in one of my favorite spring rituals: annual writing awards. There’s an award for first-year writing and rhetoric, one for second-year writing and rhetoric, and one for writing across the disciplines—and I look forward every year to listening to presentations and reading winning essays, always so happy to celebrate student ingenuity and creativity. This year's second-year awards for oral presentation of research go to students who have written and presented on subjects as diverse and fascinating as "What Does It Mean to Create Alongside Technology?," "Outside the World: Community, Culture, and Utopian Ideas in Antarctic Stations," "How Sex and Age Influence Medical Treatment," "Securing Farmworkers' Futures in the Age of AI," and "Citizen Science, Environmental Justice, and Lithium Mining." I was fortunate to join this year’s celebration via Zoom just two days ago where I got to hear six students describe their research and talk about what the process of investigation and presentation had meant to them. In every case, they commented on the privilege of being able to “dig deep” into a subject they felt passionate about as well as the chance to learn to communicate through way more than words alone. As always, I wanted to hear all their presentations and to sit and talk with each one of them.  So here near the end of the 2023-24 school year, and amidst upheaval and daily horrors, I hope that you are finding small moments of joy and connection. I am grateful for such moments, and for all teachers and students of writing, everywhere. As summer nears, I am going to take a break from this beloved blog to put my big girl pants on and tackle revisions of my books, including Easy Writer and Everything’s an Argument. So it’s nose-to-the-grindstone time!  I wish you—and our poor world—a rejuvenating summer season. And peace.    "graduation caps" by j.o.h.n. walker is licensed under CC BY 2.0. ... View more

Letter to My Students: Spring 2024 Edition Memory Plaques Neurodivergent Teaching Dear Students, The last full week of spring semester classes began with a sunny day, warm enough for summer. The quad, with its blossoming trees in white and pink, called my heart. I taught the lesson projected on the screen. You wrote for a while. Then we took a walking tour of the memory plaques around our campus. Initially I had scheduled the field trip for our penultimate class meeting, but storms were predicted for that day. We agreed to take advantage of the pleasant weather.  When my fall semester first-year writing class took this tour, we focused mostly on Mississippi Freedom Summer, 1964, and the three Civil Rights workers murdered by the KKK: James Chaney, Andew Goodman, and Michael Schwerner. The clocktower on our campus is named after them. Andrew Goodman was a student at our college who volunteered to take part in Freedom Summer. There are other memory plaques on our campus that you asked to see, two for 9/11/2001, and one for lives lost in our community to Covid-19. The Covid-19 memory plaque is slate gray with yellow letters framed in yellow rectangles. Under the letters there is a slate gray heart outlined in yellow.  Covid-19 Memorial Plaque, Queens College, City University of New York Photo by Susan Bernstein, October 17, 2023   I suggested that I would write reflection prompts that would allow you to connect this field trip to the final writing project. Then the clock tower chimed for 12 noon and we offered a moment of silence. Class had run overtime, and soon afterward all of us dispersed in the sunlight.   PROMPTS  As promised, please consider the following prompts for reflection: What did you learn from our field trip to visit the memory plaques on campus? In your opinion, what do you think is the intended purpose of the memory plaques? Do you think the plaques serve their intended purpose? Why or why not?  If you were to create your own memory plaque or other remembrance for Spring Semester 2024, what memory would you choose? What would your remembrance look like? Where would your remembrance be located? What shape or space would your remembrance take? Why? ... View more

Jennifer Duncan has been teaching English for twenty years, first at Chattanooga State Community College in Tennessee and now at Georgia State University’s Perimeter College in Atlanta, GA where she is Associate Professor of English.  For the last ten years, she has focused exclusively on online teaching concentrating on creating authentic and meaningful learning opportunities for students in composition and literature courses.   In the Doctor Who episode “Silence in the Library,” the Doctor and his companion, Donna, visit the Library, a planet containing printed copies of every book ever written in all of time and space.  