The Higher Education Inquirer is calling on student journalists, college students, faculty, and independent writers to speak truth to power about the ongoing genocide in Palestine. At a time when universities, governments, and media outlets are complicit through silence, distortion, or outright propaganda, it is urgent that we create space for honest accounts, rigorous investigations, and unapologetic solidarity.
We are seeking pieces that uncover how campuses are responding—or refusing to respond—to the atrocities, that expose academic and financial ties between U.S. higher education and Israel, that highlight student and faculty resistance, and that reflect on the risks of teaching and speaking openly in an environment of censorship and fear. We are especially interested in writing that challenges media narratives, including the BBC’s deeply biased coverage of Gaza, which research shows privileges Israeli voices and humanizes Israeli deaths while erasing Palestinian suffering.
This is not a moment for neutrality. Higher education is entangled in global systems of power, and its students and workers bear both the weight of silence and the responsibility to resist. We welcome investigative reporting, personal testimony, analytical essays, and critical reflections. Because safety is a real concern, we will publish pieces anonymously if needed.
If you are ready to contribute, send a 2–3 sentence pitch to [email protected]. The Higher Education Inquirer stands in the muckraking tradition: fearless, uncompromising, and committed to amplifying voices that others try to silence.
Paul T. Corrigan teaches at The University of Tampa. He is currently writing a book on teaching literature. He has published on teaching and learning in TheAtlantic.com, The Chronicle of Higher Education, Inside Higher Ed, College Teaching, Pedagogy, Reader, The Teaching Professor, International Journal for the Scholarship of Teaching and Learning, and other venues. He has a PhD from the University of South Florida and a MA from North Carolina State University. More at paultcorrigan.com. Follow on Twitter at @teachingcollege.
A few weeks ago, MIT’s Media Lab put out a study on how AI affects the brain. The study ignited a firestorm of posts and comments on social media, given its provocative finding that students who relied on ChatGPT for writing tasks showed lower brain engagement on EEG scans, hinting that offloading thinking to AI can literally dull our neural activity. For anyone who has used AI, it’s not hard to see how AI systems can become learning crutches that encourage mental laziness.
But I don’t think a simple “AI harms learning” conclusion tells the whole story. In this blog post (adapted from a recent series of posts I shared on LinkedIn), I want to add to the conversation by tackling the potential impact of AI in education from four angles. I’ll explore how AI’s unique adaptability can reshape rigid systems, how it both fights and fuels misinformation, how AI can be both good and bad depending on how it is used, and why its funding model may ultimately determine whether AI serves learners or short-circuits their growth.
What if the most transformative aspect of AI for schools isn’t its intelligence, but its adaptability?
Most technologies make us adjust to them. We have to learn how they work and adapt our behavior. Industrial machines, enterprise software, even a basic thermostat—they all come with instructions and patterns we need to learn and follow.
Education highlights this dynamic in a different way. How does education’s “factory model” work when students don’t come to school as standardized raw inputs? In many ways, schools expect students to conform to the requirements of the system—show up on time, sharpen your pencil before class, sit quietly while the teacher is talking, raise your hand if you want to speak. Those social norms are expectations we place on students so that standardized education can work. But as anyone who has tried to manage a group of six-year-olds knows, a class of students is full of complicated humans who never fully conform to what the system expects. So, teachers serve as the malleable middle layer. They adapt standardized systems to make them work for real students. Without that human adaptability, the system would collapse.
Same thing in manufacturing. Edgar Schein notes that engineers aim to design systems that run themselves. But operators know systems never work perfectly. Their job—and often their sense of professional identity—is about having the expertise to adapt and adjust when things inevitably go off-script. Human adaptability in the face of rigid systems keeps everything running.
So, how does this relate to AI? AI breaks the mold of most machines and systems humans have designed and dealt with throughout history. It doesn’t just follow its algorithm and expect us to learn how to use it. It adapts to us, like how teachers or factory operators adapt to the realities of the world to compensate for the rigidity of standardized systems.
You don’t need a coding background or a manual. You just speak to it. (I literally hit the voice-to-text button and talk to it like I’m explaining something to a person.) Messy, natural human language—the age-old human-to-human interface that our brains are wired to pick up on as infants—has become the interface for large language models. In other words, what makes today’s AI models amazing is their ability to use our interface, rather than asking us to learn theirs.
For me, the early hype about “prompt engineering” never really made sense. It assumed that success with AI required becoming an AI whisperer who knew how to speak AI’s language. But in my experience, working well with AI is less about learning special ways to talk to AI and more about just being a clear communicator, just like a good teacher or a good manager.
Now imagine this: what if AI becomes the new malleable middle layer across all kinds of systems? Not just a tool, but an adaptive bridge that makes other rigid, standardized systems work well together. If AI can make interoperability nearly frictionless—adapting to each system and context, rather than forcing people to adapt to it—that could be transformative. It’s not hard to see how this shift might ripple far beyond technology into how we organize institutions, deliver services, and design learning experiences.
Consider two concrete examples of how this might transform schools. First, our current system heavily relies on the written word as the medium for assessing students’ learning. To be clear, writing is an important skill that students need to develop to help them navigate the world beyond school. Yet at the same time, schools’ heavy reliance on writing as the medium for demonstrating learning creates barriers for students with learning disabilities, neurodivergent learners, or English language learners—all of whom may have a deep understanding but struggle to express it through writing in English. AI could serve as that adaptive layer, allowing students to demonstrate their knowledge and receive feedback through speech, visual representations, or even their native language, while still ensuring rigorous assessment of their actual understanding.
Second, it’s obvious that students don’t all learn at the same pace—yet we’ve forced learning to happen at a uniform timeline because individualized pacing quickly becomes completely unmanageable when teachers are on their own to cover material and provide feedback to their students. So instead, everyone spends the same number of weeks on each unit of content and then moves to the next course or grade level together, regardless of individual readiness. Here again, AI could serve as that adaptive layer for keeping track of students’ individual learning progressions and then serving up customized feedback, explanations, and practice opportunities based on students’ individual needs.
Third, success in school isn’t just about academics—it’s about knowing how to navigate the system itself. Students need to know how to approach teachers for help, track announcements for tryouts and auditions, fill out paperwork for course selections, and advocate for themselves to get into the classes they want. These navigation skills become even more critical for college applications and financial aid. But there are huge inequities here because much of this knowledge comes from social capital—having parents or peers who already understand how the system works. AI could help level the playing field by serving as that adaptive coaching layer, guiding any student through the bureaucratic maze rather than expecting them to figure it out on their own or rely on family connections to decode the system.
Can AI help solve the problem of misinformation?
Most people I talk to are skeptical of the idea in this subhead—and understandably so.
We’ve all seen the headlines: deep fakes, hallucinated facts, bots that churn out clickbait. AI, many argue, will supercharge misinformation, not solve it. Others worry that overreliance on AI could make people less critical and more passive, outsourcing their thinking instead of sharpening it.
But what if that’s not the whole story?
Here’s what gives me hope: AI’s ability to spot falsehoods and surface truth at scale might be one of its most powerful—and underappreciated—capabilities.
First, consider what makes misinformation so destructive. It’s not just that people believe wrong facts. It’s that people build vastly different mental models of what’s true and real. They lose any shared basis for reasoning through disagreements. Once that happens, dialogue breaks down. Facts don’t matter because facts aren’t shared.
Traditionally, countering misinformation has required human judgment and painstaking research, both time-consuming and limited in scale. But AI changes the equation.
Unlike any single person, a large language model (LLM) can draw from an enormous base of facts, concepts, and contextual knowledge. LLMs know far more facts from their training data than any person can learn in a lifetime. And when paired with tools like a web browser or citation database, they can investigate claims, check sources, and explain discrepancies.
Imagine reading a social media post and getting a sidebar summary—courtesy of AI—that flags misleading statistics, offers missing context, and links to credible sources. Not months later, not buried in the comments—instantly, as the content appears. The technology to do this already exists.
Of course, AI is not perfect as a fact-checker. When large language models generate text, they aren’t producing precise queries of facts; they’re making probabilistic guesses at what the right response should be based on their training, and sometimes those guesses are wrong. (Just like human experts, they also generate answers by drawing on their expertise, and they sometimes get things wrong.) AI also has its own blind spots and biases based on the biases it inherits from its training data.
But in many ways, both hallucinations and biases in AI are easier to detect and address than the false statements and biases that come from millions of human minds across the internet. AI’s decision rules can be audited. Its output can be tested. Its propensity to hallucinate can be curtailed. That makes it a promising foundation for improving trust, at least compared to the murky, decentralized mess of misinformation we’re living in now.
This doesn’t mean AI will eliminate misinformation. But it could dramatically increase the accessibility of accurate information, and reduce the friction it takes to verify what’s true. Of course, most platforms don’t yet include built-in AI fact-checking, and even if they did, that approach would raise important concerns. Do we trust the sources that those companies prioritize? The rules their systems follow? The incentives that guide how their tools are designed? But beyond questions of trust, there’s a deeper concern: when AI passively flags errors or supplies corrections, it risks turning users into passive recipients of “answers” rather than active seekers of truth. Learning requires effort. It’s not just about having the right information—it’s about asking good questions, thinking critically, and grappling with ideas. That’s why I think one of the most important things to teach young people about how to use AI is to treat it as a tool for interrogating the information and ideas they encounter, both online and from AI itself. Just like we teach students to proofread their writing or double-check their math, we should help them develop habits of mind that use AI to spark their own inquiry—to question claims, explore perspectives, and dig deeper into the truth.
Still, this focuses on just one side of the story. As powerful as AI may be for fact-checking, it will inevitably be used to generate deepfakes and spin persuasive falsehoods.
AI isn’t just good or bad—it’s both. The future of education depends on how we use it.
Much of the commentary around AI takes a strong stance: either it’s an incredible force for progress or it’s a terrifying threat to humanity. These bold perspectives make for compelling headlines and persuasive arguments. But in reality, the world is messy. And most transformative innovations—AI included—cut both ways.
