Teaching Philosophy

I teach writing as a collaborative process, and I build every course around this single conviction: students learn to write by writing and must come to know writing as a process that includes a great many stumbles and side quests in addition to the parts that involve words emerging on a page or screen. Writing is drafting, rethinking, and revising, yes, but writing is also a 2 A.M. commiseration trip to the local diner with peers after a confusing workshop session, a sometimes-helpful discussion with a Large Language Model, a visit to the Writing Center, stopping by office hours for a pep talk, and an unexpected shower thought long-after the draft was set aside for the day. The conviction is also grounded in understanding who my students are. In the First-Year Writing Program at the University of Central Florida, I meet writers who arrive with wildly uneven preparation and one thing in common: they must pass this course to graduate. My job is to make the writing process visible enough that every one of them can achieve what they need, whether that is preparation for future college writing or an understanding of how writing transfers into genres of the workplace and daily life, and how the technologies of our time can complicate and enrich all spheres of our lives.

On the first day of class, I play a piece of AI-generated music and ask who is singing. Students name a familiar artist; I tell them the artist never recorded it, was never asked, and was never paid, their voice was synthesized without consent. The room gets briefly tense, with some ready to throw the laptops out the window. I then share another face of the same technology: the synthesized farewell that let audiences see Carrie Fisher as Princess Leia once more, after the actress had passed, and the voice that let Stephen Hawking keep speaking for decades. I remind them that this technology is, at least in part, also functioning in the automatic captions running at the bottom of our own lecture. From there we turn, deliberately, to the accommodations section of the syllabus. I hold up my own Letters of Accommodation from my years as a student and invite anyone who has carried a 504 plan, an IEP, or a diagnosis, or wonders whether they should have one of these, to speak with me privately, and I make clear that no documentation or letter is a prerequisite for that conversation. Only once we have established that the classroom is built for every body and mind in it do I open ChatGPT, paste in our first assignment prompt, and read the output against the rubric together. The output is usually a competent starting place and an unusable finished one. We name why: the large language model cannot re-see a draft, cannot be surprised by its own inventive turn of phrase, cannot do the thinking the assignment is actually asking for—and if the best we can produce is what the machine already produced, we have made ourselves unemployable. We then share more about our writing experiences thus far in life, and what makes our voice our own – the lives we carry with us.

“This is not a detour from rhetoric; it is rhetoric, extended to the computational texts my students already live inside.”

That first day also captures how I understand the digital in my teaching. I came to the digital humanities through composition, and I have found the two fields asking the same question from different directions: who holds power over a text, and how is it made? The framework that has most shaped my recent teaching is Catherine d'Ignazio and Lauren Klein's Data Feminism, which insists that data and the tools that process it are never neutral, they carry the biases of the people and institutions that build them. I teach my students to read a large language model the way d'Ignazio and Klein read a dataset: to ask what it was trained on, whose language it treats as standard, whom it renders invisible, and who profits? This is not a detour from rhetoric; it is rhetoric, extended to the computational texts my students already live inside. The University of Central Florida's First-Year Composition outcomes already ask students to compose multimodally and to build information-literacy skills. Critical attention to AI and data is simply what those outcomes look like when enacted in present times.

A digital-humanities orientation does not pull me away from the core work of teaching composition; it makes me a more versatile colleague inside it. I can teach rhetoricians from Aristotle to Burke and Bitzer to Aja Martinez and April Baker-Bell in the traditional seminar and also meet students where their reading and writing now actually happen: in feeds, in generated text, and in interfaces that make rhetorical choices for them before they type a word. The value of a digital credential is not that it replaces disciplinary depth but that it travels—across courses, across program service, across the questions our students bring us that a syllabus written five years ago did not anticipate. I offer the positionality of both a composition studies scholar and a colleague fluent in the tools its students cannot avoid. In my classroom, Generative AI is invited as a sort of revision partner: students are welcome to ask large language models to critique a draft and then write a short reflection on what its feedback got right, what it got wrong, and what it could not see—an exercise that sharpens their own editorial judgment far more than a clean machine-edit ever could. I draw a clearly communicated, firm boundary at the submission fully-generated output in lieu of the students' own writing and inputting another student's work into a large language model without their permission. I also require disclosure: any use of Generative AI must be attributed and its output verified, exactly as we attribute and verify any other source, because learning to cite a tool (and to check whether that tool is misleading you) is now part of information literacy rather than a workaround for cheating. These policies are pedagogical before they are punitive. They exist to protect the thinking, not to police the technology. The disclosure process I encourage, further, does not begin and end with discussing Generative AI use, but rather invites a holistic discussion of all technologies that are used in the writing process.

The bedrock of my work remains access, and the foundational work of the scholars of disability studies within composition and rhetoric studies, including Margaret Price, Jay Dolmage, and Remi Yergeau, and all those who've striven for “a culture of access,” within composition studies. As someone who is both a digital humanist and a rhetorician, my definition of “access,” includes both this commitment to disabled and neurodivergent students and students from differing socioeconomic backgrounds with differing opportunities to access technology and study it critically. Normalizing the intentional, disclosed use of all digital tools makes room for the students—myself once among them—who need technology to participate fully, and it treats that need as ordinary rather than exceptional. Each term, my students write their way toward skills that will carry them into workplaces and communities I cannot predict. Whatever they major in, and whether they ever take another writing class, they leave more critical about the process of putting thought to page or screen, and of how they respond and interpret the content of the screens and pages around them.