AI Image Generated by Craiyon.com Unfortunately, when they arrive, the Library is entirely empty—except, of course, for the intergalactic book worms—Vashta Nerada—who have lived in the books for generations and which now awaken to eat anyone who visits the library. There’s also a second threat from the Library itself which “saves” people by uploading them into information nodes—robotic library assistants who wear the faces of those they have consumed and offer vague answers to questions which require lots of follow ups from the Doctor to offer any real information. Essentially, this is how GenAI tools like ChatGPT work. They were created by scanning vast amounts of online information indiscriminately, so, while containing lots of knowledge, they also contain both threats faced by the Doctor. In this case, Vashta Nerada serve as my metaphor for the bias sleeping in our texts throughout history. Why is ChatGPT so biased? Because it’s reflecting back to us the bias that was always hidden in our texts—in the gender stereotypes of textbooks, in the disproportionate representations of minorities in police reports, in the negative biases that have always been around for us to ignore or claim that they represent only a fringe point of view. The GenAI data collector consumed the mass of texts available to it and reflected those biases back to us. Like those information nodes, it shows us our own weakness by way of vague answers that require us to ask the follow up questions, expose the bias, and find what we really want which is, hopefully, the best of us instead of the worst. It's like a child who learned to read and was then given access to everything in a vast library with no assistance, no direction, and no guardrails to evaluate or understand what they consumed. When my son was born, we were residents of Tennessee, so he was automatically enrolled in Dolly Parton’s Imagination Library. Every month, we received a new book in the mail, and they were great books! From The Little Engine that Could to Look Out Kindergarten, Here I Come! these carefully curated book lists reach children as they develop intellectually and guide them as they develop their worldview. Kids learn to rely on friends in Kitty Up, that every creature needs rest in Panda Whispers, and that mom needs a minute in Llama Llama Red Pajama! So where does this tale of two libraries leave us? Like the Doctor and Donna confronting hidden dangers in the Library, we must confront the biases ingrained within the texts and systems that shape our culture and our digital landscape. But like Dolly’s Imagination Library, we have the opportunity to curate our students’ digital literacy skills, guiding them toward a better understanding of the flaws in our culture and in GenAI technologies. It might even be possible to cultivate a generation of discerning learners capable of embracing the complexities and contradictions of our modern age, much like the Doctor traveling through time and space armed with nothing more than curiosity, optimism, and a commitment to truth. The challenge seems impossible—especially without a sonic screwdriver—but it’s worth the effort. For in the words of the Doctor, “The universe is big, it’s vast, and complicated, and ridiculous. And sometimes, very rarely, impossible things just happen and we call them miracles.” ... View more

It’s probably no coincidence that a lot of folks are talking about the importance of and need for conversation today. News feeds are full of shouting matches, scenes of verbal attack and counterattack; everyone seems to be talking and no one seems to be listening. Conversation? Not likely or even possible, some say.  Into this scene steps conservative columnist David Brooks with a book called How to Know a Person. Brooks says he has spent a lot of his life taking and advocating for positions with little regard for what others think or say. it has taken him years, he realizes, to learn to listen with genuine curiosity to others, especially those he doesn’t agree with. In short, he was engaging in one-way talk, rather than two-way conversation. And that got him questioning his own modes of communication.   David Brooks in conversation at LBJ Library in 2022   Extensive research for his book eventually led him to identify two levels to any conversation: the first layer is the subject—what the participants are literally talking about. The second layer, which he calls the “underconversation,” is the “flow of emotion” going between the people talking. That second layer, Brooks argues, is very important: is it making the speakers feel safe? Uneasy or. unsafe? Angry? Listened to and respected? Or not? Paying attention to the underconversation led Brooks to ask different questions: not “what do you think about X or Y?” but to keep opening doors by saying “Tell me more. What am I missing? Tell me more.”  Even more recently, Pulitzer Prize-winning investigative journalist Charles Duhigg takes a close look at the need for productive conversations in his Supercommunicators: How to Unlock the Secret Language of Connection. Like Brooks, Duhigg has done a lot of research, interviewing people all over the country and working up what amount to a series of case studies on how effective or “super” communicators manage to be able to “connect to almost anyone.” This research taught him that a key to success is, first and foremost, understanding what kind of conversation you are having: one focused on practical issues (what is this conversation really about?), emotional issues (how does each participant feel?), or identity issues (who are we and how are identities silently shaping the conversation?). The rest of the book focuses