History is full of examples of technologies that have advanced society in profound ways while also creating new risks and challenges. The Industrial Revolution made it possible to mass-produce goods that have dramatically improved the quality of life for billions. It has also fueled pollution and environmental degradation. The internet connects communities, opens access to knowledge, and accelerates scientific progress—but it also fuels misinformation, addiction, and division. Nuclear energy can power cities—or obliterate them.
AI is no different. It will do amazing things. It will do terrible things. The question isn’t whether AI will be good or bad for humanity—it’s how the choices of its users and developers will determine the directions it takes.
Because I work in education, I’ve been especially focused on the impact of AI on learning. AI can make learning more engaging, more personalized, and more accessible. It can explain concepts in multiple ways, adapt to your level, provide feedback, generate practice exercises, or summarize key points. It’s like having a teaching assistant on demand to accelerate your learning.
But it can also short-circuit the learning process. Why wrestle with a hard problem when AI will just give you the answer? Why wrestle with an idea when you can ask AI to write the essay for you? And even when students have every intention of learning, AI can create the illusion of learning while leaving understanding shallow.
This double-edged dynamic isn’t limited to learning. It’s also apparent in the world of work. AI is already making it easier for individuals to take on entrepreneurial projects that would have previously required whole teams. A startup no longer needs to hire a designer to create its logo, a marketer to build its brand assets, or an editor to write its press releases. In the near future, you may not even need to know how to code to build a software product. AI can help individuals turn ideas into action with far fewer barriers. And for those who feel overwhelmed by the idea of starting something new, AI can coach them through it, step by step. We may be on the front end of a boom in entrepreneurship unlocked by AI.
At the same time, however, AI is displacing many of the entry-level knowledge jobs that people have historically relied on to get their careers started. Tasks like drafting memos, doing basic research, or managing spreadsheets—once done by junior staff—can increasingly be handled by AI. That shift is making it harder for new graduates to break into the workforce and develop their skills on the job.
One way to mitigate these challenges is to build AI tools that are designed to support learning, not circumvent it. For example, Khan Academy’s Khanmigo helps students think critically about the material they’re learning rather than just giving them answers. It encourages ideation, offers feedback, and prompts deeper understanding—serving as a thoughtful coach, not a shortcut. But the deeper issue AI brings into focus is that our education system often treats learning as a means to an end—a set of hoops to jump through on the way to a diploma. To truly prepare students for a world shaped by AI, we need to rethink that approach. First, we should focus less on teaching only the skills AI can already do well. And second, we should make learning more about pursuing goals students care about—goals that require curiosity, critical thinking, and perseverance. Rather than training students to follow a prescribed path, we should be helping them learn how to chart their own. That’s especially important in a world where career paths are becoming less predictable, and opportunities often require the kind of initiative and adaptability we associate with entrepreneurs.
In short, AI is just the latest technological double-edged sword. It can support learning, or short-circuit it. Boost entrepreneurship—or displace entry-level jobs. The key isn’t to declare AI good or bad, but to recognize that it’s both, and then to be intentional about how we shape its trajectory.
That trajectory won’t be determined by technical capabilities alone. Who pays for AI, and what they pay it to do, will influence whether it evolves to support human learning, expertise, and connection, or to exploit our attention, take our jobs, and replace our relationships.
What actually determines whether AI helps or harms?
When people talk about the opportunities and risks of artificial intelligence, the conversation tends to focus on the technology’s capabilities—what it might be able to do, what it might replace, what breakthroughs lie ahead. But just focusing on what the technology does—both good and bad—doesn’t tell the whole story. The business model behind a technology influences how it evolves.
For example, when advertisers are the paying customer, as they are for many social media platforms, products tend to evolve to maximize user engagement and time-on-platform. That’s how we ended up with doomscrolling—endless content feeds optimized to occupy our attention so companies can show us more ads, often at the expense of our well-being.
That incentive could be particularly dangerous with AI. If you combine superhuman persuasion tools with an incentive to monopolize users’ attention, the results will be deeply manipulative. And this gets at a concern my colleague Julia Freeland Fisher has been raising: What happens if AI systems start to displace human connection? If AI becomes your go-to for friendship or emotional support, it risks crowding out the real relationships in your life.
Whether or not AI ends up undermining human relationships depends a lot on how it’s paid for. An AI built to hold your attention and keep you coming back might try to be your best friend. But an AI built to help you solve problems in the real world will behave differently. That kind of AI might say, “Hey, we’ve been talking for a while—why not go try out some of the things we’ve discussed?” or “Sounds like it’s time to take a break and connect with someone you care about.”
Some decisions made by the major AI companies seem encouraging. Sam Altman, OpenAI’s CEO, has said that adopting ads would be a last resort. “I’m not saying OpenAI would never consider ads, but I don’t like them in general, and I think that ads-plus-AI is sort of uniquely unsettling to me.” Instead, most AI developers like OpenAI and Anthropic have turned to user subscriptions, an incentive structure that doesn’t steer as hard toward addictiveness. OpenAI is also exploring AI-centric hardware as a business model—another experiment that seems more promising for user wellbeing.
So far, we’ve been talking about the directions AI will take as companies develop their technologies for individual consumers, but there’s another angle worth considering: how AI gets adopted into the workplace. One of the big concerns is that AI will be used to replace people, not necessarily because it does the job better, but because it’s cheaper. That decision often comes down to incentives. Right now, businesses pay a lot in payroll taxes and benefits for every employee, but they get tax breaks when they invest in software and machines. So, from a purely financial standpoint, replacing people with technology can look like a smart move. In the book, The Once and Future Worker, Oren Cass discusses this problem and suggests flipping that script—taxing capital more and labor less—so companies aren’t nudged toward cutting jobs just to save money. That change wouldn’t stop companies from using AI, but it would encourage them to deploy it in ways that complement, rather than replace, human workers.
Currently, while AI companies operate without sustainable business models, they’re buoyed by investor funding. Investors are willing to bankroll companies with little or no revenue today because they see the potential for massive profits in the future. But that investor model creates pressure to grow rapidly and acquire as many users as possible, since scale is often a key metric of success in venture-backed tech. That drive for rapid growth can push companies to prioritize user acquisition over thoughtful product development, potentially at the expense of safety, ethics, or long-term consequences.
Given these realities, what can parents and educators do? First, they can be discerning customers. There are many AI tools available, and the choices they make matter. Rather than simply opting for what’s most entertaining or immediately useful, they can support companies whose business models and design choices reflect a concern for users’ well-being and societal impact.
Second, they can be vocal. Journalists, educators, and parents all have platforms—whether formal or informal—to raise questions, share concerns, and express what they hope to see from AI companies. Public dialogue helps shape media narratives, which in turn shape both market forces and policy decisions.
Third, they can advocate for smart, balanced regulation. As I noted above, AI shouldn’t be regulated as if it’s either all good or all bad. But reasonable guardrails can ensure that AI is developed and used in ways that serve the public good. Just as the customers and investors in a company’s value network influence its priorities, so too can policymakers play a constructive role as value network actors by creating smart policies that promote general welfare when market incentives fall short.
In sum, a company’s value network—who its investors are, who pays for its products, and what they hire those products to do—determines what companies optimize for. And in AI, that choice might shape not just how the technology evolves, but how it impacts our lives, our relationships, and our society.
Thomas Arnett, The Clayton Christensen Institute
Thomas Arnett is a senior research fellow for the Clayton Christensen Institute. His work focuses on using the Theory of Disruptive Innovation to study innovative instructional models and their potential to scale student-centered learning in K–12 education. He also studies demand for innovative resources and practices across the K–12 education system using the Jobs to Be Done Theory.
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Are you thinking about starting a podcast? I invited Dr. Anna Clemens to share her podcasting journey. We talk about how social media and online presence has changed for researchers in 2025. And, how storytelling can help people connect with your research in meaningful ways.
Dr. Anna Clemens is an academic writing coach who specializes in scientific research papers. She runs the Researchers’ Writing Academy, an online course where she helps researchers to get published in high-ranking journals without lacking structure in the writing process.
Jennifer van Alstyne: Hi everyone, this is Jennifer Van Alstyne. Welcome to the Social Academic Podcast. I’m here with Dr. Anna Clemens of the Researchers’ Writing Academy. Anna, I’m so happy to have you here today. First, because you’re my friend and we’ve been trying to do this for multiple years now. I’m so happy! And second because I want to share the program that you’ve created for scientists to help them write better. It’s actually something I’ve recommended to clients of mine, something clients of mine have participated in. So I wanted to share you with everyone who listens to the podcast. Would you please introduce yourself?
Dr. Anna Clemens: Yeah, of course. Thank you so much for having me. And I’m super excited. And it’s been such a joy having some of your clients in the program.
I run a program called the Researchers’ Writing Academy, where we help researchers, well, kind of develop a really structured writing process so they can get published in the journals they want to get published in. We kind of look a bit more toward top-tier journals, high-impact journals. But honestly, what we teach kind of helps you wherever you want to go.
I have a background in chemistry. So my PhD’s in chemistry and I transitioned into writing after that. So it’s a really fun way to be able to combine kind of my scientific knowledge with writing and helping folks to get published and make that all really time efficient.
Jennifer: Gosh, that’s amazing. I think that I did not have a lot of writing support when I was in grad school. And I really felt like even though I’m an excellent writer, like I’m a creative writer, like that’s what I went to school for.
Anna: You write poetry.
Jennifer: I write poetry and I think I’m a good academic writer, but I feel like I had to teach myself all of that. And it was a lot of correction after something was already submitted in order to bring it closer to what was actually publishable.
Anna: Right.
Jennifer: I lost so much time by not knowing things. So I love that you created a program to support people who maybe aren’t getting the training that they need to publish in those high impact journals.
Anna: Yeah, because that’s so common. Like, honestly, who gets good academic writing training? That’s really almost nobody.
I often see even people who do go on, do some kind of course of their university if they offer some kind of course. They’re often not really so focused on the things that I’m teaching, which is like a lot of storytelling and a lot like being efficient with your writing, like kind of the step by step. You kind of often know just like academic English, how do I sound good? And I think honestly, this is less important than knowing how to really tell a story in your paper and having that story be consistent and not losing time by all the like edits and rewrites, etc., that are so frustrating to do.
