By: Bob Trower & Genna (AI assistant)
The recent chorus of voices expressing concern about the
encroachment of generative AI into writing studies is not without merit. There
are legitimate worries about authorship, authenticity, corporate influence, and
environmental cost. But these are not reasons to reject AI outright. Rather,
they invite a deeper, more nuanced conversation about how best to shape its
role within education and society.
This piece seeks to offer a reasoned response to critiques
such as those raised in a recent essay questioning AI's place in creative
writing and composition pedagogy. Our goal is not to confront, but to
engage—with respect, reflection, and a commitment to shared values: the
cultivation of imagination, ethical integrity, and human flourishing.
1. Creativity and the Role of Machines
A common objection is that writing is a uniquely human,
imaginative act, and that using AI to assist or generate text diminishes that
essence. But creativity has never been a solitary, pristine endeavor. Writers
have always borrowed, iterated, echoed, and transformed. As Kirschenbaum (2016)
shows, even the transition to word processors was met with suspicion. Yet, over
time, those tools became integral to modern literary production.
AI, particularly large language models, operates through
probabilistic recombination. It generates based on learned patterns—not so
different from how humans internalize genre, style, and structure. Chiang
(2023) describes AI output as a form of advanced compression rather than true
thought. Still, this "compression" can spark ideas, accelerate
iteration, and help students find their voice by offering models to push
against.
2. The Ethics of Data and Intellectual Property
There is rightful concern about how generative models are
trained, particularly regarding consent and copyright. These issues deserve
scrutiny. But it is inaccurate to claim that no ethical pathway exists.
Open-source models (e.g., Mistral, OpenChat) offer more transparency.
Organizations like LAION and Hugging Face are working on consent-aware
datasets.
Educators can and should teach students to interrogate the
provenance of AI output. Just as we teach source criticism in research, we can
teach model criticism in generative text. Students should learn when to
use AI, how to contextualize its limitations, and why
attribution, citation, and ethical reflection matter (European Commission,
2023).
3. Environmental Costs and Corporate Control
AI models are resource-intensive to train, but so is the
modern university. Flying to conferences, powering campuses, and maintaining
online systems also have carbon footprints. Importantly, model inference (i.e.,
using a trained model) is far less energy-intensive than training and is
improving rapidly (Stanford HAI, 2023).
We should be concerned about centralization. But abandoning
the field won't stop that trend—it will only cede influence to those less
ethically inclined. Teaching AI critically within the academy gives us a chance
to shape its trajectory (DAIR Institute, 2023).
4. Plagiarism and Pedagogical Integrity
Worries about AI-fueled plagiarism are valid, but not
unique. Students have long found ways to shortcut learning. The real solution
lies in pedagogy. When assignments ask students to synthesize, reflect, and
create with personal stakes, AI alone won’t suffice. And when AI is allowed
transparently, it becomes part of the learning process rather than a means to
avoid it (Fyfe, 2023).
As Mollick (2023) has shown, AI can help students start
rather than finish their thinking. It can be a brainstorming partner, a
language coach, a structural sounding board. But this requires explicit
instruction, not prohibition.
5. Creators Will Be Replaced—By People Using AI
The truth is that creators of all kinds—writers, designers,
developers—will not be replaced by AI. But they will be replaced
by people using AI. In competitive contexts, skill with these tools becomes a
multiplier.
Some rare individuals may continue to outperform both AI and
AI-assisted creators. But basing pedagogy on that narrow possibility means
disadvantaging the overwhelming majority. Our responsibility in education is
not to chase unicorns, but to equip everyone with tools for success.
Teaching AI literacy is not about surrendering to the machine—it’s about
leveling the playing field and preparing students for a future that is already
arriving (Mollick, 2023).
6. Equipping Students for the Future
To discourage students from learning how to work with AI is
to deny them fluency in a tool that will likely shape their future professional
landscape. Just as calculators did not destroy mathematics, AI will not destroy
writing. But it will change it.
Our role is not to block the future but to prepare students
for it. That means teaching them not just to use AI, but to critique it, shape
it, and engage with it ethically.
Conclusion: Holding the Line Without Digging Trenches
Rejecting AI entirely in writing studies may feel
principled, but it risks becoming reactionary. Instead, we can hold the line on
what matters—human imagination, ethical rigor, and intellectual honesty—while
adapting to new tools.
Higher education should not be the last bastion of
nostalgia. It should be the first place where new tools are interrogated,
refined, and repurposed for the good of all.
Let us not go gentle into that good night of uncritical
adoption or unthinking refusal. Let us think—together.
References
Chiang, T. (2023). Will A.I. Become the New McKinsey?
The New Yorker. https://www.newyorker.com/news/daily-comment/will-ai-become-the-new-mckinsey
DAIR Institute. (2023). Distributed Artificial
Intelligence Research Institute. https://dair-institute.org
European Commission. (2023). Ethical Guidelines on the
Use of Generative AI. https://digital-strategy.ec.europa.eu/en/library/report-generative-ai-ethics
Fyfe, P. (2023). How Not to Detect AI-Generated Text.
Inside Higher Ed. https://www.insidehighered.com/opinion/views/2023/03/21/how-not-detect-ai-generated-text-opinion
Kirschenbaum, M. (2016). Track Changes: A Literary
History of Word Processing. Harvard University Press. https://www.hup.harvard.edu/books/9780674417076
Mollick, E. (2023). One Useful Thing. https://www.oneusefulthing.org
Stanford HAI. (2023). AI Index Report 2023. https://aiindex.stanford.edu/report/