on what Duhigg calls “learning conversations,” which I think of as deeply rhetorical, and shows how working through the four “rules” his research found at work in the powerfully effective communicative practices lead to understanding (of both self and others) and learning. Here are the four rules, simple sounding but hard to live by: Pay really close attention to the kind of conversation you are having Share your goals—and invite others to share theirs as well Inquire about how others feel, and share. Our feelings too. Explore if and how identities are central or important to the conversation In case after case, Duhigg shows how listening without judgment, sharing feelings, and identifying common ground can lead to productive conversations and sometimes (!) to changed behavior. In chapter 6, titled “Our Social Identities Shape Our Worlds,” Duhigg follows Dr. Jay Rosenbloom as he conducts numerous “well baby exams” and talks with parent about vaccinations, quickly learning that some parents are eager for such immunizations while others reject them outright. Rosenbloom does his best to give good advice but generally defers to parental wishes—until Covid. As the pandemic spreads, Dr. Rosenbloom becomes more and more frantic about the number of lives being lost land by the number of patients who refuse vaccination. When he asks a senior colleague for advice, that doctor says “tell them you’re the doctor and you know best.” Predictably, this tactic didn’t work, and often just alienated patients and infuriated doctors. You can check out the chapter to read about how Rosenbloom learned to talk with patients, sharing values and personal stories, listening to what part of their identities are at play—and eventually finding ways to connect—and sometimes to change minds and to save lives. The potential and power of conversation. Writing classes often rest on a conversational foundation, though usually more implicitly than explicitly. What I’ve been thinking about since reading these books is how I might bring what Brooks called the “underconversation” into focus in my classroom and then how we could use Duhigg’s three kinds of conversations and his four rules to guide us in classroom talk—especially on topics that are uncomfortable, that trigger strong emotions, and that are often skipped over or ignored because they are “:just too much.” The way to begin, I’ve found, is by having a conversation about conversation, one in which we sketch out what’s in it for us as a class (and as individuals) to learn how to connect to othe\rs, and to learn in the process.   Image via Wikimedia Commons ... View more

This weekend, I reached out to check on a student who had received some strident feedback from me. Thanks to specification grading, the student had earned the grade she desired on the final project of the advanced course.  For this project, she had constructed an argument in support of generative AI in ESOL pedagogy.  The project, however, was not intended to be an argument, but rather a literature review and exploration.  I address genre-mismatch in the project development schedule.  In this case, however, I did not anticipate my own response to the framing of the student’s argument:  I disagreed with every point in the essay.  The cherry-picked evidence was thin (at best), and the language of the essay unfairly positioned AI-skeptics like myself.  My feedback totaled 870 words—longer than this blog.  And as I reviewed it, I heard my own frustration.  There was little (if any) mitigation in my comments: It's a false choice.  As I said earlier, my concern has absolutely nothing to do with whether or not students cheat  with it. Wait—there are a couple of studies.  That's not enough to make this sweeping generalization. There are plenty of tools with games and quizzes—even digital ones—that don't rely on generative AI. I disagree.  My tone was clear. Recently, our writing fellows group collaborated with me to research how linguistic choices—from pronouns to punctuation—impact the perceived tone of feedback.  Our initial results suggest that mitigated statements and questions (i.e., with modals such as might or could) were perceived more positively, as were statements with we or you (in contrast to I statements, which were perceived as having a more negative tone by students).  And while questions in general were perceived more positively, sentence fragments followed by questions were more likely to be perceived as negative or neutral.    My feedback to this student fell squarely into the “negative tone zone.”   Eight of my comments were question fragments, like these: Photo by Simone Secci via Unsplash Communicate?  With a machine?  Not a human? Really? Our tone study did not address question marks at the beginning of a comment, or the repetition of question marks, like these instances from my feedback: ??? Sorry, but it cannot be "real life" if one half of the conversation is carried about by a machine.  A simulation?  Perhaps.  But let's call it what it is:  a simulation.  ?? How do we know this?  I suspect that such examples, had we included them in our pilot study, would have been rated generally negative; I hear my frustration shouting in these comments.  