Storytelling for Researchers and Scientists
Jennifer: Hmm, you brought up storytelling. That’s really insightful.
As a creative writer, story is so important to the words that we create and how people can connect with them. Why is storytelling important for researchers?
Anna: Well, I think it’s because we’re all humans, right? So we just as humans, really need storytelling to be able to access information in the best way and to connect to that information and to kind of put it into the kind of frameworks that we have already in our minds.
This is what a lot of researchers really overestimate is like, your research is so incredibly specific, right? It’s so much, like that thing to you, it’s all like when you’re doing it, you’re like, of course you know every detail about it. And you just forget how little other people know. It’s even if they’re in the same field because we always think, “Oh, no, everyone knows what I know.” Also a bit this feeling of like, not quite realizing like, it’s also called like the experts curse I think, when you are an expert in something, and you don’t realize how little other people know. And you kind of undervalue what you know.
So anyway, if you really want your papers to be read, if you want to get published, you need to be able to, to make it accessible to like the journal editor, right? The peer reviewers, but also the readers later, they need to be able to understand the data in a way that makes sense to them. And I think that’s where storytelling comes in. Also, it really helps with structuring the writing process. Like honestly, if you think about storytelling first, the really nice side effect is your writing process will be a lot easier because you don’t have to go back and edit quite so many times.
Jennifer: Oh, that’s fascinating. So not only does it improve how the research is being communicated It improves the process of writing it too.
Anna: I think so. Yeah, because when you’re clear on the story, everything is clear in your head from the start. And you don’t need to kind of . . . I mean, when you write a paper for the first time, or even people who’ve written a few papers, they still sometimes start writing with the introduction. And it’s such a waste of time. Like they just start at the start, right? And then they end up like deleting all those paragraphs and all those words after when they actually have written so much that they then after a while understand the story that they want to tell. And instead, what I’m suggesting is like, define the story first. And I like guide people through how to do that.
Because I think the problem is you don’t really know how to do it when you don’t have like a framework for it. You have kind of the framework there from the start. So you know what the story is and you don’t have to kind of figure out the story while you’re writing. Instead, you know what the story is and the way I’m teaching it, I’m like giving people prompts so that it’s really easy to define the story because also story is really elusive, I think. Or we use it in this elusive way often when we like we kind of use it as like a throwaway term. Oh, yeah, you you should tell a story in your paper. And you go like, “Yeah, I guess. But what does that mean?” I’m trying to like give a definition for that. So that is like really clear. Okay?
Jennifer: I appreciate that. I think so many people aren’t sure what it means. And even if they think they know what it means, they don’t necessarily know how it applies to their scientific writing. So that’s really interesting.
Researchers’ Writing Academy
Jennifer: I want to talk about podcasts, but actually, since we’re already talking about program stuff right now, I’m curious about the format of your program because people who are listening to this may not be familiar with your work. And I want to make sure that they get to hear about all the cool things that they get if they join.
Anna: Yeah, the Researchers’ Writing Academy is very comprehensive.
Jennifer: Yeah, in a good way.
Anna: It’s almost hard to tell people about it because there’s so much in there. So, what people get is like, there’s an online course, we call it the journal publication formula, that’s like the step-by-step system, walks you through online lessons that you can watch, super short digestible lessons that walk you through step-by-step. So you can just write your paper alongside the lessons.
And then because we noticed that you really may want some help actually writing in your day to day work, right? Because we’re also incredibly busy. And then it’s just helpful to have some kind of accountability, some community, and that’s what we offer as well. So we do a lot of things around accountability and we have like, cowriting sessions, for example, where we meet, we have six now, six per week across time zones.
Jennifer: Wow, that’s amazing! So if you’re anywhere in the world, there’s a chance that one of those six times during the day will work for you. Oh my gosh, that’s so cool.
Anna: Yeah. I mean, they should work. I mean for Europe and the US, most of them will work. Or not, but it depends where in the US you are, etc. But even like a few in Australia, there’s at least one per week that will work for you depending on how long you want to stay up. Some people do, we have one client who comes, he likes to do writing after his kids are in bed. So he loves nine to 10pm, you know, like, yeah. So yeah, there’s a lot. And we do like, writing retreats every now and again, and writing sprints. So we like offer a lot of support around that. And we have like a really lovely community that are so supportive. Actually, I just talked to one member today, and she just got promoted to full professor.
Jennifer: Exciting
Anna: And she was like, “I couldn’t have done it without this community.” This was so like, valuable, not only getting the feedback on her article, but also, just knowing that like, there’s the support. And that’s really, I mean, that’s so lovely for me to hear, because this is honestly what I dreamed of. This is what I wanted to build. And it’s really nice knowing that people do, you know, really, not only reach career goals, but have a supportive community because academia can be a little toxic.
Jennifer: Yeah, yeah, there’s so many reports that have come out and said, mental health struggles, toxicity, it’s consistent. Yeah.
Anna: And honestly, writing plays a big part in that, because like, kind of the way we are normally not talking about writing. I think writing like, it’s, you sometimes see like, more seasoned academics. They sometimes are really good at writing and then act as if they have it all figured out, but not share their process. So you as like a novice writer think, “Shit, I should have figured it out. Like, why do I not know how this works?”
Jennifer: This is easy for them.
Anna: Yeah, exactly. The other day, someone said to me, “Yeah, I know this professor and he just writes his paper while I’m talking to him at a conference.” And I’m like, “Oh, okay, this is an interesting process.”
Jennifer: Wow. Like, it’s so clear in his brain that he can focus on that and a conversation at the same time. Fascinating.
Anna: Fascinating. And honestly, you don’t have to do that. But she kind of thought like, “This is who I have to be. This is how I have to do it.” That creates so much pressure. And yeah, writing just hits like, emotionally, it’s really hard, right? When we feel like we are procrastinating, when we have really low confidence in our writing and just feel really disappointed in ourselves because we’re like overly perfectionistic, can’t send stuff off, keep like, you know, refining sentences. It’s just really, really hard.
This is really why a community is so beautiful when we can all just open up about how hard it is and also give each other tips. Like, I just love when people, you know, share also what’s working for them. And like, down to little techniques. Like the other day, someone was sharing in the community about how they started having like their Friday afternoons as like a margin in their calendar. So, if they didn’t get, you know, to all the things they had done, if there was any derailing event, they still had like time on a Friday. A little hack like that, right?
That just like makes you more productive, makes you just honestly feel better about your work. Because we’re really tough on ourselves often. Like we’re really harsh and just, you know, having like a community that has this kind of spirit of being kind to yourself and working with your brain and not against it. Yeah, that’s really, really . . . that’s a really lovely place. Really supportive.
Jennifer: That sounds amazing. I’m curious about who should join your program because it sounds like it’s so supportive. It sounds like there’s community and accountability and training. So, I love all of that, but there’s probably some people who the program’s not right for. So, like, maybe who shouldn’t join and who should definitely join?
Anna: Yeah, that’s a good question. I mean, it is in terms of like career stage, it’s pretty open from PhD student up to professor. And we have all of those kind of career stages in the program. The biggest group is assistant professors, just so you know, like who you can expect to be in the program. And also the PhD students who are in there are often older. It’s really interesting. They’re often like second kind of career type students who maybe have, you know, chosen that path a little later in life. Just a little side note. It’s kind of interesting.
Jennifer: I think that makes so much sense because if I’m going back for like a PhD later on, I’m like, “I’m going to get all the support that I can to make the most of this time.” And joining a program like yours would make so much sense to me.
Anna: Yeah, they’re probably also busier most of the time because their parents or other stuff going on in their lives already.
Jennifer: Yeah, that’s what makes it easier to have time for like the life and the people that you care about because you already have these processes in place.
Anna: Yeah, yeah. So as to who shouldn’t join or who this wouldn’t be a good fit for, we don’t actually serve researchers in the humanities. So there’s this really science-based, social sciences included. And you know, physical sciences, life science, earth science, all the sciences we are super happy to have inside the program just because the general publication formula is super focused on just that type of research and really honestly quite focused on like original research papers, even though we have members who write review papers using it because honestly, the process isn’t very different. But we are like, just the examples, everything is from like original research papers. So just FYI.
Otherwise, I would say like we’re really super supportive and we don’t have like a lot of this like hustle culture, you know. This is all about, we don’t believe in like, having to wake up at 4am to have your whole three hour morning routine, including writing done, because a lot of us like have kids or have other kinds of commitments. So there is a lot of like kind of understanding that, you know, all of this has to work for real life. And not just for, I don’t know, people who have, yeah, men I guess who have a lot of support in the background traditionally, right? This is how research has been done. And yeah, even though we do have really lovely men in the program as well. So it’s not just women, but I guess this is kind of the approach that, yeah, we have in the community, in the academy.
Jennifer: I love that. So not hustle culture. More let’s learn these processes and have accountability together so that we can move towards this goal of publishing with kindness.
Anna: Yeah. It’s so funny, like this being kind. I mean, we often say like, “Be kind to yourself,” because sometimes we don’t achieve the goals we set, often we don’t achieve the goals we set ourselves, right? And what I always say is it’s a data point. Like, this was a really good data point this week, because just reflect on what happened. Oh, did your child get sick? Oh, there you go. So maybe you now need to have a process, what happens if my child gets sick? Because then, you can’t plan that, right? So you have to have, or it’s good to have in your kind of system, in your writing system, in your writing practice, that you account for that. Some kind of strategy, what you do when that happens. Or like, this took me a lot longer to complete, like, I thought I would get my introduction section done this week, but actually, I didn’t. Well, really good data point. Actually, maybe it takes you longer.
Look at how where you spend the time doing this section. This is really good to know for next time. Actually, maybe schedule one or two days more for this. So that’s kind of like the approach, the vibe that like is in there. So it’s not so, it’s not harsh.
Jennifer: Yeah, I like that vibe. That’s my kind of vibe.
