College Composition & Communication - Volume 77, Issue 1, 2025

In a relatively short time, market and political forces have intensified the reach of artificial intelligence (AI). AI has become, in a word, climatic—not only a discrete technological system but also a creeping assemblage of ideological, material, and political forces. This article tracks these forces by developing rhetorical climates of AI as a conceptual framework. In doing so, I aim to (1) link the harms of climate change with the rapid buildout of AI infrastructure and (2) shift the frame of the conversation by emphasizing the extractive, exploitative, enclosed, and knotted supremacist conditions that have been prerequisites for building AI systems at scale. While these pervading rhetorical climates may seem unchangeable, I track how microclimates of resistance have developed, in the past and in the present. In particular, I emphasize the importance of bodily intelligence in navigating asymmetrical conditions of power felt in the AI industry. The article concludes by discussing how rhetoric and writing studies can weather the unfolding rhetorical climates of AI by diagnosing conditions, seizing moments, and plotting futures to imagine a less extractive and less harmful world.

I argue that composition studies must build critical awareness about how humans from the Global South train AI with their writing embodiments. To draw our attention to how those working in the Global South train AI in harmful conditions, even though AI companies use algorithms and terms of service to smooth away these embodiments, I adapt the concept of humans-in-the-loop. Critical awareness of humans-in-the-loop moves scholarship in writing studies from a focus on AI-human collaboration that begins after an AI produces a text to one that requires us to see how AI products are always already human authored. Through a case study of Google Translate, I demonstrate how a critical awareness of how AI can erase the writing embodiment of humans-in-the-loop affords me opportunities to ask generative questions: How does language translation play a role in the erasure of embodied writing? Why does AI produce with bias toward marginalized populations when marginalized populations are those that moderate AI? Overall, I ask compositionists to see AI products as already human authored so that writing studies can consider the invisible labor of humans-in-the-loop as the field moves forward in researching AI.

From an unsettled, ambivalent middle between discourses of generative AI integration and refusal, we offer a critical-ethical stance for AI-engaged writing assignments. We apply a critical thinking framework to these assignments, assert critical AI literacy as a kind of critical thinking, and discuss how critical thinking and critical AI literacy can facilitate ethical discernment about generative AI use. This unsettled, critical-ethical stance positions scholars in our field to support context-sensitive pedagogical responses to generative AI across first-year writing, Writing Across the Curriculum, writing centers, and beyond.

In gaming, cheat codes change how players engage a system by inviting exploration and reducing the fear of failure. Drawing on writing center pedagogy, this article proposes a similar framework for navigating generative AI in writing instruction and positions play as a method for developing critical AI literacy. Writing centers have long served as spaces where students engage collaboratively with new technologies and construct meaning through dialogue. This article extends that tradition by positioning writing center pedagogy as a framework for helping students examine AI’s ethical implications through treating it as a rhetorical situation to be unpacked, which demands principled, human-centered engagement rooted in values such as collaborative exploration. By weaving together writing center praxis and game-informed pedagogy, this article contributes to ongoing conversations in writing studies about how to integrate AI in ways that support critical thinking and ethical reflection. It demonstrates how playful, classroom-tested activities can animate discussions of bias and representation while helping students build rhetorical discernment through experience. Ultimately, the article argues that ethical literacy must be practiced through relational, iterative work. As writing classrooms become one of the few remaining spaces where students encounter generative AI with support and critical context, writing instructors have a vital opportunity to help students learn to write with, against, and around powerful technologies.

In response to disruptions introduced to the job market by AI resume screeners, this article introduces a novel theoretical framework for the life cycle of artificial intelligence systems to help unblackbox resume screening AI systems. It then applies the AI life cycle framework to a digital case study of RChilli’s job-resume matching algorithm. The article introduces an eleven-step computational job-resume matching assignment that writing instructors can use in their classrooms to explore the pedagogical implications offered by the AI life cycle framework. The assignment helps students simulate important phases in AI production and development while highlighting biases and ethical concerns in AI screening of resumes. By exploring job-resume analytics, this study helps to teach critical AI and data literacy, make job-resume matching algorithms more explainable, and transform how professional writing can be taught in the age of automated hiring.

The integration of generative artificial intelligence (AI) into academic writing has raised questions about the syntactic complexity of AI-generated texts compared to human-authored essays. While studies have explored syntactic complexity in human writing, limited research has compared AI-generated argumentative and narrative texts, particularly in isolating cognitive overload and proficiency factors. This study addressed this gap by examining genre-specific syntactic patterns in AI-generated essays. Using the L2 Syntactic Complexity Analyzer, the study analyzed four hundred AI-generated essays (two hundred argumentative and two hundred narrative) and employed paired T-tests and Pearson correlation coefficients to identify differences and relationships among syntactic measures. Results showed that argumentative essays demonstrated higher syntactic complexity than narrative essays, especially in production unit length, coordination, and phrasal sophistication, while subordination measures remained similar. Correlation analysis revealed that argumentative essays compartmentalized ideas through coordinated and nominally complex structures, while narrative essays integrated descriptive richness through longer sentences and embedded clauses. The findings suggest that genre-specific rhetorical demands shape syntactic complexity in AI-generated writing. Implications for teaching and learning writing and future studies are discussed.

Over the past year, Antonio Byrd, Ira Allen, Sherry Rankins-Robertson, and John Gallagher developed researched recommendations for a Generative AI policy for CCC. From these recommendations, the CCC editorial team wrote an official policy, which is available on our website at https://cccc.ncte.org/cccc/ccc-generative-ai-policy/. We, the editorial team, are grateful for the thoughtful, generous work of these scholars on this project, which is the foundation of the following symposium.

This Research Brief discusses transformers—the core engine for most artificial intelligence applications. The brief situates transformer technology within the field of rhetoric and composition by surveying recent studies; highlights the innovative aspects of transformers; and, finally, thinks through (Majdik and Graham) the operations of transformers and generative AI through Miller’s theory of topoi, illustrating one way in which rhetoric and composition scholars and teachers can critically engage with generative AI in instruction and research.