So, I reached out to the student:  we had had a strong working relationship, and I did not want to jeopardize that.  I pointed out that I did not regret the content of my comments, only the manner in which I had given them.  This paper came at the end of several hours of responding, and the topic touched a nerve, as it positioned me personally in a way I did not like. Now some might suggest that my evident frustration is a strong reason for having generative AI compose responses to writing:  AI isn’t subject to response fatigue or personal affront.  However, I would argue the opposite: generative AI cannot and should not respond to papers such as these, nor should I let it “improve” my response by making it “sound objective.”  Yes, I need to monitor the tone of my feedback.  But my student also needs to know how her work is perceived by a human—a tired human, a human who may disagree, a human who ultimately cares deeply about her and wants her to excel.   The email that I sent to check on my student opened space for additional dialogue.  Would that have happened if AI had responded or modified my comments? There are critical questions to be asked about generative AI in language classrooms (and in pedagogy overall), what it means to be a teacher, what it means to be a language user, and what it means to learn.   Had I decided that feedback on this paper could best be given by generative AI, those questions would not have been asked.  Would I revise some of my comments—and eliminate some of those irritated question marks—if I could?  Yes.  But in the end, I would not give the job of responding to generative AI; my student is worth my feedback, even if flawed.  At least it’s human. ... View more

When I joined a book club in the little community where I live, no member had ever read a graphic novel. But since I am a big fan of comics/graphic novels, I asked if they might take the plunge, and then we read Art Spiegelman’s Pulitzer Prize-winning Maus, which I regard as a masterpiece. At first glance, reactions varied: two people declared the work blasphemous because it was a “comic” about the Holocaust. Two others said they just didn’t want to have to read about such a terrible time. They grumbled, but they read. And by the time we met for discussion, they were, to a person, deeply moved and very, very glad they had read Spiegelman’s work.  This week we are going to discuss another of my favorite graphic memoirs, Marjane Satrapi’s Persepolis. I have been wanting to re-read this book in the last six months or so since the Hamas-Israel war began, as I’ve tried to understand the alliances and sometimes shifting allegiances among groups in the Middle East. Time to take a look back at the Iranian revolution and its aftermath–and Iran’s role in regional conflicts today. Marjane Satrapi at a screening of the film version of Persepolis in 2007   I have taught Persepolis along with the film that Satrapi did later, but I hadn't revisited the text in years. I'm glad I did: meeting little Marji again, seeing the fall of the Shah and the revolution through her young eyes, following her well-educated, activist parents from their joy at the overthrow of the old regime to their dawning horror at what came afterward with the Islamic regime and the Revolutionary Guards. And following Marji's complicated tensions between her western (French) schooling and Islamic traditions, along with her growing realization of the biases and failings of each, left me wishing I were in the classroom again. Then I heard that Satrapi, who moved solidly into film in more recent years, had a new book out. She says she thought she was through with graphic memoirs and with print books, but recent events changed her mind. In particular, the death of Mahsa Amini, “beaten to death by the Iranian morality police for wearing her veil improperly,” galvanized Satrapi and others to mount a new effort, titled Woman, Life, Freedom. The book opens with a meditation from Satrapi on the role of heroic women in Iranian history, includes 23 graphic stories by a range of distinguished writers and illustrators, and concludes with illustrated discussion among Satrapi and several contributors that looks to the Iran of today and of the future. I have read only the first couple of pieces in this new book, but it seems to make such a timely intervention into the fierce debates going on about freedom in general and freedom of speech in particular. And it has taken me back to my days of teaching Persepolis. I often asked students to take a contemporary issue about which they were passionate and to create a few panels of a graphic text that would explore that issue from two or three different perspectives. This was a challenging assignment, but I found that attempting to create drawings, even very simple or crude ones (like the ones I always did!) somehow knocked us a little off balance, got us out of our typical comfort zones, and allowed us to visualize differing perspectives on very hot button issues.  Perhaps because our drawings sometimes captured our feelings and emotions better than our words could—especially angry or hateful words—we ended up having more open conversations about sensitive issues than I had found possible in other kinds of assignments.  So–for what it’s worth–Satrapi’s past and present work is on my reading list right now, and I think you might like to add it to yours as well.   Image from Wikimedia Commons ... View more