Anna: Mine too. Yeah, mine too. And it really crystallized for me because I once was in a business coaching program where the vibe was really different. You probably remember me talking about this because I did tell you at the time, and it was so awful for me. And I really. . . but until then, it was really a bummer because I spent a lot of money on it.
Jennifer: And you’re like, “My community needs kindness and support for each other.
Anna: This was my big learning. Apparently, I needed to spend a lot of money to really have this like so, so clear that this is not for me. Like the bro-y culture is not for me. I need the kindness. Because otherwise, it doesn’t work. I don’t work like that if someone tells me I have to, I don’t know, have all these non-negotiables everyday.
Jennifer: Yeah, like change who you are.
Anna: Yeah, like you just have to do it. Like it’s just about the discipline. You know, I don’t think that works. I honestly don’t think it works in the long term. Like maybe you can force yourself for like a few months or years and then you’re burning out or something. Like, I just don’t see how this is a sensible approach.
Jennifer: No. And I remember at the time you mentioned that you felt burned out. Like you were being affected by the culture that you were experiencing. So creating a warm culture for people inside your program, the Researchers’ Writing Academy is wonderful. Everyone gets to benefit from your research.
Anna: Right? Yea!
Anna’s podcasting journey
Jennifer: So I want to chat a little bit about online presence because I mean, we met online, we mostly communicate online, but also like you have taken some actions this year in particular to have a stronger online presence through a new avenue, which is podcasting. I’m curious because when I started my podcast, it was like not very intentional. It was like, “Oh, I just better record this thing and like, it’s going to make it like a little more accessible than if it was just in writing.” And the podcast kind of evolved into a regular series after I had already decided to start it. Whereas you came in more with a plan, you had purpose, you had drive to do more episodes than I could imagine. And so what was it like to kind of get that spark of an idea that like, I want a podcast?
Anna: Yeah, I’ve had this, I mean, I had this desire for a long time. Many, many years. I always wanted to have a podcast.
Jennifer: Really?
Anna: Really because I listen to podcasts a lot. Like I’m really into them. And years ago, someone told me you would have such a good voice for podcasts. I was like, really? I don’t, because when you listen to your own voice, you’re like, “No, I don’t think so.” And I still don’t know whether this is really true, but I wanted to be more online. Like kind of, I wanted to have an online presence that wasn’t just social media.
Because honestly, I have such a weird relationship to social media, myself. It does like cognitively do something to my brain that isn’t always good, you know. Like hanging out there too much or getting sucked in, especially back on Twitter, now on Bluesky it’s a little bit like that too. There’s sometimes a lot of negativity. And I feel like people are too harsh, coming back to the being too harsh. I just can’t take it. Like, it’s not for me, but also just the fact that there’s just a lot going on there.
I wanted to be available to people somewhere else. And a podcast and I did actually simultaneously, like launch my podcast on YouTube as well. So it’s like a video podcast. That just made sense to me. Like, that just felt really aligned with what I like to consume, what I think my ideal clients like to consume. And where I also felt like I can like express myself, I guess, in a really good way. I mean, I do love writing, I do actually have a blog too. But it’s almost like when you have a blog, unless you’re like really, really good at SEO, which is a little hard in my niche, to be honest. Like nobody reads it, right? Unless you like amplify it through social media.
Jennifer: Actively sharing it. It’s its own marketing.
Anna: Yeah, yeah. So it’s still like social media connected. And I kind of wanted to have another avenue. Anyway, yeah. Talking also, I also like talking. So podcast made sense.
Jennifer: That’s amazing. When I started my podcast, it was kind of just like, you know, going on zoom and hitting record. What is your process like? Are there other people involved? What is the kind of behind the scenes for your podcast?
Anna: Yes, I have solo episodes. And I also have episodes with former clients or current clients actually, like members of the research as writing academy or alumni. And I also had one with one of my team members, our kind of client experience manager, Yvonne, where we talked about community. And I also had you on, right, as a guest expert. I think you’re the only guest expert actually we’ve had so far.
Jennifer: I feel so special. That’s amazing.
Anna: So yeah. The process for interviews, I would think of questions ahead of time. And we, for example, then chatted about the questions. This is also what I did with Yvonne. Just have a quick chat. I think both times it was written, like through Slack, just like, “Hey, does this make sense? Where do we want to go with this? Okay, maybe this should be a different discussion. Let’s focus on that.” And similar, actually, with the clients I interviewed. I would just send them a list of questions and be like,” Hey, you don’t need to prepare anything, but if you want to do” and then basically hop on and have a conversation and it’d be quite natural. And like this one where, you know, you don’t necessarily have to follow a script, you just go where it takes you.
For my solo episodes, it’s a little bit different where I do write an outline. And honestly, like, what surprised me was this took a lot of time. Even when I knew what I wanted to say, and maybe this is me being too perfect, too much of a perfectionist, because I would go back. So I’d write the outline, I would go back the next day or the day after I read it again and have more ideas. I’d be like, “No, no, this should be like this.” So, it took me a lot of time. But then also, I think the outlines got better and better and better. And then I was really, you know, proud of the episodes. I was like, “Yeah, I really expressed this, I think, in a good way.” Because what I did afterwards then is I took this transcript from that episode and turned those into a blog post.
Then with the blog post, I’m like, “Yeah, they’re really meaty. There’s so much in there.” Like, there’s so much longer than my other blog posts that were just blog posts without podcast episodes. So that was really interesting to me. Just like, you know, understanding I guess a little bit more about the process of writing or synthesizing ideas and concepts. And yeah, after the outline, I would record on my own, I would record the episodes with that outline like in front of me. So kind of a bullet point outline.
Jennifer: It sounds like your brain really likes the outlining process. And when you come back the second time, you have ideas to flush it out and tell the story even better. That’s really cool.
Anna: Yeah, it was honestly really fun writing those outlines. Because recording sometimes, especially in the beginning, was a little more stressful than I expected. It was shockingly stressful because I’m on video a lot. I thought it would be rather easy to record cause of my experience. And I think it would have been pretty easy if I just had done audio, but because I was also doing video, it felt a lot harder because it’s really hard to read an outline and look in the camera at the same time.
Jennifer: Oh yeah.
Anna: Like really, really hard. And I also couldn’t spend even more time like rehearsing the outline to the point where I didn’t need to look at it anymore. Like I didn’t feel like that made sense. And I was really struggling with that. And I was just like, being a little unhappy about it. Because when I talk, like when I’m like, I’m on a lot of calls, you know, inside the Academy, for example, or like interviews like this. And I find, for me it’s quite natural already to look at the camera. Like, I look at the camera a lot. But when I have an outline, you know, it’s like you do look at it. It was so hard. And actually, you helped me a lot with that.
Yeah, because I was sharing this, that I was really unhappy with my recordings because of, I wasn’t looking at the camera. And you said, “Well, look, so many people aren’t even recording video for that exact reason. And you’re putting something out that is less perfect than you hope will still be so useful to the people, to people watching it. Honestly, that doesn’t matter.” And then I was like, “Yeah, this is like perfectionism.” It was all right. I just wanted to have it perfect. And I had a different standard for myself. But I didn’t need to be there. Like I was just not there. And that was totally fine. It didn’t need to be quite as polished as I thought maybe it should be.
Jennifer: Yeah, and I think that we don’t give ourselves enough grace for like our first things, right? Like the first episodes, like the first launch of something new. Like, we want it to be really great because it’s new and because it represents us. But sometimes like, we’re just not there in terms of our own practice or our own skills, like something may need to build or improve for us to get to where we dream about being. And that’s okay. I really didn’t think, I didn’t have those negative feelings when I started my podcast, but so many of my clients and so many of the people that I’ve met along the way have talked about the first maybe five or six episodes being just such a struggle.
Looking at themselves on video, listening to themselves speak, doing the editing themselves. It brought up all of those feelings about like watching themselves and what it would be like for other people to watch them. But the truth is that like you are watching yourself and doing all of those things more than anyone else is. Like, if someone else is watching it, they may not even listen to or watch the entire thing. And if they are, maybe they’re doing something else, like cleaning up their room. You know, if it’s a podcast, it’s not something that people will always sit there and like stare at your face and look at everything you did that was wrong. That’s what we’re doing.
Anna: Yeah, yeah. Yeah. You’re so right.
Jennifer: For me, this year I have Sir Nic who does all of this kind of sound editing for me and he’s here in the virtual studio with us making sound levels all good. And then my husband Matthew does the video editing. So I don’t have to look at myself anymore or listen to myself. And it is so nice! It’s, oh my goodness, it’s such a relief for me to have those things off my plate. Do you have support on your team for podcast things or is it just the people who are working on, you know, the different kind of accountability coaching and things that are in the program?
Anna: Yeah, I did have support. So I outsource the editing, video and audio editing.
Jennifer: Love that.
Anna: I couldn’t have done it myself, honestly, like not so much. I mean, it takes a lot of time. I think people often underestimate just how much time this takes. And especially if you want the audio to be kind of good, you do want someone, an audio engineer I think. This was important to me to have like a decent microphone, decent audio. So I actually invested quite a lot in this space. I started recording in my former office. I’m not in there now anymore, but it had really high ceilings. So I put all these sound panels up, these like boards and I bought curtains that I now brought into this room as well to like reduce the echo. And that was just worth it to me. But yeah, I did have support. And then in-house, like on my team, my operations manager, she also helped me with the podcast. Like she would do a lot of like even reviewing episodes and suggesting maybe further edits. So I didn’t have to watch myself very much.
Jennifer: Oh, that’s great.
Anna: She would also take out little like clips from the episode that we then put on social media. Like as YouTube shorts, for example.
Jennifer: Yeah.
Anna: Yeah, so it was a really, really smooth process with a lot of support.