This spring, I’ve offered five tips to students writing in each of the four genres covered in my textbook, Creative Writing: Four Genres in Brief. The final installment is on the art of playwriting. Playwriting is perhaps the trickiest genre to cover in the introductory course. Many students may have never seen a play in a theatre. They have, however, seen many films and television shows, which don’t necessarily make great models for young playwrights. Luckily, the homemade, DIY nature of playwriting, especially when it focuses on the ten-minute play, allows beginners to jump right in. Script-in-hand performances can take place in the classroom, without the elaborate and expensive elements required for a making a movie or TV program. Indeed, even a short film recorded on a mobile phone is likely to have a more complicated production process than the staged reading of a ten-minute play.   Here are my Top 5 Tips for Beginning Playwrights:   Begin with a clear and immediate conflict and conclude with a memorable ending. In other genres, readers will sometimes allow authors the equivalent of a couple of minutes to make an implied pitch about why they should stay with us for the rest of a piece. However, an audience watching a group of actors on stage, particularly when they know the play is very short, want something to happen right away. Even a minute of hemming and hawing before the conflict becomes apparent can feel like forever. And if the opening must grab audience members by their sleeves, the ending should have as much, if not more, impact. The last speech, the final gesture, a poignant lighting or sound effect—the ten minutes that have come before should feel as though they are somehow encapsulated in the final moments of the play.   Don’t write a skit. Watching an extended comedy sketch on Saturday Night Live can feel a lot like watching a ten-minute play: a handful of characters trade lines in a confined space, typically with a central conflict that is immediately apparent. But a skit, where the overriding and sometimes only goal is to get laughs, is not a play. There are plenty of hilarious short plays, but they nearly always have something significant—a serious theme, a personal revelation, an unexpected epiphany—simmering below their surfaces, ready to erupt.   Include the fewest possible characters necessary to tell your story. Initially, it can seem fun to have a bunch of people traipsing around on stage, but in a ten-minute play, too many characters quickly look like clutter. Can two people with similar qualities and purposes be combined into one more interesting character? Does the barista with one line or the waiter with two really need to be part of your cast? The fewer characters on stage, the more time and lines each actor has to make their character feel like a real person, someone with more than two dimensions.   Read your dialogue aloud several times before including it in your play. Few of us speak without using contractions—we normally say “I don’t” rather than “I do not”—and we often speak in sentence fragments.  Naturalistic speech relies on playwrights simulating the improvised and staccato sounds and patterns of actual talk. It’s sometimes helpful to sit in a public place with your eyes closed listening to how people really talk, but once you’ve written your script, it can still be hard to hear an awkward line when it’s sitting there on the page or screen. You hear best those places that need revision when you’re listening to others read your dialogue. Even if you have to read all the parts yourself, it’s vital to do so a few times aloud.   Take advantage of production elements unique to the theater. Generally, there’s a lot more dialogue in a play than there is in a film or a television program. After all, the main tools at a playwright’s disposal are actors and their words. To mitigate the “talkiness” that can sometimes consume a play, use simple light and sound cues—the sudden blare of an alarm, for instance, may startle people in their seats. Have your actors move around the stage in interesting ways; let them get in each other’s faces then stalk off to opposite sides of the stage. And allow your actors to wield props when appropriate—a real flower held by a real human being can have an outsized impact on live audience members a few feet away. But don’t go overboard. When it comes to sets and props and effects in a stage play, a little goes a long way. ... View more

How can the classroom be extended beyond the dingy walls and rattling windows of an assigned classroom space in disrepair? Photo by Susan Bernstein   Synthesis, Application, Embodied Knowledge  Neurodivergent Teaching   The accountant who thinks in numbers. The biologist who thinks in ecosystems. The chemist who thinks in formulas. The engineer who thinks in blueprints. The gymnast who thinks in movement. The historian who thinks in chronology. The musician who thinks in sound. The physicist who thinks in matter. The poet who thinks in words. The psychologist who thinks in emotions. The statistician who thinks in data. The artist who thinks in numbers, ecosystems, formulas, blueprints, movement, chronology, sound, matter, words, emotions, data.   The artist who thinks as a synthesizer.  Synthesis: this is why art means so much to me as a neurodivergent teacher diagnosed with ADHD and generalized anxiety, and who, for many years, had no diagnosis at all.  My diagnosis helps me understand my orientation to the world. I use the present tense because the processes of orienting and understanding are ongoing and ever-shifting; the processes include synthesis. Bringing together categories and elements that seem unrelated and even opposed– the art of working with these problems is where creativity happens. Composition, in this view, is the ongoing process of synthesis.  As a teacher of composition, I try to incorporate the exhibits into our lessons whenever I have access to on-campus art museums.. In Fall 2023, reflecting Covid’s long wake of remote learning and disembodied teaching, the museum lesson focused on experiential learning, learning on purpose, and learning to show up.  This semester, with the majority of my students majoring in fields immediately outside of traditional art forms, and primarily in STEM fields, the lesson focused on synthesis. Part of synthesis is accessing what you know to learn what you don’t know.  To attend to this process, we visited the Godwin-Ternbach Museum to view an exhibit on portraiture, and then held a workshop to make our own portraits. Because our class includes corequisites, we used our additional hours to return to the museum in smaller cohorts to continue our observations. Synthesis includes application–embodied knowledge. How do we apply what we do know to situations, contexts, problems that seem removed from our everyday experiences, interests, and coursework? In the case of our art museum visit, how can rhetorical appeals be applied to portrait painting? Such questions, I suggested, are not so far removed from STEM fields. How do different areas of science and math address design, proofs, and data in problems-solving processes? What choices do portrait artists make in composition? What connections can be drawn from them? Given ongoing chaos, division, and uncertainty outside the classroom, how can connection be found inside the classroom, and how can the classroom be extended beyond the dingy walls and rattling windows of an assigned classroom space in disrepair?  How can spaces of learning include discord and dissension to arrive at new and expanded definitions and conclusions? In other words,  how is it possible to attend to dissonance in the context of first-year writing?  As this semester wanes, I return once again to the processes of synthesis, and the persistent aim of learning to grow as writers.   APPENDIX: Assignment for the Art Museum Portraiture Exhibit: Rhetorical Questions to Ask about Art Choose a work of art for your focus, then address the following questions to gather data and evidence: COMPOSITION: How does the portrait use color, light, and darkness to gain the audience’s attention? Does the  portrait succeed in capturing and keeping the audience’s attention? Why or why not?   ORGANIZATION: How is the idea of the portrait interpreted? For instance, does the artist draw the complete figure, or just the head and torso? Does the artist use an object as a portrait instead of a figure? Why do you think they made these choices? INTEGRITY AND EMOTION: Does the portrait display an artist’s struggle for integrity? Do the artists use emotion as part of their work? Does their work alleviate suffering? Do the artists make or document the truth of history? Why or why not? (Adapted from The Artist’s Struggle for Integrity by James Baldwin) These questions are adapted from a video about our museum visit, “Museum Field Trip and Questions to Ask About Art April 2024.” ... View more

Like teachers across the world, I’ve been in the process of experimenting with AI since ChatGPT burst onto the scene some 18 months ago. Along the way, I’ve been figuring out what AI can and can’t do, what I think it can help students do (brainstorm, proofread, and more), and what I hope students will not use it for (writing for them). I’ve also had some fun, asking it to write something in the style of Jane Austen or B.B. King—things you’ve no doubt done too. Recently, I plugged in one of my blog posts from back in February and asked ChatGPT to provide a brief summary. Here’s what it said: The provided text is a personal account of an educator's experience speaking to a group of first-year humanities students at the University of California, Irvine, about writing style. The speaker recalls being nervous but ultimately succeeding in engaging the students, especially during a Q&A session when a student asked how to make sentences "sing." This led to a discussion about rhythm in writing. The narrative then shifts to Daniel Tammet, an individual with synesthesia and autism, who experiences numbers with colors and attitudes. Tammet once recited the number pi, which he refers to as an epic poem, for five hours, expressing his emotions through the numbers. The