Jennifer: Yeah, getting support was something that I didn’t think my podcast deserved in the beginning, but now I feel like my listeners do. My listeners deserve that. If I can keep doing it for them, I’m going to. So I’m glad we got to chat about that because a lot of people are like, “Oh, I’m just going to go on Zoom and record.” And then maybe they’re surprised when the editing process is a lot longer. But also the first few episodes, if you’re starting something new like editing, like audio stuff, like even just being on video, it’s going to be hard. And it might not be as good as you want it to be at first, but it’s going to get better. It’s going to get better. Oh, before we… Oh, sorry. Go ahead.
Anna: No, no, no. I just said so true.
Social media for academics post-Elon
Jennifer: Well, I wanted to chat about the social media landscape and how things have been changing since Elon took over Twitter. I know you are on Bluesky now. I would love to hear a little bit about your experience of that platform.
Anna: Yeah, I’m on Bluesky now and I’m not on X or Twitter anymore. I mean, I do still have the account, but I don’t check it anymore. Some people are still finding me through there, though. That’s kind of interesting. I see it in my data, but I haven’t logged in in like months. Bluesky is very similar to Twitter, honestly, in the sense of the type of conversations that are happening there. But at least for me, there’s a lot less engagement than there was. And I’m actually wondering whether a lot of academics gave up on social media after Twitter went downhill, because there was this like really great academic community on Twitter through which I guess we met.
Jennifer: Yeah.
Anna: Back in the day. And I don’t see that happening on Bluesky. Bluesky does have a few other features, like additional features though that I really like. Like the way you can customize your feed a lot better. You can create those lists. So if you’re new to Bluesky, you can just like, there’s probably a list for researchers in your field.
“I struggle with writing a compelling story that is interesting outside of my field, yet doesn’t oversell my data.” ✍️
Jennifer: Yeah, like the starter packs and the different lists you could put together.
Anna: Exactly, starter packs. That’s what it’s called. Yeah. So you can just like hit follow all and you already have a feed full of people you want to have in your feed. And getting started is kind of really cool on Bluesky. I do think, I don’t know, something is different about the algorithm over there, but I’m not an expert. I don’t really know, but it feels like not as much things are like going viral per se.
Jennifer: Yeah.
Anna: Maybe a little more one to one.
Jennifer: Yeah. Oh, that’s really interesting. When I when I first joined Bluesky, which was much later than everyone else. It was really just last month. I found that it was very quiet. I connected with the people that were like the most talkative on Twitter. I hadn’t run Sky Follower Bridge or any of the tools to help me get connected yet because I wanted to see what the platform was like naturally. Like if someone was just signing up for the first time without having been on Twitter. And I was able to find people pretty easily. Like the people that I most often talked to or connected with, guests on The Social Academic, those kinds of things. But I wasn’t finding conversations. Like the people who I knew from social media weren’t talking all that much. They weren’t posting original content the way that they had on other platforms.
And when I did run Sky Follower Bridge and found all of the people from Threads, from X, etc. I realized that like so many people had accounts that they just hadn’t connected with people yet. Like they, you know, maybe started their account during the big X exodus and then they connected with 12 people because that’s who they found when they first got there. And when they didn’t find their community, it’s like maybe they stopped logging in. And I think that’s really normal for people. Like you’re going to look for the warmth in the conversations or just like the people talking and watching it, being able to see it without even participating in it. Like if you don’t see when you get there, it’s kind of like, “Well, why am I going to spend time in this space?” I had to do a lot more work than I expected in order to find the conversations. And I had to connect with a lot more people without knowing that they were going to follow me back. Like without that anticipation in order for me to feel connected. But once I did that, once I was following, like I follow like over a thousand people now, once I did that, it started to feel like old Twitter to me. Like the community and conversation. Yeah, there’s a lot of people who aren’t talking there, but I was just surprised how much effort it took to get to that feeling. More than other platforms for me.
Anna: Do you enjoy it now? Like the way you liked Twitter?
Jennifer: You know, I don’t think I really enjoy any one social media platform over another anymore. I feel like my relationship with creating content has changed a lot in that I found more ease and I found less pressure and I found like good processes that work for me. And because of that, I don’t spend a lot of time on social media. Like I’m not on there browsing for conversations the way that I think I did when I was on X. Like old Twitter, I liked spending time there and jumping into conversations. And now social media is more, I don’t intentionally put in my day as much anymore. That’s what it is. And I like that. I like how my relationship with social media has changed. But no, I haven’t gone back to how I engaged in old Twitter, I think. What about you?
Anna: That makes sense. Yeah, it’s similar for me, actually. I have to say I go through phases with it. So I do put out like content on several platforms like Threads, Bluesky and LinkedIn and then like YouTube as shorts. And I do go in and kind of check, does anyone comment? Like is anyone starting a conversation? I do this several times a week. But I don’t get sucked in as much anymore, if ever. Yeah, and I’m like super intentional about the time I spend there, I guess.
Jennifer: How are you intentional?
Anna: Well, I kind of set myself a timer as well.
Jennifer: Oh, like a literal timer.
Anna: So I don’t let myself like do more than, I don’t know, five minutes per platform.
Jennifer: Really?!
Anna: If there is like, of course, if there is comments, like actual, interesting conversations to join, I will, you know, override, but I’m really trying not to, not to get sucked in because it’s so easy for me. I don’t know. My brain is really-
Jennifer: That is really smart. I’ve never set a timer for that short amount of time. I’ll be like 30 minutes, you know, 30 minutes a day. Like if I’m going to have a timer maybe that’s what I would set it for. But five minutes is so much more specific, direct. That would wake my brain up. I should try something like that if I get sucked in again.
Anna: Yeah, I like it. I do like it. And because now I feel like the social media landscape for academics has changed in a way. They’re used to be, or for me they’re used to be just Twitter. I was basically just on Twitter and I didn’t really do anything on any other platform whereas now it’s a lot more spread out. And, I don’t know, there’s good and bad things about that. But now I feel like, “Okay, I need to spend time on LinkedIn. I need to spend on Blue Sky and on Threads.” So, you know, I just can’t spend like that much time anymore on just one platform. So it has to be kind of a bit more time efficient.
Jennifer: Okay, so you’re on Bluesky, Mastodon, YouTube, LinkedIn-
Anna: I’m not on Mastodon. Threads.
Jennifer: Not on Mastodon. Threads, LinkedIn and YouTube.
Where can people find your blog and your podcast? I want people to be able to get connected with you after this.
Anna: Thank you so much for that lovely conversation. And it was so fun finally being a guest on your show.
Jennifer: I’m so happy. Anna, I am so happy to have shared the Researchers’ Writing Academy with people because I really believe in your program. I believe in the process. And I know that you’re someone who goes in and updates things and improves them. And so I’ve always recommended the Researchers’ Writing Academy to professors. And I really encourage you if you’re listening to this to check it out.
Jennifer receives no monies or gift when you sign up for the Researchers’ Writing Academy or any of the other recommendations she shares on The Social Academic.
Dr Anna Clemens is an academic writing coach who specializes in scientific research papers. She runs the Researchers’ Writing Academy, an online course where she helps researchers to get published in high-ranking journals without lacking structure in the writing process.
Sign up for Anna’s free training on how to develop a structured writing process to get published in top-tier journals efficiently.
Early in our careers, when we were fresh-faced and idealistic (we still are!) the prepackaged curriculum and the advice of more experienced colleagues was the go-to resource. Largely, we were advised that teaching writing was a simple matter of having students walk through and complete organizers, spending about one day for each “stage” of the writing process. At the end of the writing unit, students had finished their compositions–the standardized, boring, recreated ideas that we taught them to write.
As we matured and grew as teachers of writing, we learned that teaching writing in such simplistic ways may be easier, but it was not actually teaching students to be writers. We learned with time and experience that writing instruction is a complex task within a complex system.
Complex systems and wicked problems
Complexity as it is applied to composition instruction recognizes that there is more than just a linear relationship between the student, the teacher, and the composition. It juggles the experiences of individual composers, characteristics of genre, availability of resources, assignment and individual goals, and constraints of composing environments. As with other complex systems and processes, it is non-linear, self-organizing, and unpredictable (Waltuck, 2012).
Complex systems are wicked in their complexity; therefore, wicked problems cannot be solved by simple solutions. Wicked problems are emergent and generative; they are nonlinear as they do not follow a straight path or necessarily have a clear cause-and-effect relationship. They are self-organizing, evolving and changing over time through the interactions of various elements. They are unpredictable and therefore difficult to anticipate how they will unfold or what the consequences of any intervention might be. Finally, they are often interconnected, as they are symptoms of other problems. In essence, a wicked problem is a complex issue embedded in a dynamic system (Rittel & Webber, 1973).
Writing formulas are wicked
As formulaic writing has become and remains prevalent in instruction and classroom writing activity, graphic organizers and structural guides, which were introduced as a tool to support acts of writing, have become a wicked problem of formula; the resource facilitating process has become the focus of product. High-stakes standardized assessment has led to a focus on compliance, production, and quality control, which has encouraged the use of formulas to simplify and standardize writing instruction, the student writing produced, and the process of evaluation of student work. Standardization may improve test scores in certain situations, but does not necessarily improve learning. Teachers resort to short, formulaic writing to help students get through material more quickly as well as data and assessment compliance. This serves to not only create product-oriented instruction, but a false dichotomy between process and product, ignoring the complex thinking and design that goes into writing.
As a result of such a narrow view of and limited focus on writing process and purpose, formulas have been shown to constrain thinking and limit creativity by prioritizing product over the composing process. The five-paragraph essay, specifically, is a structure that hinders authentic composing because it doesn’t allow for the “associative leaps” between ideas that come about in less constrained writing. Formulas undermine student agency by limiting writers’ abilities to express their unique voices because of over-reliance on rigid structures (Campbell, 2014; Lannin & Fox, 2010; Rico, 1988).
An objective process lens: A wicked solution
The use of writing formulas grew from a well-intentioned desire to improve student writing, but ultimately creates a system that is out of balance, lacking the flexibility to respond to a system that is constantly evolving. To address this, we advocate for shifting away from rigid formulas and towards a design framework that emphasizes the individual needs and strategies of student composers, which allows for a more differentiated approach to teaching acts of writing.
The proposed framework is an objective process lens that is informed by design principles. It focuses on the needs and strategies that drive the composing process (Sharples, 1999). This approach includes two types of needs and two types of strategies:
Formal needs: The assigned task itself
Informal needs: How a composer wishes to execute the task
“What” strategies: The concrete resources and available tools
“How” strategies: The ability to use the tools
An objective process lens acknowledges that composing is influenced by the unique experiences composers bring to the task. It allows teachers to view the funds of knowledge composers bring to a task and create entry points for support.
The objective process lens encourages teachers to ask key questions when designing instruction:
Do students have a clear idea of how to execute the formal need?
Do they have access to the tools necessary to be successful?
What instruction and/or supports do they need to make shifts in ideas when strategies are not available?
What instruction in strategies is necessary to help students communicate their desired message effectively?
Now how do we do that?
Working within a design framework that balances needs and strategies starts with understanding the type of composers you are working with. Composers bring different needs and strategies to each new composing task, and it is important for instructors to be aware of those differences. While individual composers are, of course, individuals with individual proclivities and approaches, we propose that there are (at least) four common types of student composers who bring certain combinations of strategies and needs to the composition process: the experience-limited, the irresolute, the flexible, and the perfectionist composers. By recognizing these common composer types, composition instructors can develop a flexible design for their instruction.
An experience-limited composer lacks experience in applying both needs and strategies to a composition, so they are entirely reliant on the formal needs of the assigned task and any what-strategies that are assigned by the instructor. These students gravitate towards formulaic writing because of their lack of experience with other types of writing. Relatedly, an irresolute composer may have a better understanding of the formal and informal needs, but they struggle with the application of what and how strategies for the composition. They can become overwhelmed with options of what without a clear how and become stalled during the composing process. Where the irresolute composer becomes stalled, the flexible composer is more comfortable adapting their composition. This type of composer has a solid grasp on both the formal and informal needs and is willing to adapt the informal needs as necessary to meet the formal needs of the task. As with the flexible composer, the perfectionist composer is also needs-driven, with clear expectations for the formal task and their own goals for the informal tasks. Rather than adjusting the informal needs as the composition develops, a perfectionist composer will focus intensely on ensuring that their final product exactly meets their formal and informal needs.
Teaching writing requires embracing its complexity and moving beyond formulaic approaches prioritizing product over process. Writing is a dynamic and individualized task that takes place within a complex system, where composers bring diverse needs, strategies, and experiences. By adopting a design framework, teachers of writing and composing can support students in navigating this complexity, fostering creativity, agency, and authentic expression. It is an approach that values funds of knowledge students bring to the writing process, recognizing the interplay of formal and informal needs, as well as their “what” and “how” strategies; those they have and those that need growth via instruction and experience. Through thoughtful design, we can grow flexible, reflective, and skilled communicators who are prepared to navigate the wicked challenges of composing in all its various forms.
These ideas and more can be found in When Teaching Writing Gets Tough: Challenges and Possibilities in Secondary Writing Instruction.
References
Campbell, K. H. (2014). Beyond the five-paragraph essay. Educational Leadership, 71(7), 60-65.
Lannin, A. A., & Fox, R. F. (2010). Chained and confused: Teacher perceptions of formulaic writing. Writing & Pedagogy, 2(1), 39-64.
Rico, G. L. (1988). Against formulaic writing. The English Journal, 77(6), 57-58.
Rittel, H. W. J., & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155–169.
Sharples, M. (1999). How we write : writing as creative design (1st ed.). Routledge. https://doi.org/10.4324/9780203019900
Waltuck, B. A. (2012). Characteristics of complex systems. The Journal for Quality & Participation, 34(4), 13–15.
Brett Stamm, University of Southern Mississippi & Tiffany Larson, University of Central Oklahoma
Brett Stamm is a husband, dad, educator, and persistent learner with 20 years of K-8 public school teaching and administrative experiences. He currently serves as Assistant Professor of Elementary Education at the University of Southern Mississippi and is an AERA Study of Deeper Learning Fellow.
Tiffany Larson is a teacher educator who is passionate about literacy education.She worked in secondary schools for 12 years as an English teacher and campus administrator before moving to higher education.She is currently an Assistant Professor at the University of Central Oklahoma where she is the program coordinator for English Education.
Latest posts by eSchool Media Contributors (see all)
Scenario 1: You’re part of a cross-disciplinary group of faculty members working on the new general education requirement. By the end of the semester, your group has to produce a report for your institution’s administration. As you start to generate content, one member’s primary contributions focus on editing for style and mechanics, while the other members are focused on coming to an agreement on the content and recommendations.
Scenario 2: When you’re at the stage of drafting content for a grant, one member of a writing team uses strikethrough to delete a large chunk of text, with no annotation or explanation for the decision. The writing stops as individual participants angrily back channel.
Scenario 3: A team of colleagues decides to draft a vision statement for their unit on campus. They come to the process assuming that everyone has a shared idea about the vision and mission of their department. But when they each contribute a section to the draft, it becomes clear that they are not, in fact, on the same page about how they imagine the future of their unit’s work.
In the best case scenarios, we choose people to write with. People whom we trust, who we know will pull their weight and might even be fun to work with. However, many situations are thrust upon us rather than carefully selected. We have to complete a report, write an important email, articulate a new policy, compose and submit a grant proposal, author a shared memo, etc., with a bunch of folks we would likely not have chosen on our own.
Further, teams of employees tasked with writing are rarely selected because of their ability to write well with others, and many don’t have the language to talk through their preferred composing practices. Across professional writing and within higher education, the inability to work collaboratively on a writing product is the cause of endless strife and inefficiency. How can we learn how to collaborate with people we don’t choose to write with?
Instead of just jumping into the writing task, we argue for a quick conversation about writing before any team authorship even starts. If time is limited, this conversation doesn’t necessarily need to be more than 15 minutes (though devoting 30 minutes might be more effective) depending on the size of the writing team, but it will save you time—and, likely, frustration—in the long run.
Drawing from knowledge in our discipline—writing studies—we offer the following strategies for a guided conversation before starting any joint writing project. The quick convo should serve to surface assumptions about each member’s beliefs about writing, articulate the project’s goal and genre, align expectations, and plan the logistics.
Shouldn’t We Just Use AI for This Kind of Writing?
Because writing is thinking. Certainly, the final writing product matters—a lot—but the reason getting to the product can be so hard is that writing requires critical thinking around project alignment. Asking AI to do the writing skips the hard planning, thinking and drafting work that will make the action/project/product that the writing addresses more successful.
Further, we do more than just complete a product/document when we write (either alone or together)—we surface shared assumptions, we come together through conversation and we build relationships. A final written product that has a real audience and purpose can be a powerful way to build community, and not just in the sense that it might make writers feel good. An engaged community is important, not just for faculty and staff happiness, but for productivity, for effective project completion and for long-term institutional stability.
Set the Relational Vibe
To get the conversation started, talk to each other: Do real introductions in which participants talk about how they write and what works for them. Talk to yourself: Do a personal gut check, acknowledging any feelings/biases about group members, and commit to being aware of how these personal relationships/feelings might influence how you perceive and accept their contributions. Ideas about authorship, ownership and credit, including emotional investments in one’s own words, are all factors in how people approach writing with others.
Articulate the Project Purpose and Genre
Get on the same page about what the writing should do (purpose) and what form it should take (genre). Often the initial purpose of a writing project is that you’ve been assigned to a task—students may find it funny that so much faculty and staff writing at the university is essentially homework! Just like our students, we have to go beyond the bare minimum of meeting a requirement to find out why that writing product matters, what it responds to and what we want it to accomplish. To help the group come to agreement about form and writing conventions, find some effective examples of the type of project you’re trying to write and talk through what you like about each one.
Align Your Approach
Work to establish a sense of shared authorship—a “we” approach to the work. This is not easy, but it’s important to the success of the product and for the sake of your sanity. Confront style differences and try to come to agreement about not making changes to each other’s writing that don’t necessarily improve the content. There’s always that one person who wants to add “nevertheless” for every transition or write “next” instead of “then”—make peace with not being too picky. Or, agree to let AI come in at the end and talk about the proofreading recommendations from the nonperson writer.
This raises another question: With people increasingly integrating ChatGPT and its ilk into their processes (and Word/Google documents offering AI-assisted authorship tools), how comfortable is each member of the writing team with integrating AI-generated text into a final product?
Where will collaboration occur? In person, online? Synchronously or asynchronously? In a Google doc, on Zoom, in the office, in a coffee shop? Technologies and timing both influence process, and writers might have different ideas about how and when to write (ideas that might vary based on the tools that your team is going to use).
When will collaboration occur? Set deadlines and agree to stick with them. Be transparent about expectations from and for each member.
How will collaboration occur? In smaller groups/pairs, all together, or completely individually? How will issues be discussed and resolved?
Finally, Some Recommendations on What Not to Do
Don’t:
Just divvy up the jobs and call it a day. This will often result in a disconnected, confusing and lower-quality final product.
Take on everything because you’re the only one who can do it. This is almost never true and is a missed opportunity to build capacity among colleagues. Developing new skills is an investment.
Overextend yourself and then resent your colleagues. This is a surefire path to burnout.
Sit back and let other folks take over. Don’t be that person.
Over the last two years, I’ve witnessed the rise in students’ use of generative AI as whole. Not surprisingly, more students are using generative AI to assist them in writing.
In an undergraduate business communication course that I oversee, the percentage of students who declared their use of generative AI for a writing assessment (i.e. business proposal) increased steadily over four semesters from 35% in 2023 to 61% in 2025. What’s more fascinating is the corresponding increase in the reported use of generative AI for their spoken assessment – their presentation (i.e. pitch) – from only 18% in 2023 to 43% in 2025.
*Note that there were about 350 students per semester and a total of about 1,400 students over four semesters/two years.
You may be wondering, how exactly are these students using generative AI for presentations?