author reflects on how Tammet's experience might resonate with students who struggle to find their own voices and suggests using Tammet's essays to inspire students to express their deepest emotions through their unique voices. This is a perfectly good straightforward summary of my post. Thank you, ChatGPT (“You are most welcome,” it replied.). But it is also the kind of paint-by-the-numbers, generic or bland prose that I associate with AI at this point. That’s not to diminish the accomplishment, which still startles and often delights me, or to ignore the fact that a better prompt could have elicited better prose. But it also shows why I don’t want students letting AI write for them: I am convinced that they can often write better than AI—because they can write prose that sounds like them, that has their own voices.   So I asked ChatGPT if it could revise its summary to make it “sound more like the author of the original text.” Oh, my word—here’s the response: Certainly! The text describes a stirring encounter I had with a lively group of first-year humanities students at UC Irvine. I was there to weave a tapestry of words about writing style, an endeavor that quickened my pulse with excitement, though I must confess a flutter of nerves as I took the stage. Armed with an arsenal of exemplary student-written openings and closings, plus sentences that danced with stylistic flair from a kaleidoscope of authors, the presentation blossomed into a vibrant success, much to my heart's delight. The real magic unfurled during the Q&A, where a question that has since etched itself in my memory emerged from a strapping young man with the bearing of an athlete. His inquiry about making sentences "sing" was a clarion call that ignited a collective exploration into the rhythm of words, a session that resonated with nods and murmurs of enlightenment from the captivated audience. This vibrant session brought to mind Daniel Tammet's literary odyssey, "Every Word Is a Bird We Teach to Sing." Tammet, with a mind that paints days of the week in hues and hears the personalities of numbers, shared his own symphony of digits. Reciting the number pi, he transformed it into an epic narrative, eliciting rapt attention and emotional responses from his audience. In reflecting upon this, I ponder the kinship our students might share with Tammet—not as synesthetes, but as seekers of their own distinctive voice. What catalyzes their ability to articulate the most profound crevices of their being? It's a question I hold dear, hoping to stir within them a symphony of self-expression, much like the essays of Tammet that I encourage them to delve into and discuss, in pursuit of their own words that truly sing. I had to read this out loud to fully take it in, with its love of adjectives and flowery language, along with amazing clichés and even words or phrases I have seldom, if ever, said. Interesting! But of course, AI had only one little sample of my text to work from, or at least I think that’s all it had. Still, I decided to ask another question: how would ChatGPT describe this author’s style? Another surprise: The style of the original text's author is reflective, anecdotal, and engaging. It possesses a narrative quality that weaves personal experiences with broader reflections. The author uses vivid imagery and metaphor, such as "sentences that danced with stylistic flair" and "symphony of digits," to create a strong sensory experience. The language is emotive and expressive, aiming to connect with the reader on a personal level. The author also demonstrates a clear appreciation for the intricacy and power of language, both in the recounting of their own experience with teaching writing style and in the portrayal of Daniel Tammet's unique relationship with numbers. There's a rhythm to the writing that mirrors the topic of discussion—making sentences sing—which suggests a deliberate attention to the cadence and flow of the prose. Moreover, the author's style is thoughtful and introspective, often posing questions to engage the reader's own musings on the subject of voice and self-expression. The writing is peppered with moments of vulnerability, sharing uncertainties and personal triumphs that add a layer of authenticity and relatability. Overall, the style is that of a storyteller who is deeply fascinated by the nuances of communication and the individual journey to find one’s voice. Hmm. Some of this seems accurate and useful—commenting on the narrative qualities, for example, or an attempt to mirror the topic of discussion. But in this response, ChatGPT begins to make things up, quoting passages (“symphony of digits” and “danced with stylistic flair”) that appear nowhere in the post. Hallucinations, as they say. This little recent experiment reaffirms my sense that I don’t want AI writing for me any time soon: even with all my flaws as a writer, I can hear myself in the original blog post. But not in any of the ChatGPT responses. And that’s exactly as it should be. For now, AI can be a helpful tool, especially for getting started on a project, for brainstorming, and for proofreading. But not for writing for me. Or for students, as so many teachers are pointing out.   Image via Wikimedia Commons ... View more