They reported using generative AI to:
Create and edit visuals (e.g. images, prototypes/ mockups, logos)
Get inspiration for rhetorical devices (e.g. taglines, stories, alliterations)
Prepare for the Q&A (e.g. generate questions, review/structure answers)
Beyond verbal language,visuals are an important facet of communication and students need to be prepared for more multimodal communication tasks at the workplace (Brumberger, 2005). With digital media, there has been a shift in balance between words and images (Bolter, 2003) which can be seen in websites, reports and even manuals. A students’ ability to communicate in writing and speaking must now be complemented with a proficiency in visual language. Now, generative AI can reduce those barriers to creative visual expression (Ali et al., 2004).
For example, students on my business communication course use AI tools to create prototypes and mockups of their project ideas to complement their explanations. When they are unable to generate exactly what they need, they edit those images with traditional editing software or more recently, software with generative AI editing abilities such as Adobe firefly which allows users to select specific areas of an image and use “generative fill” to brainstorm and edit it without advanced technical skills. This and other AI text generators including Dall-e (Openart) and Midjourney have opened up possibilities for communicators to enhance their message using visuals.
Here are the AI Visual Tools students have reported using in their written and spoken assessment over two years:
What’s interesting from the list is not only an increase in the number of AI tools used but the type of tools used (1) for specific purposes like Logopony, for the creation of logos, Usegalileo, for app Interface designs, and Slidesgo, for the creation of slides, as well as AI tools (2) for editing such as Photoshop AI, Adobe Firefly, and Canva. Beyond that, we can see how students are using different tools from companies that are constantly evolving such as Canva with Magic Studio and Dream Lab, OpenAI who has integrated Dall-e into ChatGPT, as well as their latest offering, Sora, and even Google who now has Gemini Flash 2.0. Generative AI is also becoming more accessible on different platforms with the integration of Meta AI to WhatsApp, a cross-platform messaging app.
Ultimately, this list provides a glimpse of what some undergraduate business students are dabbling with and educators should consider trying them out. More importantly, we could guide students in thinking about the visuals and graphics that they ultimately use because not all graphics are equally effective (Mayer and Moreno 2003).
Some graphics are:
Decorative They are neutral and may enhance the aesthetics but is not interesting or directly relevant.
Seductive They may be highly interesting but may not be directly relevant and can distract the audience and cause their cognitive processing to focus on irrelevant material.
Instructive They are directly relevant to the topic (Sung and Maye, 2012).
However, it doesn’t mean that all visuals should be instructive because it depends on the goal of the communicator. For example, if the main goal is for enjoyment, then decorative visuals can enhance the aesthetics and seductive visuals can be so interesting that it leads to higher satisfaction, so we should remind students to be intentional in their use of visuals and AI tools. For example, AI tools tend to create visuals with a lot of extraneous details that may be distracting and lead to cognitive overload (Deleeuw and Mayer 2008) so students should refine their prompts by being more specific and precise (Hwang and Wu 2024) and they should be prepared to use editing software which can include other AI enhanced software like Adobe firefly and Imagen to achieve their final goal.
There are limitations to what AI can do at the moment.
It cannot be truly innovative because it learns from existing data.
It cannot fully understand subtle aspects like culture, values or emotional nuances (Hwang and Wu 2024).
But it can provide the stepping stone for students to visualize their ideas.
Let’s encourage our students to be aware of what they want to achieve when using AI tools and be proactive in selecting, rearranging, editing and refining the visuals to suit their purposes.
Aileen Wanli Lam is a Senior Lecturer and technology enthusiast at the National University of Singapore. She is fascinated by education technology and enjoys conversations about the latest industry developments. She is also passionate about professional communications, student engagement and educational leadership.
References
Ali, Safinah, Prerna Ravi, Randi Williams, Daniella DiPaola, and Cynthia Breazeal. “Constructing dreams using generative AI.” In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 21, pp. 23268-23275. 2024.
Bolter, Jay David. “Critical theory and the challenge of new media.” (2003).
Brumberger, Eva R. “Visual rhetoric in the curriculum: Pedagogy for a multimodal workplace.” Business Communication Quarterly 68, no. 3 (2005): 318-333.
DeLeeuw, Krista E., and Richard E. Mayer. “A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load.” Journal of educational psychology 100, no. 1 (2008): 223.
Hwang, Younjung, and Yi Wu. “Methodology for Visual Communication Design Based on Generative AI.” International journal of advanced smart convergence 13, no. 3 (2024): 170-175.
Mayer, Richard E., and Roxana Moreno. “Nine ways to reduce cognitive load in multimedia learning.” Educational psychologist 38, no. 1 (2003): 43-52.
Sung, Eunmo, and Richard E. Mayer. “When graphics improve liking but not learning from online lessons.” Computers in human behavior 28, no. 5 (2012): 1618-1625.
I vividly recall when an editor in chief invited me to publish in a well-known journal. Fresh from defending my dissertation, I still grappled with understanding how publishing worked in academia—like whether I should try to imitate the densely written, abstract sentences that appeared in the journal he edited. I thumbed the latest issue and looked at him. “Do you have a house style I should use?”
He shuddered and gave a response I’ve since heard echoed by other editors in chief of similarly well-respected journals: “Please don’t! We publish manuscripts despite how they’re written.”
But this candid advice leaves most graduate students and even seasoned faculty members with another dilemma. If you can’t imitate articles published in the best journals, how do you write up your research so it gets published?
During my early years of teaching writing courses, I discovered that students seldom revised their work significantly, even when they received extensive feedback from both me and their peers. In fact, students failed to revise even when they received feedback and grades from their peers.
All writing students also struggle with the idea that both feedback and grades on their writing are subjective, a reflection of how a particular instructor prefers students to write in a specific course. In addition, English literature and creative writing courses teach students that writing is a combination of mystery and art.
In contrast, researchers in cognitive neuroscience and psycholinguistics identified the features that make sentences easy or difficult to read decades ago. As a result, we can teach students how to make their sentences clear—no matter how complex the subject—by teaching science-based writing methods. And as a graduate student or faculty member, you can improve your own academic writing—and your chances for publication—by focusing on the five basic principles that cause readers to perceive sentences as clear.
Second, English sentence structure reflects causes and effects in its ordering of words: subject-verb-object. As researchers discovered, participants read sentences with active voice at speeds one-third faster than they read sentences in passive voice. Moreover, these same participants misunderstood even simple sentences in passive voice about one-quarter of the time. While many writing instructors require students to use active voice, few alert students to the specific benefits of active sentences that make them easier to read. These sentences are shorter, more efficient and more concrete, while sharpening readers’ sense of cause and effect.
Consider the differences between the first example below, which relies on passive voice, and the second, which uses active voice.
Passive: It has been reported that satiety may be induced by the distention of the gastric antrum due to the release of dissolved gas from carbonated water,which may improve gastric motility, thereby reducing hunger.
Active: Cuomo, Savarese, Sarnelli et al. reported that drinking carbonated water distends the gastric antrum through the release of dissolved gas, inducing satiety and improving gastric motility, all of which reduce hunger.
Actors or concrete objects turn sentences into microstories.
Academic writing naturally tackles complex content that can prove challenging even to subject matter experts. However, writers can make even challenging content comprehensible to nonexperts by making cause and effect clear in their sentences by using nouns that readers can easily identify as subjects. When the grammatical subjects in sentences are nouns clearly capable of performing actions, readers process sentences with greater speed and less effort. For actors, use people, organizations or publications—any individual, group or item created with intention that generates impact.
We unconsciously perceive these sentences as easier to read and recall because identifying actors and actions in sentences aids readers in fixing both a word’s meaning and the role it plays in sentence structure. Furthermore, these nouns enhance the efficiency of any sentence by paring down its words. Take these examples below:
Abstract noun as subject: Virginia Woolf’s examination of the social and economic obstacles female writers faced, due to the presumption that women had no place in literary professions and so were instead relegated to the household, particularly resonated with her audience of young women who had struggled to fight for their right to study at their colleges, even after the political successes of the suffragettes.
Actor as subject: In A Room of One’s Own, Virginia Woolf examined social and economic obstacles female writers faced. Despite the political success of the suffragettes, writers like Woolf battled the perception that women had no place in the literary professions. Thus Woolf’s book resonated with her audience, young women who had to fight for the right to study at their colleges.
Pronouns send readers backward, but readers make sense of sentences by anticipating what comes next.
If writers imitate the academic writing they see in print, they typically rely on pronouns as the subjects of sentences, especially “this,” “that,” “these,” “those” and “it.” However, pronouns save writers time but cost readers significantly, for two reasons.
First, readers typically assume that pronouns refer to a single noun rather than a cluster of nouns, a phrase or even an entire sentence. Second, when writers use these pronouns without nouns to anchor their meaning, readers slow down and frequently misidentify the meanings of pronouns. Moreover, readers rated writing samples with higher numbers of pronouns as less well-written than sentences that relied on actors as subjects—or even pronouns like “this” anchored by nouns like “outcome.”
Pronoun as subject: Due to the potential confounding detrimental effects of sulfonylureas and insulin in the comparator arms of the trials evaluating anticancer effects of metformin/thiazolidinediones, it is difficult to draw any firm conclusions from prior studies.
Actor as subject: In trials to assess the anticancer effects of metformin/thiazolidinediones, we had difficulty drawing any firm conclusions from prior studies due to potential confounding detrimental effects from sulfonylureas and insulin.
Action verbs make sentences more concrete, efficient and memorable.
Open any newspaper or magazine and, even in just-the-facts-ma’am hard news stories, you’ll find action verbs, like “argues,” “reinvents,” “writes” and “remakes.” In contrast, most writers overrely on nonaction verbs. These verbs include “is,” “has been,” “seems,” “appears,” “becomes,” “represents” and that evergreen staple of academic writing, “tends.”
Action verbs enable readers to immediately identify verbs, a process central to comprehending sentence structure and understanding meaning alike. Furthermore, action verbs make sentences more efficient, more concrete and more memorable. In one study of verbs and memory, readers recalled concrete verbs more accurately than nonaction verbs.