Jennifer Duncan has been teaching English for twenty years, first at Chattanooga State Community College in Tennessee and now at Georgia State University’s Perimeter College in Atlanta, GA where she is Associate Professor of English.  For the last ten years, she has focused exclusively on online teaching concentrating on creating authentic and meaningful learning opportunities for students in composition and literature courses.   The latest episode of Abbott Elementary – a truly hilarious show that you should probably watch instead of reading this blog – hits on the topic of AI detectors. As Jacob, a History teacher at the school, demonstrates the newest AI detector to his class of eighth graders, he discovers his colleagues have been using AI to write responses to his newsletters. He’s deeply offended, but it’s a sitcom, so we can all predict exactly how the episode will end.   The problem, of course, is that accurate AI detectors are as fictitious as the class sizes at this TV school (seriously, how does a public school class have only ten students?) The truth is that AI detectors don’t work. Don’t get me wrong; I’d love to have a tool that immediately and accurately identifies AI-generated text. I’d also love to teach in a classroom next to Sheryl Lee Ralph and have visits from a wise-cracking janitor every day, but none of these things is likely to happen. AI image generated by craiyon.com So, should we even bother with AI detection tools? I’m on the fence about it. Just as I can’t figure out why a school that couldn’t buy nap mats for kindergartners in season one can afford a major software upgrade, I’m not sure AI detection systems are a great use of our students’ tech fees. The news is flooded with stories of students protesting academic integrity charges based on AI detection, and students should not have to fight accusations based solely on auto-detection reports which basically amount to one robot pointing a finger at another robot.  AI detection systems are full of false positives – a fact that can be confirmed by a quick Google search (and is there any more reliable standard than a Google search?). The news reports don’t make students stop cheating; they do make faculty and universities look like the enemy, and, when they get it wrong, as they often do, it fractures relationships in a way that won’t be fixed by a few jokes and heartfelt conversation after the second commercial. Tuition does not entitle students to specific grades, but it does entitle them to be part of a scholarly conversation with other humans who are invested in their educational growth. That conversation is where a detection tool may be helpful – not as evidence, but as a talking point. AI detectors note common language patterns and flag them as potentially AI-generated. Put your own writing into one, and it’s likely that some portion of it will be flagged, not because you cheated but rather because language has recognizable patterns. It’s why we can finish each other’s ---. Detectors are great at noting template-style writing – explicit announcements of what an essay will do, signpost transitions, restatements of common phrases and ideas - but this doesn’t necessarily indicate a student is cheating. We rely on patterns every day to make sense of the world; it’s how we know Jacob and his colleagues will be friends again by the show’s end.   Students use patterns and predictable writing for lots of reasons: their overworked high school teachers may have used automated feedback systems which rewarded predictability; they may be second language learners who learned English through patterns; they may be neurodivergent learners. These underserved students are already at risk of educational bias, and accusing them of cheating just pushes them further outside the academic circle. So, to run the report or not? Well, a report can be used to provide feedback, but just like students can’t give their writing over to a bot, we can’t give over our assessment of the writing to one either.  We can, however, use a report as a conversation starter with a student – not necessarily a conversation about cheating, but a conversation about their writing. Help students identify the patterns or word choices flagged as robotic. Then, discuss why these patterns are problematic or if they are appropriate for the situation. Talk to them about their writing choices and acknowledge that sometimes our choices will raise an AI detector’s flag, not because they’re the wrong choice, but because they’re the predictable one. And predictability does have a place in writing. A process essay shouldn’t sound like a William Faulkner book chapter – at least not if anyone is going to read it. I certainly don’t want students using AI (to quote Barbara Howard, “That privilege is reserved for teachers”), but when they do, we should deal with students as individuals, not as computer-generated reports. Will my strategy succeed at preventing all cheating? Of course not, but “If you come back here tomorrow, ready to do your job, having not given up on yourself or that student, well, that is not a failure. Sometimes that’s what success looks like” – Barbara Howard. ... View more