When we read action verbs, our brains recruit the sensory-motor system, generating faster reaction times than with abstract or nonaction verbs, which are processed outside that system. Even in patients with dementia, action verbs remain among words patients with advanced disease can identify due to the semantic richness of connections action verbs recruit in the brain.
Nonaction verbs: Claiming the promotion of research “excellence” and priding oneself in the record of “excellence” has become commonplace, but what this excellence is concretely about is unclear.
Action verbs: Research institutions claim to promote faculty on the basis of research “excellence,” but institutions define “excellence” in many ways, with few clear definitions.
Place subjects and verbs close together.
When we read, we understand sentences’ meaning based on our predictions of how sentences unfold. We unconsciously make these predictions from our encounters with thousands of sentences. Most important, these predictions rely on our ability to identify grammatical subjects and verbs.
We make these predictions easily when writers place subjects and verbs close together. In contrast, we struggle when writers separate subjects and verbs. With each increase in distance between subjects and verbs, readers exert greater effort, while reading speeds slow down. More strikingly, readers also make more errors in identifying subjects and verbs with increases in the number of words between subjects and verbs—even in relatively short sentences.
For example, in this sentence, readers must stumble through two adjective clauses, noted in orange below, before encountering the verb “decreases,” paired with the underlined subject, “rule”:
Specifically, a rule that indicates a reduction in delaythat precedes an aversive consequence decreases procrastination in university students.
But this separation strains working memory, as readers rely on subject-verb-object order to identify sentence structure. Ironically, as academic writers gain sophistication in their subject-matter expertise, they frustrate readers’ mechanisms for comprehension. Your urge to immediately modify the subject of your sentence with phrases and clauses slows reading and increases readers’ sense of conscious effort.
On the other hand, reading speeds increase while effort decreases when subjects and verbs appear close together. Introduce your main point with a subject and verb, then modify them with clauses or phrases:
Specifically, university students decrease procrastination when they face aversive consequences immediately for failure to meet deadlines.
These principles will work in any discipline, enabling writers to control how editors and peer reviewers respond to their manuscripts and proposals. These changes can help make an academic career successful, crucial in today’s competitive environment.
Yellowlees Douglas is a former professor of English at Holy Names University and was a director of five writing programs at universities including the City University of New York and the University of Florida. She is the author, most recently, of Writing for the Reader’s Brain: A Science-Based Guide (Cambridge University Press, 2024).
AI is here, and it is here to stay, which means that academia needs to incorporate it so that students learn about AI’s capability and are ready to use it properly. The most complained about issue in writing classes today is that students simply use AI to write their essays for them and, in the process, do not learn anything and use AI improperly. “The Anders 4 Phase AI Method of Writing Instruction,” is able to overcome these issues. This instructional method develops students’ writing skills while teaching AI literacy, which includes critical thinking. Different aspects of this method can also be applied to other courses/assignments. The Anders 4 Phase AI Method of Writing Instruction is a much-needed new way to develop writing in a way that better aligns with the new realities of how many people are already writing with AI.
Key Components (the four phases):
Foundational Writing Skills Development: instruction and assessment on key aspects of writing such as sentence structure, paragraph structure, transitional sentences, use of personal voice, researching, outlining, thesis statements, and any other needed writing components. Done through: multiple-choice, fill-in-the-blank, and short in-class writing.
Understanding of Different Essay Types: instruction and assessment on key aspects of different essay types done through multiple-choice, fill-in-the-blank, and short in-class writing
Prompt Engineering Development: instruction and assessment on prompt engineering using an advanced prompt formula, the ability to create effective prompts for AI to generate good essays that have proper formatting, student voice, and accurate information. Evaluated via multiple-choice, fill-in-the-blank tests, and in-class writing of prompts and additional drafting.
Use of AI for Writing with Full Personal Accountability: assessment on specific essay creation done via student submission of essays developed through the use and assistance of AI. Additional in-class exams on key contents and periodic student presentations on created essays (to help ensure student accountability of knowledge integration).
Key Benefits:
Develops students’ foundational knowledge of writing and ability to create multiple essay types
Eliminates issues with students inappropriately using AI to write essays without fully understanding writing components
Reduces instructors’ stress/anxiety in feeling the need to run AI detection tools (no longer needed)
Helps to directly develop students’ understanding of effective writing while simultaneously developing their critical thinking, AI literacy, and ethical AI use skills
A much more detailed description of this method is available through the Sovorel Center for Teaching & Learning YouTube educational Channel:
Following HEPI’s recent Policy Note on students’ use of artificial intelligence (AI), HEPI Director Nick Hillman reviews a new book from the United States on what AI means for writing.
The author John Warner’s persuasive argument is that generative AI creates syntax but doesn’t write because ‘writing is thinking.’ (I hope this is the only reason why, when asked to write a higher education policy speech ‘in the style of Nick Hillman’, ChatGPT’s answer is so banal and vacuous…) People are, Warner says, attracted to AI because they’ve not previously been ‘given the chance to explore and play within the world of writing.’
Although Warner is not as negative about using ChatGPT to retrieve information as he is on using it to write wholly new material, he sees the problems it presents as afflicting the experience of ‘deep reading’ too: ‘Reading and writing are being disrupted by people who do not seem to understand what it means to read and write.’
The book starts by reminding the reader how generative AI based on Large Language Models actually works. ChatGPT and the like operate as machines predicting the next word in a sentence (called a ‘token’). To me, it is reminiscent of Gromit placing the next piece of train track in front of him as he goes. It’s all a bit like a more sophisticated version of how the iPhone Notes app on which I’m typing this keeps suggesting the next word for me. (If you click on the suggestions, it tends to end up as nonsense though – I’ve just done it and got, ‘the app doesn’t even make a sentence in a single note’, which sounds like gibberish while also being factually untrue.)
‘The result’, we are told of students playing with ChatGPT and the like, ‘is a kind of academic cosplay where you’ve dressed up a product in the trappings of an academic output, but the underlying process is entirely divorced from the genuine article.’
Writing, Warner says, is a process in which ‘the idea may change based on our attempts to capture it.’ That is certainly my experience: there have been times when I’ve started to bash out a piece not quite knowing if it will end up as a short blog based on one scatty thought or flower into a more polished full-length HEPI paper. Academics accustomed to peer review and the slow (tortuous?) procedures of academic journals surely know better than most that writing is a process.
The most interesting and persuasive part of the book (and Warner’s specialist subject) is the bit on how formulae make writing mundane rather than creative. Many parents will recognise this. It seems to me that children are being put off English in particular by being forced to follow the sort of overweening instructions that no great author ever considered (‘write your essay like a burger’, ‘include four paragraphs in each answer’, ‘follow PEE in each paragraph’ [point / evidence / explain]). Warner sees AI taking this trend to its logical and absurd conclusion where machines are doing the writing and the assessment – and ruining both.
Because writing is a process, Warner rejects even the popular idea that generative AI may be especially useful in crafting a first draft. He accepts it can produce ‘grammatically and syntactically sound writing … ahead of what most students can produce.’ But he also argues that the first draft is the most important draft ‘as it establishes the intention behind the expression.’ Again, I have sympathy with this. Full-length HEPI publications tend to go through multiple drafts, while also being subjected to peer review by HEPI’s Advisory Board and Trustees, yet the final published version invariably still closely resembles the first draft because that remains the original snapshot of the author’s take on the issue at hand. Warner concludes that AI ‘dazzles on first impression but … has significantly less utility than it may seem at first blush.’
One of the most interesting chapters compares and contrasts the rollout of ChatGPT with the old debates about the rise of calculators in schools. While calculators might mean mental arithmetic skills decline, they are generally empowering; similarly, ChatGPT appears to remove the need to undertake routine tasks oneself. But Warner condemns such analogies: for calculators ‘the labor of the machine is identical to the labor of a human’, whereas ‘Fetching tokens based on weighted probabilities is not the same process as what happens when humans write.’
At all the many events I go to on AI in higher education, three areas always comes up: students’ AI use; what AI might mean for professional services; and how AI could change assessment and evaluation. The general outcome across all three issues is that no one knows for sure what AI will mean, but Warner is as big a sceptic on AI and grading as he is on so much else. Because it is formulaic and based on algorithms, Warner argues:
Generative AI being able to give that “good” feedback means that the feedback isn’t actually good. We should instead value that which is uniquely human. … Writing is meant to be read. Having something that cannot read generate responses to writing is wrong.
The argument that so many problems are coursing through education as a result of new tech reminds me a little of the argument common in the 1980s that lead pipes brought down the Roman Empire. Information is said to become corrupted by AI in the way that the water supposedly became infected by the lead channels. But the theory about lead pipes is no longer taken seriously and I remain uncertain whether Warner’s take will survive the passage of time in its entirety either.
Moreover, Warner’s criticisms of the real-world impact of ChatGPT are scattergun in their approach. They include the ‘literal army of precarious workers doing soul-killing tasks’ to support the new technology as well as the weighty environmental impact. This critique calls to mind middle-class drug-takers in the developed world enjoying their highs while dodging the real-world impact on developing countries of their habit.
In the end, Warner’s multifarious criticisms tot up to resemble an attack on technology that comes perhaps just a little too close for comfort to the attacks in the early 1980s by the Musicians’ Union’s on synthesisers and drum machines. In other words, the downsides may be exaggerated while the upsides might be downplayed.
Nonetheless, I was partially persuaded. The process of writing is exactly that: a process. Writing is not just mechanical. (The best young historian I taught in my first career as a school teacher, who is now an academic at UCL, had the worst handwriting imaginable as his brain moved faster than his hand / pen could manage.) So AI is unlikely to replace those who pen words for a living just yet.
Although, paradoxically, I also wished the author had run his text through an AI programme and asked it to knock out around 40% of his text. Perhaps current iterations of generative AI can’t write like a smart human or think like a smart human, but they might be able to edit like a smart human? Perhaps AI’s biggest contribution could come at the end of the writing process rather than the beginning? Technology speeds up all our lives, leaving less time for a leisurely read, and it seems to me that all those ‘one-idea’ books that the US floods the market with, including this one, could nearly always be significantly shorter without losing anything of substance.
